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1. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Brauer, M, Roth, GA, Aravkin, AY, Zheng, P, Abate, KH, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasi, MA, Abbasian, M, Abbasifard, M, Abbasi-Kangevari, M, Abd ElHafeez, S, Abd-Elsalam, S, Abdi, P, Abdollahi, M, Abdoun, M, Abdulah, DM, Abdullahi, A, Abebe, M, Abedi, A, Abegaz, TM, Abeldaño Zuñiga, RA, Abiodun, O, Abiso, TL, Aboagye, RG, Abolhassani, H, Abouzid, M, Aboye, GB, Abreu, LG, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abukhadijah, HJJ, Aburuz, S, Abu-Zaid, A, Adane, MM, Addo, IY, Addolorato, G, Adedoyin, RA, Adekanmbi, V, Aden, B, Adetunji, JB, Adeyeoluwa, TE, Adha, R, Adibi, A, Adnani, QES, Adzigbli, LA, Afolabi, AA, Afolabi, RF, Afshin, A, Afyouni, S, Afzal, MS, Afzal, S, Agampodi, SB, Agbozo, F, Aghamiri, S, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, N, Ahmad, S, Ahmad, T, Ahmed, A, Ahmed, LA, Ahmed, MB, Ahmed, S, Ahmed, SA, Ajami, M, Akalu, GT, Akara, EM, Akbarialiabad, H, Akhlaghi, S, Akinosoglou, K, Akinyemiju, T, Akkaif, MA, Akkala, S, Akombi-Inyang, B, Al Awaidy, S, Al Hasan, SM, Alahdab, F, AL-Ahdal, TMA, Alalalmeh, SO, Alalwan, TA, Al-Aly, Z, Alam, K, Alam, N, Alanezi, FM, Alanzi, TM, Albakri, A, AlBataineh, MT, Aldhaleei, WA, Aldridge, RW, Alemayohu, MA, Alemu, YM, Al-Fatly, B, Al-Gheethi, AAS, Al-Habbal, K, Alhabib, KF, Alhassan, RK, Ali, A, Ali, BA, Ali, I, Ali, L, Ali, MU, Ali, R, Ali, SSS, Ali, W, Alicandro, G, Alif, SM, Aljunid, SM, Alla, F, Al-Marwani, S, Al-Mekhlafi, HM, Almustanyir, S, Alomari, MA, Alonso, J, Alqahtani, JS, Alqutaibi, AY, Al-Raddadi, RM, Alrawashdeh, A, Al-Rifai, RH, Alrousan, SM, Al-Sabah, SK, Alshahrani, NZ, Altaany, Z, Altaf, A, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Alwafi, H, Al-Wardat, MS, Al-Worafi, YM, Aly, H, Aly, S, Alzoubi, KH, Al-Zyoud, W, Amaechi, UA, Aman Mohammadi, M, Amani, R, Amiri, S, Amirzade-Iranaq, MH, Ammirati, E, Amu, H, Amugsi, DA, Amusa, GA, Ancuceanu, R, Anderlini, D, Anderson, JA, Andrade, PP, Andrei, CL, Andrei, T, Anenberg, SC, Angappan, D, Angus, C, Anil, A, Anil, S, Anjum, A, Anoushiravani, A, Antonazzo, IC, Antony, CM, Antriyandarti, E, Anuoluwa, BS, Anvari, D, Anvari, S, Anwar, S, Anwar, SL, Anwer, R, Anyabolo, EE, Anyasodor, AE, Apostol, GLC, Arabloo, J, Arabzadeh Bahri, R, Arafat, M, Areda, D, Aregawi, BB, Aremu, A, Armocida, B, Arndt, MB, Ärnlöv, J, Arooj, M, Artamonov, AA, Artanti, KD, Aruleba, IT, Arumugam, A, Asbeutah, AM, Asgary, S, Asgedom, AA, Ashbaugh, C, Ashemo, MY, Ashraf, T, Askarinejad, A, Assmus, M, Astell-Burt, T, Athar, M, Athari, SS, Atorkey, P, Atreya, A, Aujayeb, A, Ausloos, M, Avila-Burgos, L, Awoke, AA, Ayala Quintanilla, BP, Ayatollahi, H, Ayestas Portugal, C, Ayuso-Mateos, JL, Azadnajafabad, S, Azevedo, RMS, Azhar, GS, Azizi, H, Azzam, AY, Backhaus, IL, Badar, M, Badiye, AD, Bagga, A, Baghdadi, S, Bagheri, N, Bagherieh, S, Bahrami Taghanaki, P, Bai, R, Baig, AA, Baker, JL, Bakkannavar, SM, Balasubramanian, M, Baltatu, OC, Bam, K, Bandyopadhyay, S, Banik, B, Banik, PC, Banke-Thomas, A, Bansal, H, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Bärnighausen, TW, Barone-Adesi, F, Barqawi, HJ, Barrero, LH, Barrow, A, Barteit, S, Basharat, Z, Basiru, A, Basso, JD, Bastan, M-M, Basu, S, Batchu, S, Batra, K, Batra, R, Baune, BT, Bayati, M, Bayileyegn, NS, Beaney, T, Behnoush, AH, Beiranvand, M, Béjot, Y, Bekele, A, Belgaumi, UI, Bell, AW, Bell, ML, Bello, MB, Bello, OO, Belo, L, Beloukas, A, Bendak, S, Bennett, DA, Bennitt, FB, Bensenor, IM, Benzian, H, Beran, A, Berezvai, Z, Bernabe, E, Bernstein, RS, Bettencourt, PJG, Bhagavathula, AS, Bhala, N, Bhandari, D, Bhardwaj, N, Bhardwaj, P, Bhaskar, S, Bhat, AN, Bhat, V, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhatti, R, Bhuiyan, MA, Bhutta, ZA, Bikbov, B, Bishai, JD, Bisignano, C, Biswas, A, Biswas, B, Biswas, RK, Bjørge, T, Boachie, MK, Boakye, H, Bockarie, MJ, Bodolica, V, Bodunrin, AO, Bogale, EK, Bolla, SR, Boloor, A, Bonakdar Hashemi, M, Boppana, SH, Bora Basara, B, Borhany, H, Botero Carvajal, A, Bouaoud, S, Boufous, S, Bourne, R, Boxe, C, Braithwaite, D, Brant, LC, Brar, A, Breitborde, NJK, Breitner, S, Brenner, H, Briko, AN, Britton, G, Brown, CS, Browne, AJ, Brunoni, AR, Bryazka, D, Bulamu, NB, Bulto, LN, Buonsenso, D, Burkart, K, Burns, RA, Busse, R, Bustanji, Y, Butt, NS, Butt, ZA, Caetano dos Santos, FL, Cagney, J, Cahuana-Hurtado, L, Calina, D, Cámera, LA, Campos, LA, Campos-Nonato, IR, Cao, C, Cao, F, Cao, Y, Capodici, A, Cárdenas, R, Carr, S, Carreras, G, Carrero, JJ, Carugno, A, Carvalho, F, Carvalho, M, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catalá-López, F, Catapano, AL, Cattaruzza, MS, Caye, A, Cederroth, CR, Cegolon, L, Cenderadewi, M, Cercy, KM, Cerin, E, Chadwick, J, Chakraborty, C, Chakraborty, PA, Chakraborty, S, Chan, JSK, Chan, RNC, Chandan, JS, Chandika, RM, Chaturvedi, P, Chen, A-T, Chen, CS, Chen, H, Chen, MX, Chen, M, Chen, S, Cheng, C-Y, Cheng, ETW, Cherbuin, N, Chi, G, Chichagi, F, Chimed-Ochir, O, Chimoriya, R, Ching, PR, Chirinos-Caceres, JL, Chitheer, A, Cho, WCS, Chong, B, Chopra, H, Chowdhury, R, Christopher, DJ, Chu, D-T, Chukwu, IS, Chung, E, Chung, S-C, Chutiyami, M, Cioffi, I, Cogen, RM, Cohen, AJ, Columbus, A, Conde, J, Corlateanu, A, Cortese, S, Cortesi, PA, Costa, VM, Costanzo, S, Criqui, MH, Cruz, JA, Cruz-Martins, N, Culbreth, GT, da Silva, AG, Dadras, O, Dai, X, Dai, Z, Daikwo, PU, Dalli, LL, Damiani, G, D'Amico, E, D'Anna, L, Darwesh, AM, Das, JK, Das, S, Dash, NR, Dashti, M, Dávila-Cervantes, CA, Davis Weaver, N, Davitoiu, DV, De la Hoz, FP, de la Torre-Luque, A, De Leo, D, Debopadhaya, S, Degenhardt, L, Del Bo', C, Delgado-Enciso, I, Delgado-Saborit, JM, Demoze, CK, Denova-Gutiérrez, E, Dervenis, N, Dervišević, E, Desai, HD, Desai, R, Devanbu, VGC, Dewan, SMR, Dhali, A, Dhama, K, Dhane, AS, Dhimal, ML, Dhimal, M, Dhingra, S, Dhulipala, VR, Dhungana, RR, Dias da Silva, D, Diaz, D, Diaz, LA, Diaz, MJ, Dima, A, Ding, DD, Dinu, M, Djalalinia, S, Do, TC, Do, THP, do Prado, CB, Dodangeh, M, Dohare, S, Dokova, KG, Dong, W, Dongarwar, D, D'Oria, M, Dorostkar, F, Dorsey, ER, Doshi, R, Doshmangir, L, Dowou, RK, Driscoll, TR, Dsouza, AC, Dsouza, HL, Dumith, SC, Duncan, BB, Duraes, AR, Duraisamy, S, Dushpanova, A, Dzianach, PA, Dziedzic, AM, Ebrahimi, A, Echieh, CP, Ed-Dra, A, Edinur, HA, Edvardsson, D, Edvardsson, K, Efendi, F, Eftekharimehrabad, A, Eini, E, Ekholuenetale, M, Ekundayo, TC, El Arab, RA, El Sayed Zaki, M, El-Dahiyat, F, Elemam, NM, Elgar, FJ, ElGohary, GMT, Elhabashy, HR, Elhadi, M, Elmehrath, AO, Elmeligy, OAA, Elshaer, M, Elsohaby, I, Emeto, TI, Esfandiari, N, Eshrati, B, Eslami, M, Esmaeili, SV, Estep, K, Etaee, F, Fabin, N, Fagbamigbe, AF, Fagbule, OF, Fahimi, S, Falzone, L, Fareed, M, Farinha, CSES, Faris, MEM, Faris, PS, Faro, A, Fasina, FO, Fatehizadeh, A, Fauk, NK, Fazylov, T, Feigin, VL, Feng, X, Fereshtehnejad, S-M, Feroze, AH, Ferrara, P, Ferrari, AJ, Ferreira, N, Fetensa, G, Feyisa, BR, Filip, I, Fischer, F, Fitriana, I, Flavel, J, Flohr, C, Flood, D, Flor, LS, Foigt, NA, Folayan, MO, Force, LM, Fortuna, D, Foschi, M, Franklin, RC, Freitas, A, Friedman, SD, Fux, B, G, S, Gaal, PA, Gaihre, S, Gajdács, M, Galali, Y, Gallus, S, Gandhi, AP, Ganesan, B, Ganiyani, MA, Garcia, V, Gardner, WM, Garg, RK, Gautam, RK, Gebi, TG, Gebregergis, MW, Gebrehiwot, M, Gebremariam, TBB, Gebremeskel, TG, Gerema, U, Getacher, L, Getahun, GKA, Getie, M, Ghadirian, F, Ghafarian, S, Ghaffari Jolfayi, A, Ghailan, KY, Ghajar, A, Ghasemi, M, Ghasempour Dabaghi, G, Ghasemzadeh, A, Ghassemi, F, Ghazy, RM, Gholami, A, Gholamrezanezhad, A, Gholizadeh, N, Ghorbani, M, Gil, AU, Gil, GF, Gilbertson, NM, Gill, PS, Gill, TK, Gindaba, EZ, Girmay, A, Glasbey, JC, Gnedovskaya, EV, Göbölös, L, Godinho, MA, Goel, A, Golechha, M, Goleij, P, Golinelli, D, Gomes, NGM, Gopalani, SV, Gorini, G, Goudarzi, H, Goulart, AC, Gouravani, M, Goyal, A, Graham, SM, Grivna, M, Grosso, G, Guan, S-Y, Guarducci, G, Gubari, MIM, Guha, A, Guicciardi, S, Gulati, S, Gulisashvili, D, Gunawardane, DA, Guo, C, Gupta, AK, Gupta, B, Gupta, M, Gupta, R, Gupta, RD, Gupta, S, Gupta, VB, Gupta, VK, Habibzadeh, F, Habibzadeh, P, Hadaro, TS, Hadian, Z, Haep, N, Haghi-Aminjan, H, Haghmorad, D, Hagins, H, Haile, D, Hailu, A, Hajj Ali, A, Halboub, ES, Halimi, A, Hall, BJ, Haller, S, Halwani, R, Hamadeh, RR, Hamdy, NM, Hameed, S, Hamidi, S, Hammoud, A, Hanif, A, Hanifi, N, Haq, ZA, Haque, MR, Harapan, H, Hargono, A, Haro, JM, Hasaballah, AI, Hasan, I, Hasan, MJ, Hasan, SMM, Hasani, H, Hasanian, M, Hashmeh, N, Hasnain, MS, Hassan, A, Hassan, I, Hassan Zadeh Tabatabaei, MS, Hassani, S, Hassanipour, S, Hassankhani, H, Haubold, J, Havmoeller, RJ, Hay, SI, Hebert, JJ, Hegazi, OE, Hegena, TY, Heidari, G, Heidari, M, Helfer, B, Hemmati, M, Henson, CA, Herbert, ME, Herteliu, C, Heuer, A, Hezam, K, Hinneh, TK, Hiraike, Y, Hoan, NQ, Holla, R, Hon, J, Hoque, ME, Horita, N, Hossain, S, Hosseini, SE, Hosseinzadeh, H, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hoven, H, Hsairi, M, Hsu, JM, Hu, C, Huang, J, Huda, MN, Hulland, EN, Hultström, M, Hushmandi, K, Hussain, J, Hussein, NR, Huynh, CK, Huynh, H-H, Ibitoye, SE, Idowu, OO, Ihler, AL, Ikeda, N, Ikuta, KS, Ilesanmi, OS, Ilic, IM, Ilic, MD, Imam, MT, Immurana, M, Inbaraj, LR, Irham, LM, Isa, MA, Islam, MR, Ismail, F, Ismail, NE, Iso, H, Isola, G, Iwagami, M, Iwu, CCD, Iwu-Jaja, CJ, J, V, Jaafari, J, Jacob, L, Jacobsen, KH, Jadidi-Niaragh, F, Jahankhani, K, Jahanmehr, N, Jahrami, H, Jain, A, Jain, N, Jairoun, AA, Jaiswal, A, Jakovljevic, M, Jalilzadeh Yengejeh, R, Jamora, RDG, Jatau, AI, Javadov, S, Javaheri, T, Jayaram, S, Jeganathan, J, Jeswani, BM, Jiang, H, Johnson, CO, Jokar, M, Jomehzadeh, N, Jonas, JB, Joo, T, Joseph, A, Joseph, N, Joshi, V, Joshua, CE, Jozwiak, JJ, Jürisson, M, Kaambwa, B, Kabir, A, Kabir, Z, Kadashetti, V, Kahn, EM, Kalani, R, Kaliyadan, F, Kalra, S, Kamath, R, Kanagasabai, T, Kanchan, T, Kandel, H, Kanmiki, EW, Kanmodi, KK, Kansal, SK, Kapner, DJ, Kapoor, N, Karagiannidis, E, Karajizadeh, M, Karakasis, P, Karanth, SD, Karaye, IM, Karch, A, Karim, A, Karimi, H, Karmakar, S, Kashoo, FZ, Kasraei, H, Kassahun, WD, Kassebaum, NJ, Kassel, MB, Katikireddi, SV, Kauppila, JH, Kawakami, N, Kaydi, N, Kayode, GA, Kazemi, F, Keiyoro, PN, Kemmer, L, Kempen, JH, Kerr, JA, Kesse-Guyot, E, Khader, YS, Khafaie, MA, Khajuria, H, Khalaji, A, Khalil, M, Khalilian, A, Khamesipour, F, Khan, A, Khan, MN, Khan, M, Khan, MJ, Khan, MAB, Khanmohammadi, S, Khatab, K, Khatatbeh, H, Khatatbeh, MM, Khatib, MN, Khavandegar, A, Khayat Kashani, HR, Khidri, FF, Khodadoust, E, Khormali, M, Khorrami, Z, Khosla, AA, Khosrowjerdi, M, Khreis, H, Khusun, H, Kifle, ZD, Kim, K, Kim, MS, Kim, YJ, Kimokoti, RW, Kisa, A, Kisa, S, Knibbs, LD, Knudsen, AKS, Koh, DSQ, Kolahi, A-A, Kompani, F, Kong, J, Koren, G, Korja, M, Korshunov, VA, Korzh, O, Kosen, S, Kothari, N, Koul, PA, Koulmane Laxminarayana, SL, Krishan, K, Krishnamoorthy, V, Krishnamoorthy, Y, Krishnan, B, Krohn, KJ, Kuate Defo, B, Kucuk Bicer, B, Kuddus, MA, Kuddus, M, Kugbey, N, Kuitunen, I, Kulimbet, M, Kulkarni, V, Kumar, A, Kumar, N, Kumar, V, Kundu, S, Kurmi, OP, Kusnali, A, Kusuma, D, Kutluk, T, La Vecchia, C, Ladan, MA, Laflamme, L, Lahariya, C, Lai, DTC, Lal, DK, Lallukka, T, Lám, J, Lan, Q, Lan, T, Landires, I, Lanfranchi, F, Langguth, B, Lansingh, VC, Laplante-Lévesque, A, Larijani, B, Larsson, AO, Lasrado, S, Lauriola, P, Le, H-H, Le, LKD, Le, NHH, Le, TTT, Leasher, JL, Ledda, C, Lee, M, Lee, PH, Lee, SW, Lee, SWH, Lee, YH, LeGrand, KE, Leigh, J, Leong, E, Lerango, TL, Lescinsky, H, Leung, J, Li, M-C, Li, W-Z, Li, W, Li, Y, Li, Z, Ligade, VS, Lim, L-L, Lim, SS, Lin, R-T, Lin, S, Liu, C, Liu, G, Liu, J, Liu, RT, Liu, S, Liu, W, Liu, X, Livingstone, KM, Llanaj, E, Lohiya, A, López-Bueno, R, Lopukhov, PD, Lorkowski, S, Lotufo, PA, Lozano, R, Lubinda, J, Lucchetti, G, Luo, L, lv, H, M Amin, HI, Ma, ZF, Maass, KL, Mabrok, M, Machairas, N, Machoy, M, Mafhoumi, A, Magdy Abd El Razek, M, Maghazachi, AA, Mahadeshwara Prasad, DR, Maharaj, SB, Mahmoud, MA, Mahmoudi, E, Majeed, A, Makram, OM, Makris, KC, Malasala, S, Maled, V, Malhotra, K, Malik, AA, Malik, I, Malinga, LA, Malta, DC, Mamun, AA, Manda, AL, Manla, Y, Mansour, A, Mansouri, B, Mansouri, P, Mansourian, M, Mansournia, MA, Mantovani, LG, Manu, E, Marateb, HR, Maravilla, JC, Marsh, E, Martinez, G, Martinez-Piedra, R, Martini, S, Martins-Melo, FR, Martorell, M, Marx, W, Maryam, S, Mathangasinghe, Y, Mathioudakis, AG, Matozinhos, FP, Mattumpuram, J, Maugeri, A, Maulik, PK, Mayeli, M, Mazidi, M, Mazzotti, A, McGrath, JJ, McKee, M, McKowen, ALW, McLaughlin, SA, McPhail, MA, McPhail, SM, Mechili, EA, Mehmood, A, Mehmood, K, Mehrabani-Zeinabad, K, Mehrabi Nasab, E, Meier, T, Mejia-Rodriguez, F, Mekene Meto, T, Mekonnen, BD, Menezes, RG, Mengist, B, Mensah, GA, Mensah, LG, Mentis, A-FA, Meo, SA, Meretoja, A, Meretoja, TJ, Mersha, AM, Mesfin, BA, Mestrovic, T, Mettananda, KCD, Mettananda, S, Miazgowski, T, Micha, G, Michalek, IM, Micheletti Gomide Nogueira de Sá, AC, Miller, TR, Mirarefin, M, Mirghafourvand, M, Mirica, A, Mirijello, A, Mirrakhimov, EM, Mirshahi, A, Mirzaei, M, Mishra, AK, Mishra, V, Mitchell, PB, Mithra, P, Mittal, C, Moazen, B, Moberg, ME, Mocciaro, G, Mohamadkhani, A, Mohamed, AZ, Mohamed, AI, Mohamed, J, Mohamed, MFH, Mohamed, NS, Mohammadi, E, Mohammadi, S, Mohammadian-Hafshejani, A, Mohammadifard, N, Mohammed, H, Mohammed, M, Mohammed, S, Mokdad, AH, Monasta, L, Mondello, S, Moni, MA, Moodi Ghalibaf, A, Moore, CE, Moradi, M, Moradi, Y, Moraga, P, Morawska, L, Moreira, RS, Morovatdar, N, Morrison, SD, Morze, J, Mosaddeghi Heris, R, Mossialos, E, Motappa, R, Mougin, V, Mousavi, P, Msherghi, A, Mubarik, S, Muccioli, L, Mueller, UO, Mulita, F, Mullany, EC, Munjal, K, Murillo-Zamora, E, Murlimanju, BV, Musina, A-M, Mustafa, G, Muthu, S, Muthupandian, S, Muthusamy, R, Muzaffar, M, Myung, W, Nafei, A, Nagarajan, AJ, Nagaraju, SP, Nagel, G, Naghavi, M, Naghavi, P, Naik, GR, Naik, G, Nainu, F, Nair, TS, Najdaghi, S, Nakhostin Ansari, N, Nanavaty, DP, Nangia, V, Narasimha Swamy, S, Narimani Davani, D, Nascimento, BR, Nascimento, GG, Nashwan, AJ, Natto, ZS, Nauman, J, Navaratna, SNK, Naveed, M, Nayak, BP, Nayak, VC, Ndejjo, R, Nduaguba, SO, Negash, H, Negoi, I, Negoi, RI, Nejadghaderi, SA, Nejjari, C, Nematollahi, MH, Nepal, S, Neupane, S, Ng, M, Nguefack-Tsague, G, Ngunjiri, JW, Nguyen, DH, Nguyen, NNY, Nguyen, PT, Nguyen, VT, Nguyen Tran Minh, D, Niazi, RK, Nicholson, SI, Nie, J, Nikoobar, A, Nikpoor, AR, Ningrum, DNA, Nnaji, CA, Noman, EA, Nomura, S, Noroozi, N, Norrving, B, Noubiap, JJ, Nri-Ezedi, CA, Ntaios, G, Ntsekhe, M, Nunemo, MH, Nurrika, D, Nutor, JJ, Oancea, B, O'Connell, EM, Odetokun, IA, O'Donnell, MJ, Oduro, MS, Ogunfowokan, AA, Ogunkoya, A, Oh, I-H, Okati-Aliabad, H, Okeke, SR, Okekunle, AP, Okonji, OC, Olagunju, AT, Olasupo, OO, Olatubi, MI, Oliveira, AB, Oliveira, GMM, Olorukooba, AA, Olufadewa, II, Olusanya, BO, Olusanya, JO, Oluwafemi, YD, Omar, HA, Omar Bali, A, Omer, GL, Ong, KL, Ong, S, Onwujekwe, OE, Onyedibe, KI, Oppong, AF, Ordak, M, Orish, VN, Ornello, R, Orpana, HM, Ortiz, A, Ortiz-Prado, E, Osman, WMS, Ostroff, SM, Osuagwu, UL, Otoiu, A, Otstavnov, N, Otstavnov, SS, Ouyahia, A, Owolabi, MO, Oyeyemi, IT, Oyeyemi, OT, P A, MP, Pacheco-Barrios, K, Padron-Monedero, A, Padubidri, JR, Pal, PK, Palicz, T, Pan, F, Pan, H-F, Pana, A, Panda, SK, Panda-Jonas, S, Pandey, A, Pandi-Perumal, SR, Pangaribuan, HU, Pantazopoulos, I, Pantea Stoian, AM, Papadopoulou, P, Parent, MC, Parija, PP, Parikh, RR, Park, S, Parsons, N, Pashaei, A, Pasovic, M, Passera, R, Patil, S, Patoulias, D, Patthipati, VS, Paudel, U, Pawar, S, Pazoki Toroudi, H, Peden, AE, Pedersini, P, Peng, M, Pensato, U, Pepito, VCF, Peprah, EK, Peprah, P, Peres, MFP, Perianayagam, A, Perico, N, Perna, S, Pesudovs, K, Petcu, I-R, Petermann-Rocha, FE, Pham, HT, Philip, AK, Phillips, MR, Pickering, BV, Pierannunzio, D, Pigeolet, M, Pigott, DM, Piracha, ZZ, Piradov, MA, 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YF, Wiangkham, T, Wickramasinghe, ND, Wilkerson, C, Willeit, P, Wilson, S, Wong, YJ, Wongsin, U, Wozniak, S, Wu, C, Wu, D, Wu, F, Wu, Z, Xia, J, Xiao, H, Xu, S, Xu, X, Xu, YY, Yadav, MK, Yaghoubi, S, Yamagishi, K, Yang, L, Yano, Y, Yaribeygi, H, Yasufuku, Y, Ye, P, Yesodharan, R, Yesuf, SA, Yezli, S, Yi, S, Yiğit, A, Yigzaw, ZA, Yin, D, Yip, P, Yismaw, MB, Yon, DK, Yonemoto, N, You, Y, Younis, MZ, Yousefi, Z, Yu, C, Yu, Y, Zadey, S, Zadnik, V, Zakham, F, Zaki, N, Zakzuk, J, Zamagni, G, Zaman, SB, Zandieh, GGZ, Zanghì, A, Zar, HJ, Zare, I, Zarimeidani, F, Zastrozhin, MS, Zeng, Y, Zhai, C, Zhang, AL, Zhang, H, Zhang, L, Zhang, M, Zhang, Y, Zhang, Z, Zhang, Z-J, Zhao, H, Zhao, JT, Zhao, X-JG, Zhao, Y, Zhong, C, Zhou, J, Zhou, S, Zhu, B, Zhu, L, Zhu, Z, Ziaeian, B, Ziafati, M, Zielińska, M, Zimsen, SRM, Zoghi, G, Zoller, T, Zumla, A, Zyoud, SH, Murray, CJL, and Gakidou, E
Published: 2024

2. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Naghavi, M, Ong, KL, Aali, A, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbasi-Kangevari, M, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdelwahab, A, Abdollahi, M, Abdollahifar, M-A, Abdoun, M, Abdulah, DM, Abdullahi, A, Abebe, M, Abebe, SS, Abedi, A, Abegaz, KH, Abhilash, ES, Abidi, H, Abiodun, O, Aboagye, RG, Abolhassani, H, Abolmaali, M, Abouzid, M, Aboye, GB, Abreu, LG, Abrha, WA, Abtahi, D, Abu Rumeileh, S, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Abu-Rmeileh, NME, Aburuz, S, Abu-Zaid, A, Accrombessi, MMK, Adal, TG, Adamu, AA, Addo, IY, Addolorato, G, Adebiyi, AO, Adekanmbi, V, Adepoju, AV, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adeyinka, DA, Adeyomoye, OI, Admass, BAA, Adnani, QES, Adra, S, Afolabi, AA, Afzal, MS, Afzal, S, Agampodi, SB, Agasthi, P, Aggarwal, M, Aghamiri, S, Agide, FD, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmad, T, Ahmadi, K, Ahmadzade, AM, Ahmed, A, Ahmed, H, Ahmed, LA, Ahmed, MS, Ahmed, MB, Ahmed, SA, Ajami, M, Aji, B, Akara, EM, Akbarialiabad, H, Akinosoglou, K, Akinyemiju, T, Akkaif, MA, Akyirem, S, Al Hamad, H, Al Hasan, SM, Alahdab, F, Alalalmeh, SO, Alalwan, TA, Al-Aly, Z, Alam, K, Alam, M, Alam, N, Al-amer, RM, Alanezi, FM, Alanzi, TM, Al-Azzam, S, Albakri, A, Albashtawy, M, AlBataineh, MT, Alcalde-Rabanal, JE, Aldawsari, KA, Aldhaleei, WA, Aldridge, RW, Alema, HB, Alemayohu, MA, Alemi, S, Alemu, YM, Al-Gheethi, AAS, Alhabib, KF, Alhalaiqa, FAN, Al-Hanawi, MK, Ali, A, Ali, L, Ali, MU, Ali, R, Ali, S, Ali, SSS, Alicandro, G, Alif, SM, Alikhani, R, Alimohamadi, Y, Aliyi, AA, Aljasir, MAM, Aljunid, SM, Alla, F, Allebeck, P, Al-Marwani, S, Al-Maweri, SAA, Almazan, JU, Al-Mekhlafi, HM, Almidani, L, Almidani, O, Alomari, MA, Al-Omari, B, Alonso, J, Alqahtani, JS, Alqalyoobi, S, Alqutaibi, AY, Al-Sabah, SK, Altaany, Z, Altaf, A, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Alwafi, H, Al-Worafi, YM, Aly, H, Aly, S, Alzoubi, KH, Amani, R, Amare, AT, Amegbor, PM, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Amusa, GA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Andrei, T, Angus, C, Anil, A, Anil, S, Anoushiravani, A, Ansari, H, Ansariadi, A, Ansari-Moghaddam, A, Antony, CM, Antriyandarti, E, Anvari, D, Anvari, S, Anwar, S, Anwar, SL, Anwer, R, Anyasodor, AE, Aqeel, M, Arab, JP, Arabloo, J, Arafat, M, Aravkin, AY, Areda, D, Aremu, A, Aremu, O, Ariffin, H, Arkew, M, Armocida, B, Arndt, MB, Ärnlöv, J, Arooj, M, Artamonov, AA, Arulappan, J, Aruleba, RT, Arumugam, A, Asaad, M, Asadi-Lari, M, Asgedom, AA, Asghariahmadabad, M, Asghari-Jafarabadi, M, Ashraf, M, Aslani, A, Astell-Burt, T, Athar, M, Athari, SS, Atinafu, BTT, Atlaw, HW, Atorkey, P, Atout, MMW, Atreya, A, Aujayeb, A, Ausloos, M, Avan, A, Awedew, AF, Aweke, AM, Ayala Quintanilla, BP, Ayatollahi, H, Ayuso-Mateos, JL, Ayyoubzadeh, SM, Azadnajafabad, S, Azevedo, RMS, Azzam, AY, B, DB, Babu, AS, Badar, M, Badiye, AD, Baghdadi, S, Bagheri, N, Bagherieh, S, Bah, S, Bahadorikhalili, S, Bahmanziari, N, Bai, R, Baig, AA, Baker, JL, Bako, AT, Bakshi, RK, Balakrishnan, S, Balasubramanian, M, Baltatu, OC, Bam, K, Banach, M, Bandyopadhyay, S, Banik, PC, Bansal, H, Bansal, K, Barbic, F, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Bärnighausen, TW, Barone-Adesi, F, Barqawi, HJ, Barrero, LH, Barrow, A, Barteit, S, Barua, L, Basharat, Z, Bashiri, A, Basiru, A, Baskaran, P, Basnyat, B, Bassat, Q, Basso, JD, Basting, AVL, Basu, S, Batra, K, Baune, BT, Bayati, M, Bayileyegn, NS, Beaney, T, Bedi, N, Beghi, M, Behboudi, E, Behera, P, Behnoush, AH, Behzadifar, M, Beiranvand, M, Bejarano Ramirez, DF, Béjot, Y, Belay, SA, Belete, CM, Bell, ML, Bello, MB, Bello, OO, Belo, L, Beloukas, A, Bender, RG, Bensenor, IM, Beran, A, Berezvai, Z, Berhie, AY, Berice, BN, Bernstein, RS, Bertolacci, GJ, Bettencourt, PJG, Beyene, KA, Bhagat, DS, 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RG, Cramer, KM, Criqui, MH, Cruz-Martins, N, Cuadra-Hernández, SM, Culbreth, GT, Cullen, P, Cunningham, M, Curado, MP, Dadana, S, Dadras, O, Dai, S, Dai, X, Dai, Z, Dalli, LL, Damiani, G, Darega Gela, J, Das, JK, Das, S, Dascalu, AM, Dash, NR, Dashti, M, Dastiridou, A, Davey, G, Dávila-Cervantes, CA, Davis Weaver, N, Davletov, K, De Leo, D, de Luca, K, Debele, AT, Debopadhaya, S, Degenhardt, L, Dehghan, A, Deitesfeld, L, Del Bo', C, Delgado-Enciso, I, Demessa, BH, Demetriades, AK, Deng, K, Deng, X, Denova-Gutiérrez, E, Deravi, N, Dereje, N, Dervenis, N, Dervišević, E, Des Jarlais, DC, Desai, HD, Desai, R, Devanbu, VGC, Dewan, SMR, Dhali, A, Dhama, K, Dhimal, M, Dhingra, S, Dhulipala, VR, Dias da Silva, D, Diaz, D, Diaz, MJ, Dima, A, Ding, DD, Ding, H, Dinis-Oliveira, RJ, Dirac, MA, Djalalinia, S, Do, THP, do Prado, CB, Doaei, S, Dodangeh, M, Dohare, S, Dokova, KG, Dolecek, C, Dominguez, R-MV, Dong, W, Dongarwar, D, D'Oria, M, Dorostkar, F, Dorsey, ER, dos Santos, WM, Doshi, R, 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Zhu, L, Ziafati, M, Zielińska, M, Zitoun, OA, Zoladl, M, Zou, Z, Zuhlke, LJ, Zumla, A, Zweck, E, Zyoud, SH, Wool, EE, and Murray, CJL
Published: 2024

3. Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Author: Vollset, SE, Ababneh, HS, Abate, YH, Abbafati, C, Abbasgholizadeh, R, Abbasian, M, Abbastabar, H, Abd Al Magied, AHA, Abd ElHafeez, S, Abdelkader, A, Abdelmasseh, M, Abd-Elsalam, S, Abdi, P, Abdollahi, M, Abdoun, M, Abdullahi, A, Abebe, M, Abiodun, O, Aboagye, RG, Abolhassani, H, Abouzid, M, Aboye, GB, Abreu, LG, Absalan, A, Abualruz, H, Abubakar, B, Abukhadijah, HJJ, Addolorato, G, Adekanmbi, V, Adetunji, CO, Adetunji, JB, Adeyeoluwa, TE, Adha, R, Adhikary, RK, Adnani, QES, Adzigbli, LA, Afrashteh, F, Afzal, MS, Afzal, S, Agbozo, F, Agodi, A, Agrawal, A, Agyemang-Duah, W, Ahinkorah, BO, Ahlstrom, AJ, Ahmad, A, Ahmad, F, Ahmad, MM, Ahmad, S, Ahmed, A, Ahmed, H, Ahmed, S, Ahmed, SA, Akinosoglou, K, Akkaif, MA, Akrami, AE, Akter, E, Al Awaidy, S, Al Hasan, SM, Al Mosa, AS, Al Ta'ani, O, Al Zaabi, OAM, Alahdab, F, Alajlani, MM, Al-Ajlouni, Y, Alalalmeh, SO, Al-Aly, Z, Alam, K, Alam, N, Alam, T, Alam, Z, Al-amer, RM, Alanezi, FM, Alanzi, TM, Albakri, A, Aldhaleei, WA, Aldridge, RW, Alemohammad, SY, Alemu, YM, Al-Gheethi, AAS, Al-Hanawi, MK, Ali, A, Ali, I, Ali, MU, Ali, R, Ali, SSS, Ali, VE, Ali, W, Al-Ibraheem, A, Alicandro, G, Alif, SM, Aljunid, SM, Alla, F, Almazan, JU, Al-Mekhlafi, HM, Alqutaibi, AY, Alrawashdeh, A, Alrousan, SM, Al-Sabah, SK, Alsabri, MA, Altaany, Z, Al-Tammemi, AB, Al-Tawfiq, JA, Altirkawi, KA, Aluh, DO, Alvis-Guzman, N, Al-Wardat, MS, Al-Worafi, YM, Aly, H, Alyahya, MS, Alzoubi, KH, Al-Zyoud, W, Amani, R, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amirzade-Iranaq, MH, Amu, H, Amugsi, DA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Andrei, T, Andrews, EA, Anil, A, Anil, S, Anoushiravani, A, Antony, CM, Antriyandarti, E, Anuoluwa, BS, Anvari, S, Anyasodor, AE, Appiah, F, Aquilano, M, Arab, JP, Arabloo, J, Arafa, EA, Arafat, M, Aravkin, AY, Ardekani, A, Areda, D, Aregawi, BB, Aremu, A, Ariffin, H, Arkew, M, Armani, K, Artamonov, AA, Arumugam, A, Asghari-Jafarabadi, M, Ashbaugh, C, Astell-Burt, T, Athari, SS, 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Silva, AG, Dadras, O, Dai, X, Dai, Z, Dalton, BE, Damiani, G, Dandona, L, Dandona, R, Das, JK, Das, S, Dash, NR, Davletov, K, De la Hoz, FP, De Leo, D, Debopadhaya, S, Delgado-Enciso, I, Denova-Gutiérrez, E, Dervenis, N, Desai, HD, Devanbu, VGC, Dewan, SMR, Dhama, K, Dhane, AS, Dhingra, S, Dias da Silva, D, Diaz, D, Diaz, LA, Diaz, MJ, Dima, A, Ding, DD, Do, THP, do Prado, CB, Dodangeh, M, Doegah, PT, Dohare, S, Dong, W, D'Oria, M, Doshi, R, Dowou, RK, Dsouza, HL, Dsouza, V, Dube, J, Dumith, SC, Duncan, BB, Duraes, AR, Duraisamy, S, Durojaiye, OC, Dushpanova, A, Dutta, S, Dzianach, PA, Dziedzic, AM, Eboreime, E, Ebrahimi, A, Ebrahimi Kalan, M, Edinur, HA, Efendi, F, Eikemo, TA, Eini, E, Ekundayo, TC, El Arab, RA, El Sayed, I, Elamin, O, Elemam, NM, ElGohary, GMT, Elhadi, M, Elmeligy, OAA, Elmoselhi, AB, Elshaer, M, Elsohaby, I, Eltahir, ME, Emeto, TI, Eshrati, B, Eslami, M, Esmaeili, Z, Fabin, N, Fagbamigbe, AF, Fagbule, OF, Falzone, L, Fareed, M, Farinha, CSES, Faris, MEM, Faro, A, Fasihi, K, Fatehizadeh, A, Fauk, NK, Fazylov, T, Feigin, VL, Fekadu, G, Feng, X, Fereshtehnejad, S-M, Ferrara, P, Ferreira, N, Firew, BS, Fischer, F, Fitriana, I, Flavel, J, Flor, LS, Folayan, MO, Foley, KM, Fonzo, M, Force, LM, Foschi, M, Freitas, A, Fridayani, NKY, Fukutaki, KG, Furtado, JM, Fux, B, Gaal, PA, Gadanya, MA, Gallus, S, Ganesan, B, Ganiyani, MA, Gautam, RK, Gebi, TG, Gebregergis, MW, Gebrehiwot, M, Getacher, L, Getahun, GKA, Gething, PW, Ghadimi, DJ, Ghadirian, F, Ghafarian, S, Ghailan, KY, Ghasemi, M, Ghasempour Dabaghi, G, Ghazy, RM, Ghoba, S, Gholami, E, Gholamrezanezhad, A, Gholizadeh, N, Ghorbani, M, Ghorbani Vajargah, P, Ghotbi, E, Gil, AU, Gill, TK, Girmay, A, Glasbey, JC, Glushkova, EV, Gnedovskaya, EV, Göbölös, L, Goldust, M, Goleij, P, Golinelli, D, Gopalani, SV, Goulart, AC, Gouravani, M, Goyal, A, Grivna, M, Grosso, G, Guarducci, G, Gubari, MIM, Guicciardi, S, Guimarães, RA, Gulati, S, Gulisashvili, D, Gunawardane, DA, Guo, C, Gupta, AK, Gupta, R, Gupta, S, Gupta, VK, Haakenstad, A, Hadi, NR, Haep, N, Hafiz, A, Haghmorad, D, Haile, D, Hajj Ali, A, Haj-Mirzaian, A, Halboub, ES, Haller, S, Halwani, R, Hamagharib Abdullah, K, Hamdy, NM, Hamoudi, R, Hanifi, N, Hankey, GJ, Haq, ZA, Haque, MR, Harapan, H, Hargono, A, Haro, JM, Hasaballah, AI, Hasan, SMM, Hasanian, M, Hasnain, MS, Hassan, A, Haubold, J, Hay, SI, Hebert, JJ, Hegazi, OE, Heidari, M, Hemmati, M, Henson, CA, Herrera-Serna, BY, Herteliu, C, Heydari, M, Hezam, K, Hidayana, I, Hiraike, Y, Hoan, NQ, Holla, R, Hoogar, P, Horita, N, Hossain, MM, Hosseinzadeh, H, Hosseinzadeh, M, Hostiuc, M, Hostiuc, S, Hu, C, Huang, J, Hultström, M, Hundie, TG, Hunt, AJ, Hushmandi, K, Hussain, J, Hussain, MA, Hussein, NR, Huynh, H-H, Hwang, B-F, Ibitoye, SE, Iftikhar, PM, Ikiroma, AI, Ikwegbue, PC, Ilic, IM, Ilic, MD, Immurana, M, Isa, MA, Islam, MR, Islam, SMS, Ismail, F, Ismail, NE, Isola, G, Iwagami, M, Iyamu, IO, Jacob, L, Jacobsen, KH, Jafarinia, M, Jahankhani, K, Jahanmehr, N, Jain, N, Jairoun, AA, Jakhmola Mani, DR, Jamil, S, Jamora, RDG, Jatau, AI, Javadov, S, Javaheri, T, Jayaram, S, Jee, SH, Jeganathan, J, Jiang, H, Jokar, M, Jonas, JB, Joseph, N, Joshua, CE, Jürisson, M, K, V, Kabir, A, Kabir, Z, Kadashetti, V, Kalankesh, LR, Kalra, S, Kamath, A, Kamath, R, Kamireddy, A, Kanaan, M, Kanchan, T, Kanmiki, EW, Kanmodi, KK, Kansal, SK, Karim, A, Karkhah, S, Kashoo, FZ, Kasraei, H, Kassel, MB, Katikireddi, SV, Kauppila, JH, Kaur, H, Kayode, GA, Kazemi, F, Kazemian, S, Kebede, F, Kendal, ES, Kesse-Guyot, E, Khademvatan, S, Khajuria, H, Khalaji, A, Khalid, A, Khalid, N, Khalilian, A, Khamesipour, F, Khan, F, Khan, MJ, Khan, MAB, Khanmohammadi, S, Khatab, K, Khatatbeh, H, Khatatbeh, MM, Khatib, MN, Khayat Kashani, HR, Kheirallah, KA, Khokhar, M, Khormali, M, Khorrami, Z, Khosla, AA, Khosravi, M, Khosrowjerdi, M, Khubchandani, J, Kifle, ZD, Kim, G, Kim, JS, Kim, MS, Kim, YJ, Kimokoti, RW, Kisa, A, Kisa, S, Knibbs, LD, Knudsen, AKS, Kochhar, S, Kolahi, A-A, Kompani, F, Koren, G, Korzh, O, Krishan, K, Krishna, V, Krishnamoorthy, V, Kucuk Bicer, B, Kuddus, MA, Kuddus, M, Kuitunen, I, Kujan, O, Kulimbet, M, Kulkarni, V, Kumar, GA, Kumar, H, Kumar, N, Kumar, R, Kumar, V, Kundu, A, Kusuma, D, Kyei-Arthur, F, Kytö, V, Kyu, HH, La Vecchia, C, Lacey, B, Ladan, MA, Laflamme, L, Lahariya, C, Lai, DTC, Lalloo, R, Lallukka, T, Lám, J, Lan, Q, Lan, T, Landires, I, Lanfranchi, F, Langguth, B, Lansingh, VC, Laplante-Lévesque, A, Larijani, B, Larsson, AO, Lasrado, S, Lauriola, P, Lawlor, HR, Le, H-H, Le, LKD, Le, NHH, Le, TTT, Le, TDT, Leasher, JL, Lee, DW, Lee, M, Lee, PH, Lee, S-W, Lee, SW, Lee, SWH, Lee, YH, Leigh, J, Leong, E, Li, M-C, Libra, M, Ligade, VS, Lim, L-L, Lim, SS, Limenh, LW, Lindholm, D, Lindstedt, PA, Listl, S, Liu, G, Liu, S, Liu, X, Llanaj, E, López-Bueno, R, López-Gil, JF, Loreche, AM, Lotufo, PA, Lozano, R, Lubinda, J, Lucchetti, G, Luo, L, Lusk, JB, Lv, L, M Amin, HI, Ma, ZF, Maass, KL, Machairas, N, Machoy, M, Madureira-Carvalho, ÁM, Magdy Abd El 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Published: 2024

4. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author: Ferrari, AJ, Santomauro, DF, Aali, A, Abate, YH, Abbafati, C, Abbastabar, H, Abd ElHafeez, S, Abdelmasseh, M, Abd-Elsalam, S, Abdollahi, A, Abdullahi, A, Abegaz, KH, Abeldaño Zuñiga, RA, Aboagye, RG, Abolhassani, H, Abreu, LG, Abualruz, H, Abu-Gharbieh, E, Abu-Rmeileh, NME, Ackerman, IN, Addo, IY, Addolorato, G, Adebiyi, AO, Adepoju, AV, Adewuyi, HO, Afyouni, S, Afzal, S, Agodi, A, Ahmad, A, Ahmad, D, Ahmad, F, Ahmad, S, Ahmed, A, Ahmed, LA, Ahmed, MB, Ajami, M, Akinosoglou, K, Akkaif, MA, Al Hasan, SM, Alalalmeh, SO, Al-Aly, Z, Albashtawy, M, Aldridge, RW, Alemu, MD, Alemu, YM, Alene, KA, Al-Gheethi, AAS, Alharrasi, M, Alhassan, RK, Ali, MU, Ali, R, Ali, SSS, Alif, SM, Aljunid, SM, Al-Marwani, S, Almazan, JU, Alomari, MA, Al-Omari, B, Altaany, Z, Alvis-Guzman, N, Alvis-Zakzuk, NJ, Alwafi, H, Al-Wardat, MS, Al-Worafi, YM, Aly, S, Alzoubi, KH, Amare, AT, Amegbor, PM, Ameyaw, EK, Amin, TT, Amindarolzarbi, A, Amiri, S, Amugsi, DA, Ancuceanu, R, Anderlini, D, Anderson, DB, Andrade, PP, Andrei, CL, Ansari, H, Antony, CM, Anwar, S, Anwar, SL, Anwer, R, Anyanwu, PE, Arab, JP, Arabloo, J, Arafat, M, Araki, DT, Aravkin, AY, Arkew, M, Armocida, B, Arndt, MB, Arooj, M, Artamonov, AA, Aruleba, RT, Arumugam, A, Ashbaugh, C, Ashemo, MY, Ashraf, M, Asika, MO, Askari, E, Astell-Burt, T, Athari, SS, Atorkey, P, Atout, MMW, Atreya, A, Aujayeb, A, Ausloos, M, Avan, A, Awotidebe, AW, Awuviry-Newton, K, Ayala Quintanilla, BP, Ayuso-Mateos, JL, Azadnajafabad, S, Azevedo, RMS, Babu, AS, Badar, M, Badiye, AD, Baghdadi, S, Bagheri, N, Bah, S, Bai, R, Baker, JL, Bakkannavar, SM, Bako, AT, Balakrishnan, S, Bam, K, Banik, PC, Barchitta, M, Bardhan, M, Bardideh, E, Barker-Collo, SL, Barqawi, HJ, Barrow, A, Barteit, S, Barua, L, Bashiri Aliabadi, S, Basiru, A, Basu, S, Bathini, PP, Batra, K, Baune, BT, Bayileyegn, NS, Behnam, B, Behnoush, AH, Beiranvand, M, Bejarano Ramirez, DF, Bell, ML, Bello, OO, Beloukas, A, Bensenor, IM, Berezvai, Z, Bernabe, E, Bernstein, RS, Bettencourt, PJG, Bhagavathula, AS, Bhala, N, Bhandari, D, Bhargava, A, Bhaskar, S, Bhat, V, Bhatti, GK, Bhatti, JS, Bhatti, MS, Bhatti, R, Bhutta, ZA, Bikbov, B, Bishai, JD, Bisignano, C, Bitra, VR, Bjørge, T, Bodolica, V, Bodunrin, AO, Bogale, EK, Bonakdar Hashemi, M, Bonny, A, Bora Basara, B, Borhany, H, Boxe, C, Brady, OJ, Bragazzi, NL, Braithwaite, D, Brant, LC, Brauer, M, Breitner, S, Brenner, H, Brown, J, Brugha, T, Bulamu, NB, Buonsenso, D, Burkart, K, Burns, RA, Busse, R, Bustanji, Y, Butt, ZA, Byun, J, Caetano dos Santos, FL, Calina, D, Cámera, LA, Campos-Nonato, IR, Cao, C, Capodici, A, Carr, S, Carreras, G, Carugno, A, Carvalho, M, Castaldelli-Maia, JM, Castañeda-Orjuela, CA, Castelpietra, G, Catapano, AL, Cattaruzza, MS, Caye, A, Cegolon, L, Cembranel, F, Cenderadewi, M, Cerin, E, Chakraborty, PA, Chan, JSK, Chan, RNC, Chandika, RM, Chandrasekar, EK, Charalampous, P, Chattu, VK, Chatzimavridou-Grigoriadou, V, Chen, AW, Chen, A-T, Chen, CS, Chen, H, Chen, NM, Cheng, ETW, Chimed-Ochir, 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Pease, SA, Peden, AE, Pedersini, P, Peng, M, Pensato, U, Pepito, VCF, Peprah, EK, Peprah, P, Perdigão, J, Pereira, MO, Perianayagam, A, Perico, N, Pesudovs, K, Petermann-Rocha, FE, Petri, WA, Pham, HT, Philip, AK, Phillips, MR, Pigeolet, M, Pigott, DM, Pillay, JD, Piracha, ZZ, Pirouzpanah, S, Plass, D, Plotnikov, E, Poddighe, D, Polinder, S, Postma, MJ, Pourtaheri, N, Prada, SI, Pradhan, PMS, Prakash, V, Prasad, M, Prates, EJS, Priscilla, T, Pritchett, N, Puri, P, Puvvula, J, Qasim, NH, Qattea, I, Qazi, AS, Qian, G, Rabiee Rad, M, Radhakrishnan, RA, Radhakrishnan, V, Raeisi Shahraki, H, Rafferty, Q, Raggi, A, Raghav, PR, Rahim, MJ, Rahman, MM, Rahman, MHU, Rahman, M, Rahman, MA, Rahmani, S, Rahmanian, M, Rahmawaty, S, Rajaa, S, Ramadan, MM, Ramasamy, SK, Ramasubramani, P, Ramazanu, S, Rana, K, Ranabhat, CL, Rancic, N, Rane, A, Rao, CR, Rao, K, Rao, M, Rao, SJ, Rashidi, M-M, Rathnaiah Babu, G, Rauniyar, SK, Rawaf, DL, Rawaf, S, Razo, C, Reddy, MMRK, Redwan, EMM, Reifels, L, Reiner Jr, 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M, Shah, H, Shah, NS, Shah, PA, Shahbandi, A, Shahid, S, Shahid, W, Shahwan, MJ, Shaikh, MA, Shakeri, A, Shalash, AS, Sham, S, Shamim, MA, Shamshirgaran, MA, Shamsi, MA, Shanawaz, M, Shankar, A, Shannawaz, M, Sharath, M, Sharifan, A, Sharifi-Rad, J, Sharma, M, Sharma, R, Sharma, S, Sharma, U, Sharma, V, Shastry, RP, Shavandi, A, Shayan, AM, Shayan, M, Shehabeldine, AME, Shetty, PH, Shibuya, K, Shifa, JE, Shiferaw, D, Shiferaw, WS, Shigematsu, M, Shiri, R, Shitaye, NA, Shittu, A, Shivakumar, KM, Shivarov, V, Shokati Eshkiki, Z, Shool, S, Shrestha, S, Shuval, K, Sibhat, MM, Siddig, EE, Sigfusdottir, ID, Silva, DAS, Silva, JP, Silva, LMLR, Silva, S, Simpson, CR, Singal, A, Singh, A, Singh, BB, Singh, H, Singh, JA, Singh, M, Singh, P, Skou, ST, Sleet, DA, Slepak, ELN, Solanki, R, Soliman, SSM, Song, S, Song, Y, Sorensen, RJD, Soriano, JB, Soyiri, IN, Spartalis, M, Sreeramareddy, CT, Stark, BA, Starodubova, AV, Stein, C, Stein, DJ, Steiner, C, Steiner, TJ, Steinmetz, JD, Steiropoulos, P, 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Unsworth, CA, Upadhyay, E, Urso, D, Usman, JS, Vahabi, SM, Vaithinathan, AG, Van den Eynde, J, Varga, O, Varma, RP, Vart, P, Vasankari, TJ, Vasic, M, Vaziri, S, Vellingiri, B, Venketasubramanian, N, Veroux, M, Verras, G-I, Vervoort, D, Villafañe, JH, Violante, FS, Vlassov, V, Vollset, SE, Volovat, SR, Vongpradith, A, Waheed, Y, Wang, C, Wang, F, Wang, N, Wang, S, Wang, Y, Wang, Y-P, Ward, P, Wassie, EG, Weaver, MR, Weerakoon, KG, Weintraub, RG, Weiss, DJ, Weldemariam, AH, Wells, KM, Wen, YF, Whisnant, JL, Whiteford, HA, Wiangkham, T, Wickramasinghe, DP, Wickramasinghe, ND, Wilandika, A, Wilkerson, C, Willeit, P, Wimo, A, Woldegebreal, DH, Wolf, AW, Wong, YJ, Woolf, AD, Wu, C, Wu, F, Wu, X, Wu, Z, Wulf Hanson, S, Xia, Y, Xiao, H, Xu, X, Xu, YY, Yadav, L, Yadollahpour, A, Yaghoubi, S, Yamagishi, K, Yang, L, Yano, Y, Yao, Y, Yaribeygi, H, Yazdanpanah, MH, Ye, P, Yehualashet, SS, Yesuf, SA, Yezli, S, Yiğit, A, Yiğit, V, Yigzaw, ZA, Yismaw, Y, Yon, DK, Yonemoto, N, Younis, MZ, Yu, C, Yu, Y, Yusuf, H, Zahid, MH, Zakham, F, Zaki, L, Zaki, N, Zaman, BA, Zamora, N, Zand, R, Zandieh, GGZ, Zar, HJ, Zarrintan, A, Zastrozhin, MS, Zhang, H, Zhang, N, Zhang, Y, Zhao, H, Zhong, C, Zhong, P, Zhou, J, Zhu, Z, Ziafati, M, Zielińska, M, Zimsen, SRM, Zoladl, M, Zumla, A, Zyoud, SH, Vos, T, and Murray, CJL
Published: 2024

