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3. Poster session 2: Thursday 4 December 2014, 08:30-12:30 * Location: Poster area

Author

Domingos, JS, Augustine