5. Molecular Assessment of Genetic Diversity Among Male, Female and Hermaphrodite Simarouba species Using Random Amplified Polymorphic Deoxyribose Nucleic Acid Markers

Author: Vaidya Gayatri and Naik Gr
Subjects: Genetics, Aging, Genetic diversity, Male female, Biology, Simarouba, biology.organism_classification, Health Professions (miscellaneous), Biochemistry, Genetics and Molecular Biology (miscellaneous), General Biochemistry, Genetics and Molecular Biology, chemistry.chemical_compound, Hermaphrodite, Deoxyribose, chemistry, General Health Professions, Nucleic acid, Dentistry (miscellaneous), General Dentistry
Published: 2021

6. Progress in Brain Computer Interface: Challenges and Opportunities.

Author: Saha, S, Mamun, KA, Ahmed, K, Mostafa, R, Naik, GR, Darvishi, S, Khandoker, AH, Baumert, M, Saha, S, Mamun, KA, Ahmed, K, Mostafa, R, Naik, GR, Darvishi, S, Khandoker, AH, and Baumert, M
Abstract: Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
Published: 2021

7. An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology

Author: Naik, GR, Selvan, SE, Arjunan, SP, Acharyya, A, Kumar, DK, Ramanujam, A, and Nguyen, HT
Subjects: Male, Electromyography, Movement, Entropy, Biomedical Engineering, Discriminant Analysis, Knee Injuries, Walking, Healthy Volunteers, Biomechanical Phenomena, Young Adult, Lower Extremity, Humans, Muscle, Skeletal, Algorithms
Abstract: © 2001-2011 IEEE. Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.
Published: 2018

8. Prediction of freezing of gait in patients with Parkinson’s disease using EEG signals

Author: Handojoseno, AMA, Naik, GR, Gilat, M, Shine, JM, Nguyen, TN, Quynh, TLY, Lewis, SJG, and Nguyen, HT
Subjects: Male, Quality of Life, Humans, Parkinson Disease, Electroencephalography, Bayes Theorem, Female, Middle Aged, Gait, Medical Informatics, Gait Disorders, Neurologic, Aged
Abstract: © 2018 The authors and IOS Press. All rights reserved. Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson’s disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD.
Published: 2018

9. Nonparametric dynamical model of cardiorespiratory responses at the onset and offset of treadmill exercises

Author: Yu, H, Ye, L, Naik, GR, Song, R, Nguyen, HT, Su, SW, Yu, H, Ye, L, Naik, GR, Song, R, Nguyen, HT, and Su, SW
Abstract: © 2018, International Federation for Medical and Biological Engineering. This paper applies a nonparametric modelling method with kernel-based regularization to estimate the carbon dioxide production during jogging exercises. The kernel selection and regularization strategies have been discussed; several commonly used kernels are compared regarding the goodness-of-fit, sensitivity, and stability. Based on that, the most appropriate kernel is then selected for the construction of the regularization term. Both the onset and offset of the jogging exercises are investigated. We compare the identified nonparametric models, which include both impulse response models and step response models for the two periods, as well as the relationship between oxygen consumption and carbon dioxide production. The result statistically indicates that the steady-state gain of the carbon dioxide production in the onset of exercise is bigger than that in the offset while the response time of both onset and offset are similar. Compared with oxygen consumption, the response speed of carbon dioxide production is slightly slower in both onset and offset period while its steady-state gains are similar for both periods. The effectiveness of the kernel-based method for the dynamic modelling of cardiorespiratory response to exercise is also well demonstrated. [Figure not available: see fulltext.].
Published: 2018

10. Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG

Author: Acharyya, A, Jadhav, PN, Bono, V, Maharatna, K, Naik, GR, Acharyya, A, Jadhav, PN, Bono, V, Maharatna, K, and Naik, GR
Abstract: © 2018 Elsevier B.V. Background and objective: EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment. Therefore, it would be ideal if these artifacts can be removed real time on the hardware platform in an automated fashion and then the denoised EEG can be used for online diagnosis in a pervasive personalized healthcare environment without the need of any reference electrode. Methods: In this paper we propose a reliable, robust and automated methodology to solve the aforementioned problem. The proposed methodology is based on the Haar function based Wavelet decompositions with simple threshold based wavelet domain denoising and artifacts removal schemes. Subsequently hardware implementation results are also presented. 100 EEG data from Physionet, Klinik für Epileptologie, Universität Bonn, Germany, Caltech EEG databases and 7 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The proposed methodology is prototyped and validated using FPGA platform. Results: Like existing literature, the performance of the proposed methodology is also measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and improvement in hardware delay of 53.58% compared to state-of-the art approaches. Hardware
Published: 2018

11. Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine

Author: Ly, QT, Gilat, M, Chai, R, Martens, KAE, Georgiades, M, Naik, GR, Tran, Y, Lewis, SJG, and Nguyen, HT
Subjects: Support Vector Machine, Wavelet Analysis, Humans, Parkinson Disease, Gait, Gait Disorders, Neurologic
Abstract: © 2017 IEEE. Gait initiation Failure (GIF) is the situation in which patients with Parkinson's disease (PD) feel as if their feet get 'stuck' to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG.
Published: 2017

12. Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals

Author: Ly, QT, Gilat, M, Chai, R, Martens, KAE, Georgiades, M, Naik, GR, Tran, Y, Lewis, SJG, and Nguyen, HT
Subjects: Quality of Life, Humans, Parkinson Disease, Electroencephalography, Bayes Theorem, Gait, Gait Disorders, Neurologic
Abstract: © 2017 IEEE. Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation.
Published: 2017

13. Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions

Author: Al-Ali, AKH, Senadji, B, Naik, GR, Al-Ali, AKH, Senadji, B, and Naik, GR
Abstract: © 2017 IEEE. The performance of forensic speaker verification degrades severely in the presence of high levels of environmental noise and reverberation conditions. Multiple channel speech enhancement algorithms are a possible solution to reduce the effect of environmental noise from the noisy speech signals. Although multiple speech enhancement algorithms such as multi-run independent component analysis (ICA) were used in previous studies to improve the performance of recognition in biosignal applications, the effectiveness of multi-run ICA algorithm to improve the performance of noisy forensic speaker verification under reverberation conditions has not been investigated yet. In this paper, the multi-run ICA algorithm is used to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times. Wavelet-based mel frequency cepstral coefficients (MFCCs) feature warping approach is applied to the enhanced speech signals to extract the robust features to environmental noise and reverberation conditions. The state-of-The-Art intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) are used as a classifier in our approach. Experimental results show that forensic speaker verification based on the multi-run ICA algorithm achieves significant improvements in equal error rate (EER) of 60.88%, 51.84%, 66.15% over the baseline noisy speaker verification when enrolment speech signals reverberated at 0.15 sec and the test speech signals were mixed with STREET, CAR and HOME noises respectively at-10 dB signal to noise ratio (SNR).
Published: 2017

14. Computational algorithms underlying the time-based detection of sudden cardiac arrest via electrocardiographic markers

Author: Raka, AG, Naik, GR, Chai, R, Raka, AG, Naik, GR, and Chai, R
Abstract: © 2017 by the authors. Early detection of sudden cardiac arrest (SCA) is critical to prevent serious repercussion such as irreversible neurological damage and death. Currently, the most effective method involves analyzing electrocardiogram (ECG) features obtained during ventricular fibrillation. In this study, data from 10 normal patients and 10 SCA patients obtained from Physiobank were used to statistically compare features, such as heart rate, R-R interval duration, and heart rate variability (HRV) features from which the HRV features were then selected for classification via linear discriminant analysis (LDA) and linear and fine Gaussian support vector machines (SVM) in order to determine the ideal time-frame in which SCA can be accurately detected. The best accuracy was obtained at 2 and 8 min prior to SCA onset across all three classifiers. However, accuracy rates of 75-80% were also obtained at time-frames as early as 50 and 40 min prior to SCA onset. These results are clinically important in the field of SCA, as early detection improves overall patient survival.
Published: 2017

15. Ponseti method in the management of clubfoot under 2 years of age: A systematic review

Author: Ganesan, B, Luximon, A, Al-Jumaily, A, Balasankar, SK, Naik, GR, Ganesan, B, Luximon, A, Al-Jumaily, A, Balasankar, SK, and Naik, GR
Abstract: © 2017 Ganesan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background: Congenital talipes equinovarus (CTEV), also known as clubfoot, is common congenital orthopedic foot deformity in children characterized by four components of foot deformities: hindfoot equinus, hindfoot varus, midfoot cavus, and forefoot adduction. Although a number of conservative and surgical methods have been proposed to correct the clubfoot deformity, the relapses of the clubfoot are not uncommon. Several previous literatures discussed about the technical details of Ponseti method, adherence of Ponseti protocol among walking age or older children. However there is a necessity to investigate the relapse pattern, compliance of bracing, number of casts used in treatment and the percentages of surgical referral under two years of age for clear understanding and better practice to achieve successful outcome without or reduce relapse. Therefore this study aims to review the current evidence of Ponseti method (manipulation, casting, percutaneous Achilles tenotomy, and bracing) in the management of clubfoot under two years of age. Materials and methods: Articles were searched from 2000 to 2015, in the following databases to identify the effectiveness of Ponseti method treatment for clubfoot: Medline, Cumulative Index to Nursing and Allied Health Literature (CINHAL), PubMed, and Scopus. The database searches were limited to articles published in English, and articles were focused on the effectiveness of Ponseti method on children with less than 2 years of age. Results: Of the outcome of 1095 articles from four electronic databases, twelve articles were included in the review. Pirani scoring system, Dimeglio scoring system, measuring the range of motion and rate of relapses were used as outcome measures. Co
Published: 2017

16. Shape memory effect of nano-ferromagnetic particle doped NiTi for orthopedic devices and rehabilitation techniques

Author: Gautam, A, Balouria, A, Acharyya, A, Acharyya, SG, Panwar, M, Naik, GR, Gautam, A, Balouria, A, Acharyya, A, Acharyya, SG, Panwar, M, and Naik, GR
Abstract: © 2017 IEEE. This paper introduces a novel shape memory alloy (SMA) material for the controllability in the shape recovery of traditional SMA for orthopedic devices and rehabilitation techniques. The proposed material is formed by doping nano-ferromagnetic particle into porous NiTi alloy. The finite element analysis of shape memory effect property of the different distribution of nano-ferromagnetic particle is done and compared for same load and boundary conditions. The comparative analysis of the percentage change in volume deformation when load is released (for 2nd step) shows an average of 2.55 % with standard deviation of 1.69 whereas on thermal loading (for 3rd step) shows an average of 94.94% with standard deviation of 7.75 for all heterogeneous distribution of nano-particles in porous NiTi alloy. Our findings are, all the different conditions of heterogeneous distributions of nano-ferromagnetic particle doped NiTi alloy exhibits its inherent SME property.
Published: 2017

17. CNN based approach for activity recognition using a wrist-worn accelerometer

Author: Panwar, M, Ram Dyuthi, S, Chandra Prakash, K, Biswas, D, Acharyya, A, Maharatna, K, Gautam, A, Naik, GR, Panwar, M, Ram Dyuthi, S, Chandra Prakash, K, Biswas, D, Acharyya, A, Maharatna, K, Gautam, A, and Naik, GR
Abstract: © 2017 IEEE. In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.
Published: 2017

18. Differences in lower limb muscle activation patterns during Sit to Stand Task for different heel heights

Author: Naik, GR, Pratihast, M, Chai, R, Al-Ani, A, Acharyya, A, Nguyen, HT, Naik, GR, Pratihast, M, Chai, R, Al-Ani, A, Acharyya, A, and Nguyen, HT
Abstract: © 2017 IEEE. The purpose of this study was to investigate differences in lower limb muscle activation patterns for females wearing shoes with different heel heights during Sit to Stand Task (STS). Ten female participants with no prior history of neurological disorders participated in this study. Surface electromyography (sEMG) characteristics were recorded for four different heel heights (ranging from 4cm to 10cm) while performing the STS task. Signal processing analysis suggests that muscle activities increases on elevated heel heights, which may induce muscle imbalance for frequent STS tasks. In addition, results of muscle utilisation (percentage) for different heel heights suggest that lower limb muscles tend to compensate in order to maintain postural balance.
Published: 2017

19. Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems

Author: Chai, R, Naik, GR, Ling, SH, Nguyen, HT, Chai, R, Naik, GR, Ling, SH, and Nguyen, HT
Abstract: © 2017 The Author(s). Background: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.
Published: 2017

20. Optimized phase-space reconstruction for accurate musical-instrument signal classification

Author: Guo, Y, Liu, Q, Wang, A, Sun, C, Tian, W, Naik, GR, Abraham, A, Guo, Y, Liu, Q, Wang, A, Sun, C, Tian, W, Naik, GR, and Abraham, A
Abstract: © 2016, Springer Science+Business Media New York. Traditional musical-instrument classification methods mainly use regions in the time or/and frequency characteristics, cepstrum characteristics, and MPEG-7 characteristics, and they often lead to erroneous classification. Therefore, there is need to develop a more suitable method that is more applicable to the nonlinear characteristics of musical-instrument signals and can avoid the abovementioned problems. In this paper, a musical-instrument classification method that couples the optimized phase-space reconstruction (OPSR) with a flexible neural tree (FNT) is proposed. As per nonlinear dynamic theory, a principal component analysis and correlation coefficient are used to optimize the phase-space reconstruction (PSR) method. Multidimensional PSR results for different musical-instrument signals are extracted as the main components, and the dimensionality is reduced by the OPSR method. A probability density function (PDF) is introduced in the feature extraction step to differentiate musical instruments according to the phase-space-reconstructible characteristics. A FNT is adopted as a classifier to tackle the variability in musical-instrument signals and to improve the adaptive ability of various target classification problems. Experimental testing has been conducted to show that the proposed OPSR–PDF–FNT algorithm gives superior performance over other comparable algorithms and can classify 12 musical instruments with an accuracy of 98.2 %.
Published: 2017

21. Improving EEG-based driver fatigue classification using sparse-deep belief networks

Author: Chai, R, Ling, SH, San, PP, Naik, GR, Nguyen, TN, Tran, Y, Craig, A, Nguyen, HT, Chai, R, Ling, SH, San, PP, Naik, GR, Nguyen, TN, Tran, Y, Craig, A, and Nguyen, HT
Abstract: © 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.
Published: 2017

22. Enhanced Forensic Speaker Verification Using a Combination of DWT and MFCC Feature Warping in the Presence of Noise and Reverberation Conditions

Author: Al-Ali, AKH, Dean, D, Senadji, B, Chandran, V, Naik, GR, Al-Ali, AKH, Dean, D, Senadji, B, Chandran, V, and Naik, GR
Abstract: © 2013 IEEE. Environmental noise and reverberation conditions severely degrade the performance of forensic speaker verification. Robust feature extraction plays an important role in improving forensic speaker verification performance. This paper investigates the effectiveness of combining features, mel frequency cepstral coefficients (MFCCs), and MFCC extracted from the discrete wavelet transform (DWT) of the speech, with and without feature warping for improving modern identity-vector (i-vector)-based speaker verification performance in the presence of noise and reverberation. The performance of i-vector speaker verification was evaluated using different feature extraction techniques: MFCC, feature-warped MFCC, DWT-MFCC, feature-warped DWT-MFCC, a fusion of DWT-MFCC and MFCC features, and fusion feature-warped DWT-MFCC and feature-warped MFCC features. We evaluated the performance of i-vector speaker verification using the Australian Forensic Voice Comparison and QUT-NOISE databases in the presence of noise, reverberation, and noisy and reverberation conditions. Our results indicate that the fusion of feature-warped DWT-MFCC and feature-warped MFCC is superior to other feature extraction techniques in the presence of environmental noise under the majority of signal-to-noise ratios (SNRs), reverberation, and noisy and reverberation conditions. At 0-dB SNR, the performance of the fusion of feature-warped DWT-MFCC and feature-warped MFCC approach achieves a reduction in average equal error rate of 21.33%, 20.00%, and 13.28% over feature-warped MFCC, respectively, in the presence of various types of environmental noises only, reverberation, and noisy and reverberation environments. The approach can be used for improving the performance of forensic speaker verification and it may be utilized for preparing legal evidence in court.
Published: 2017

23. Does heel height cause imbalance during sit-to-stand task: surface EMG perspective

Author: Naik, GR, Al-Ani, A, Gobbo, M, Nguyen, HT, Naik, GR, Al-Ani, A, Gobbo, M, and Nguyen, HT
Abstract: © 2017 Naik, Al-Ani, Gobbo and Nguyen. The purpose of this study was to determine whether electromyography (EMG) muscle activities around the knee differ during sit-to-stand (STS) and returning task for females wearing shoes with different heel heights. Sixteen healthy young women (age = 25.2 ± 3.9 years, body mass index = 20.8 ± 2.7 kg/m2 ) participated in this study. Electromyography signals were recorded from the two muscles, vastus medialis (VM) and vastus lateralis (VL) that involve in the extension of knee. The participants wore shoes with five different heights, including 4, 6, 8, 10, and 12 cm. Surface electromyography (sEMG) data were acquired during STS and stand-to-sit-returning (STSR) tasks. The data was filtered using a fourth order Butterworth (band pass) filter of 20–450 Hz frequency range. For each heel height, we extracted median frequency (MDF) and root mean square (RMS) features to measure sEMG activities between VM and VL muscles. The experimental results (based on MDF and RMS-values) indicated that there is imbalance between vasti muscles for more elevated heels. The results are also quantified with statistical measures. The study findings suggest that there would be an increased likelihood of knee imbalance and fatigue with regular usage of high heel shoes (HHS) in women.
Published: 2017

24. A system for accelerometer-based gesture classification using artificial neural networks

Author: Stephenson, RM, Naik, GR, Chai, R, Stephenson, RM, Naik, GR, and Chai, R
Abstract: © 2017 IEEE. A great many people suffer from neurological movement disorders that render typical hardware interface devices ineffective. A need exists for a universal interface device that can be trained to accept a wide range of inputs across varying types and severities of movement disorders. In this regard, this paper details the design, testing and optimization of an accelerometer-based gesture identification system. A Bluetooth-enabled IMU mounted on the wrist provides hand motion trajectory information to a local terminal. Several techniques are applied to decrease the intra-class variance and reduce classifier complexity including filtering, segmentation and temporal scaling. Datasets consisted of 520 training samples, 260 validation samples and a further 520 testing samples. A multi-layer feed forward artificial neural network (ML-FFNN) was used to classify the input space into 26 different classes. Initial system accuracy, using arbitrary hyperparameters was 77.69% with final optimized accuracy at 99.42%.
Published: 2017

25. Driver Fatigue Classification with Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System

Author: Chai, R, Naik, GR, Nguyen, TN, Ling, SH, Tran, Y, Craig, A, Nguyen, HT, Chai, R, Naik, GR, Nguyen, TN, Ling, SH, Tran, Y, Craig, A, and Nguyen, HT
Abstract: © 2016 IEEE. This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.
Published: 2017

26. Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

Author: Chai, R, Naik, GR, Ling, SH, Tran, Y, Craig, A, Nguyen, HT, Chai, R, Naik, GR, Ling, SH, Tran, Y, Craig, A, and Nguyen, HT
Abstract: © 2017 IEEE. This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.
Published: 2017

27. Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

Author: Viola Stella Mary, MC, Rajsingh, EB, and Naik, GR
Subjects: genetic structures, eye diseases
Abstract: © 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease.
Published: 2016

28. Shape memory alloy smart knee spacer to enhance knee functionality: Model design and finite element analysis

Author: Gautam, A, Rani, AB, Callejas, MA, Acharyya, SG, Acharyya, A, Biswas, D, Bhandari, V, Sharma, P, Naik, GR, Gautam, A, Rani, AB, Callejas, MA, Acharyya, SG, Acharyya, A, Biswas, D, Bhandari, V, Sharma, P, and Naik, GR
Abstract: © 2016 IEEE. In this paper we introduce Shape Memory Alloy (SMA) for designing the tibial part of Total Knee Arthroplasty (TKA) by exploiting the shape-memory and pseudo-elasticity property of the SMA (e.g. NiTi). This would eliminate the drawbacks of the state-of-the art PMMA based knee-spacer including fracture, sustainability, dislocation, tilting, translation and subluxation for tackling the Osteoarthritis especially for the aged people of 45-plus or the athletes. In this paper a Computer Aided Design (CAD) model using SolidWorks for the knee-spacer is presented based on the proposed SMA adopting the state-of-the art industry-standard geometry that is used in the PMMA based spacer design. Subsequently Ansys based Finite Element Analysis is carried out to measure and compare the performance between the proposed SMA based model with the state-of-the art PMMA ones. 81% more bending is noticed in the PMMA based spacer compared to the proposed SMA that would eventually cause fracture and tilting or translation of spacer. Permanent shape deformation of approximately 58.75% in PMMA based spacer is observed compared to recoverable 11% deformation in SMA when same load is applied on both separately.
Published: 2016

29. Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering

Author: Naik, GR, Al-Timemy, AH, Nguyen, HT, Naik, GR, Al-Timemy, AH, and Nguyen, HT
Abstract: © 2001-2011 IEEE. Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy (> 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications.
Published: 2016

30. Wavelet PCA for automatic identification of walking with and without an exoskeleton on a treadmill using pressure and accelerometer sensors

Author: Naik, GR, Pendharkar, G, Nguyen, HT, Naik, GR, Pendharkar, G, and Nguyen, HT
Abstract: © 2016 IEEE. Nowadays portable devices with more number of sensors are used for gait assessment and monitoring for elderly and disabled. However, the problem with using multiple sensors is that if they are placed on the same platform or base, there could be cross talk between them, which could change the signal amplitude or add noise to the signal. Hence, this study uses wavelet PCA as a signal processing technique to separate the original sensor signal from the signal obtained from the sensors through the integrated unit to compare the two types of walking (with and without an exoskeleton). This comparison using wavelet PCA will enable the researchers to obtain accurate sensor data and compare and analyze the data in order to further improve the design of compact portable devices used to monitor and assess the gait in stroke or paralyzed subjects. The advantage of designing such systems is that they can also be used to assess and monitor the gait of the stroke subjects at home, which will save them time and efforts to visit the laboratory or clinic.
Published: 2016

31. Reconfigurable hardware-software codesign methodology for protein identification

Author: Gudur, VY, Thallada, S, Deevi, AR, Gande, VK, Acharyya, A, Bhandari, V, Sharma, P, Khursheed, S, Naik, GR, Gudur, VY, Thallada, S, Deevi, AR, Gande, VK, Acharyya, A, Bhandari, V, Sharma, P, Khursheed, S, and Naik, GR
Abstract: © 2016 IEEE. In this paper we propose an on-the-fly reconfigurable hardware-software codesign based reconfigurable solution for real-time protein identification. Reconfigurable string matching is performed in the disciplines of protein identification and biomarkers discovery. With the generation of plethora of sequenced data and number of biomarkers for several diseases, it is becoming necessary to have an accelerated processing and on-the-fly reconfigurable system design methodology to bring flexibility to its usage in the medical science community without the need of changing the entire hardware every time with the advent of new biomarker or protein. The proteome database of human at UniProtKB (Proteome ID up000005640) comprising of 42132 canonical and isoform proteins with variable database-size are used for testing the proposed design and the performance of the proposed system has been found to compare favorably with the state-of-the-art approaches with the additional advantage of real-time reconfigurability.
Published: 2016

32. Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network

Author: Chai, R, Tran, Y, Naik, GR, Nguyen, TN, Ling, SH, Craig, A, Nguyen, HT, Chai, R, Tran, Y, Naik, GR, Nguyen, TN, Ling, SH, Craig, A, and Nguyen, HT
Abstract: © 2016 IEEE. This paper presents an electroencephalography (EEG) based-classification of between pre- and post-mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76% Also based on data from the 6 PCs, during eye closed, the classification between pre- and post-task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.
Published: 2016

33. Selecting optimal EEG channels for mental tasks classification: An approach using ICA

Author: Chai, R, Naik, GR, Nguyen, TN, Ling, SH, Tran, Y, Nguyen, HT, Chai, R, Naik, GR, Nguyen, TN, Ling, SH, Tran, Y, and Nguyen, HT
Abstract: © 2016 IEEE. This paper presents a systematic method to select optimal electroencephalography (EEG) channels for three mental tasks-based brain-computer interface (BCI) classification. A blind source separation (BSS) technique based on independent component analysis (ICA) with its back-projecting of the scalp map was used for selecting the optimal EEG channels. The three mental tasks included: mental letter composing, mental arithmetic and mental Rubik's cube rolling. Based on a power spectral density (PSD), the features of the two-channel EEG data were extracted, and then were classified by Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map showed that the prominent channels could be selected for dominant features from original six EEG channels (C3, C4, P3, P4, O1, O2) to four dominant channels (P3, O1, C4, O2) with the best two EEG channels selection at O1&C4. Two channel combinations classification yielded to the best two EEG channels of O1&C4 with an accuracy of 76.4%, followed by P3&O2 with an accuracy of 74.5%; P3&C4 with an accuracy of 71.9% and O1&O2 with an accuracy of 70%.
Published: 2016

34. Principal Component Analysis Applied to Surface Electromyography: A Comprehensive Review

Author: Naik, GR, Selvan, SE, Gobbo, M, Acharyya, A, Nguyen, HT, Naik, GR, Selvan, SE, Gobbo, M, Acharyya, A, and Nguyen, HT
Abstract: © 2016 IEEE. Surface electromyography (sEMG) records muscle activities from the surface of muscles, which offers a wealth of information concerning muscle activation patterns in both research and clinical settings. A key principle underlying sEMG analyses is the decomposition of the signal into a number of motor unit action potentials (MUAPs) that capture most of the relevant features embedded in a low-dimensional space. Toward this, the principal component analysis (PCA) has extensively been sought after, whereby the original sEMG data are translated into low-dimensional MUAP components with a reduced level of redundancy. The objective of this paper is to disseminate the role of PCA in conjunction with the quantitative sEMG analyses. Following the preliminaries on the sEMG methodology and a statement of PCA algorithm, an exhaustive collection of PCA applications related to sEMG data is in order. Alongside the technical challenges associated with the PCA-based sEMG processing, the envisaged research trend is also discussed.
Published: 2016

35. Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders

Author: Naik, GR, Selvan, SE, Nguyen, HT, Naik, GR, Selvan, SE, and Nguyen, HT
Abstract: © 2001-2011 IEEE. An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.
Published: 2016

36. A dynamic channel selection algorithm for the classification of EEG and EMG data

Author: Al-Ani, A, Koprinska, I, Naik, GR, Khushaba, RN, Al-Ani, A, Koprinska, I, Naik, GR, and Khushaba, RN
Abstract: © 2016 IEEE. The multichannel nature of EEG and EMG data poses a big challenge to the development of automatic EEG/EMG analysis and classification systems. Due to the 'curse of dimensionality' problem, the analysis and classification of several channels may not lead to the desired performance. Accordingly, a number of algorithms have been proposed to identify small 'static' subsets of channels that are capable of differentiating between samples of different classes. However, the identification of small subsets of relevant channels may not always be possible, where for certain applications the smaller the number of channels the less chance that sufficient information is provided. We propose in this paper a dynamic channel selection algorithm that identifies a channel (or a subset of channels) for each time segment of the signal that is relevant to the class of that particular time segment. To achieve this, we embraced the dynamic classifier selection methodology, and particularly the multiple classifier behaviour approach. Each EEG/EMG channel can be chosen to represent a certain unseen time segment of the signal based on the performance, or local accuracy, of its nearest neighbours in the set of training time segments. Results obtained using EEG data of a four-class alertness state classification problem reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. The algorithm also produced very encouraging results when used to classify EMG data collected from nine transradial amputees performing six classes of movements.
Published: 2016

37. Dependence independence measure for posterior and anterior EMG sensors used in simple and complex finger flexion movements: Evaluation using SDICA

Author: Naik, GR, Baker, KG, Nguyen, HT, Naik, GR, Baker, KG, and Nguyen, HT
Abstract: © 2015 IEEE. Identification of simple and complex finger flexion movements using surface electromyography (sEMG) and a muscle activation strategy is necessary to control human-computer interfaces such as prosthesis and orthoses. In order to identify these movements, sEMG sensors are placed on both anterior and posterior muscle compartments of the forearm. In general, the accuracy of myoelectric classification depends on several factors, which include number of sensors, features extraction methods, and classification algorithms. Myoelectric classification using a minimum number of sensors and optimal electrode configuration is always a challenging task. Sometimes, using several sensors including high density electrodes will not guarantee high classification accuracy. In this research, we investigated the dependence and independence nature of anterior and posterior muscles during simple and complex finger flexion movements. The outcome of this research shows that posterior parts of the hand muscles are dependent and hence responsible for most of simple finger flexion. On the other hand, this study shows that anterior muscles are responsible for most complex finger flexion. This also indicates that simple finger flexion can be identified using sEMG sensors connected only on anterior muscles (making posterior placement either independent or redundant), and vice versa is true for complex actions which can be easily identified using sEMG sensors on posterior muscles. The result of this study is beneficial for optimal electrode configuration and design of prosthetics and other related devices using a minimum number of sensors.
Published: 2015

38. Multiscale PCA to distinguish regular and irregular surfaces using tri axial head and trunk acceleration signals

Author: Pendharkar, G, Naik, GR, Acharyya, A, Nguyen, HT, Pendharkar, G, Naik, GR, Acharyya, A, and Nguyen, HT
Abstract: © 2015 IEEE. This study uses multiscale principal component analysis (MSPCA) signal processing technique in order to distinguish the two different surfaces, tiled (regular) and cobbled (irregular) using accelerometry data (recorded from MTx sensors). Two MTx sensors were placed on the head and trunk of the subject while the subject walked freely over the regular and irregular surfaces during a free walk. 3D acceleration signals, vertical, medio lateral (ML) and anterior-posterior (AP) were recorded for the head and trunk segments and compared for the free walk on a defined route. The magnitude of the ML and AP acceleration obtained from the MTx sensors (for both head & trunk) was higher when walking over the irregular (cobbled) surface as compared to the regular (tiled) surface. The accelerometry data was initially analysed using MSPCA and was later classified using naïve Bayesian classifier with >86% accuracy. This research study demonstrates that MSPCA can be used to distinguish the regular and irregular surfaces. The proposed method could be very useful as an automated method for classification of the two surfaces.
Published: 2015

39. Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis

Author: Naik, GR, Nguyen, HT, Naik, GR, and Nguyen, HT
Abstract: © 2014 IEEE. Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis.
Published: 2015

40. Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

Author: Chai, R, Naik, GR, Tran, Y, Ling, SH, Craig, A, Nguyen, HT, Chai, R, Naik, GR, Tran, Y, Ling, SH, Craig, A, and Nguyen, HT
Abstract: © 2015 IEEE. An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).
Published: 2015

41. Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification

Author: Guo, Y, Naik, GR, Huang, S, Abraham, A, Nguyen, HT, Guo, Y, Naik, GR, Huang, S, Abraham, A, and Nguyen, HT
Abstract: © 2015 Elsevier B.V. All rights reserved. Surface Electromyography (sEMG) is a non-invasive, easy to record signal of superficial muscles from the skin surface. The sEMG is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. Considering the nonlinear and non-stationary characteristics of sEMG, hand gesture recognition using sEMG signals necessitate designers to use Maximal Lyapunov Exponent (MLE) or ensemble Empirical Mode Decomposition (EMD) based MLEs. In this research, we propose a hand gesture recognition method of sEMG based on nonlinear multiscale MLE. The aim is to increase the classification accuracy of sEMG features while reducing the complexity of EMD. The nonlinear MLE features are classified using Flexible Neural Tree (FNT), which can solve highly structured dependent problems of the Artificial Neural Network (ANN). The testing has been conducted using several experiments with five participants. The classification performance of nonlinear multiscale MLE method is compared with MLE and EMD-based MLE through simulations. Experimental results demonstrate that the former algorithm outperforms the two latter algorithms and can classify six different hand gestures up to 97.6% accuracy.
Published: 2015

42. Subtle electromyographic pattern recognition for finger movements: A Pilot study using BSS techniques

Author: Naik, GR and Kumar, DK
Abstract: In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax). © 2012 World Scientific Publishing Company.
Published: 2012

43. Measure of increase in motor unit synchronisaton for young and old using sEMG

Author: Naik, GR, Kumar, DK, and Arjunan, S
Abstract: The motor-unit synchronization is impacted by the motor cortex projection to the spinal motor-neurons via the lateral corticospinal tract which may be affected by age. This paper reports an investigation of the age-related changes to the motor unit synchronization. Experiments were conducted on rested subjects during which they contracted their biceps against a fixed load and two channel surface electromyogram was recorded. The signal was analysed to determine the level of synchronization between multiple channels. The results indicate that there is age related reduction in the independence between multiple channels of sEMG, which implies there is a significant change in synchronization in old age groups as compared to the young age range. © 2012 IEEE.
Published: 2012

44. Investigation of age and gender related changes in force of isometric contraction, muscle endurance and muscle strength among young and old healthy people

Author: Arjunan, SP, Kumar, DK, Bastos, T, and Naik, GR
Abstract: This study has investigated the effect of age and gender on the muscle endurance, muscle strength and force of contraction among 30 moderately active urban Australian adults. Experiments were conducted with healthy people falling under two age groups (Young: 20-29 years and Old: 60-69 years). The endurance time, force at maximum voluntary contraction and variability in the steadiness of the force were computed from the measured force while performing sustained isometric contraction. It has experimentally determined that there small decrease in the endurance period for old people but there was a significant change (p < 0.05) with gender. The results show that there was no significant difference in maximum voluntary force of contraction (normalized to body mass index) between young and old. The results also indicate that there is an increase in the force variability among the older cohort compared with the younger cohort indicating that there loss of steadiness in force as age increases (p < 0.01) across all levels of force of contraction. The findings of this study can be used for the assessment and planning of rehabilitation regimes for people based on their age and gender. © 2012 IEEE.
Published: 2012

45. Evaluation of higher order statistics parameters for multi channel sEMG using different force levels

Author: Naik, GR and Kumar, DK
Subjects: body regions, Electromyography, Humans, Action Potentials, Muscle Contraction
Abstract: The electromyograpy (EMG) signal provides information about the performance of muscles and nerves. The shape of the muscle signal and motor unit action potential (MUAP) varies due to the movement of the position of the electrode or due to changes in contraction level. This research deals with evaluating the non-Gaussianity in Surface Electromyogram signal (sEMG) using higher order statistics (HOS) parameters. To achieve this, experiments were conducted for four different finger and wrist actions at different levels of Maximum Voluntary Contractions (MVCs). Our experimental analysis shows that at constant force and for non-fatiguing contractions, probability density functions (PDF) of sEMG signals were non-Gaussian. For lesser MVCs (below 30% of MVC) PDF measures tends to be Gaussian process. The above measures were verified by computing the Kurtosis values for different MVCs. © 2011 IEEE.
Published: 2011

46. Estimation of independent and dependent components of non-invasive EMG using fast ICA: Validation in recognising complex gestures

Author: Naik, GR and Kumar, DK
Subjects: Adult, Male, Principal Component Analysis, Gestures, Electromyography, Movement, Biomedical Engineering, Reproducibility of Results, Sensitivity and Specificity, Pattern Recognition, Automated, Data Interpretation, Statistical, Arm, Humans, Female, Muscle, Skeletal, Algorithms, Muscle Contraction
Abstract: The identification of a number of active muscles during complex actions is the useful information to identify different gestures. Biosignals such as surface electromyogram (sEMG) are a result of the summation of electrical activity of a number of sources. The complexity of the anatomy and actions makes it difficult in identifying the number of active sources from the multiple channel recordings. This paper addresses two applications of independent component analysis (ICA) on sEMG: the first one is to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The second application is to identify complex hand gestures using decomposed sEMG. The theoretical analysis and experimental results demonstrate that the ICA is suitable for the separation of myoelectric signals. The results identify the usage of ICA for identifying complex gestures. © 2011 Taylor & Francis.
Published: 2011

47. Improving the quality of the audio sources using Gaussianity reduction technique

Author: Naik, GR and Kumar, DK
Subjects: Electrical & Electronic Engineering
Abstract: This research has developed a novel technique that is based on the fundamental property of background and foreground signals. Background signals are a result of the inferential summation of large number of sources, while the foreground signals are a result of limited number of sources. This makes the statistical properties of the signal very different. Using negative entropy, this article demonstrates that it is possible to obtain the foreground signals from the mixture of foreground and background signals. The technique is based on mixing the noisy recording with a similar known signal and separating the signals using negative entropy based independent component analysis (ICA). The results indicate that the technique is successful in significantly improving the quality of the audio signals. © 2011 Taylor & Francis.
Published: 2011

48. Towards classification of low-level finger movements using forearm muscle activation: A comparative study based on ICA and Fractal theory

Author: Naik, GR, Kumar, DK, and Arjunan, SP
Abstract: There are number of possible rehabilitation applications of surface Electromyogram (sEMG) that are currently unreliable, when the level of muscle contraction is low. This paper has experimentally analysed the features of forearm sEMG based on Independent Component Analysis (ICA) and Fractal Dimension (FD) for identification of low-level finger movements. To reduce inter-experimental variations, the normalised feature values were used as the training and testing vectors to artificial neural network. The identification accuracy using raw sEMG and FD of sEMG was 51% and 58%, respectively. The accuracy increased to 96% when the signals are separated to their independent components using ICA. © 2011 Inderscience Enterprises Ltd.
Published: 2011

49. Dimensional reduction using Blind source separation for identifying sources

Author: Naik, GR and Kumar, DK
Subjects: Industrial Engineering & Automation
Abstract: Separation of independent sources using Blind Source Separation (BSS) techniques requires prior knowledge of the number of independent sources. Performing BSS when the number of recordings is greater than the number of sources can give erroneous results. Techniques employed to estimate suitable recordings from all the recordings require estimation of number of sources or require repeated iterations. This paper demonstrates that normalised determinant of the global matrix is a measure of the number of independent sources, K, in a mixture of M recordings. This paper also shows that performing ICA on K out of M randomly selected recordings gives good quality of separation. The qualities of the outcome of this experiment were measured using Signal to Interference Ratio (SIR) and Signal to Noise Ratio (SNR). The results demonstrate that using this technique, there is an improvement in the quality of separation as measured using SIR and SNRs. ICIC International © 2011.
Published: 2011

50. Source separation techniques for optimal electrode configurations: A study on electromyography

Author: Naik, GR, Kumar, DK, Weghorn, H, and Arjunan, SP
Subjects: Biomedical Engineering
Abstract: Surface electromyography (sEMG) is used to study underlying muscle activity. However, due to anatomical complexity, there is cross-talk, and the recorded sEMG corresponds to the mix of activities of different muscles. To overcome this, studies have attempted the use of blind source separation (BSS) and also the use of arrays of electrodes to identify activity of individual muscles. However, it is difficult to determine the most appropriate set of electrodes due to the dependency between the various channels. This paper describes a method by which it is possible to identify the dependency between the various channels, and thus the most appropriate set of channels can be identified. This study has investigated different configurations of electrodes for accurate identification of different hand gestures required for human-computer interface application.
Published: 2010

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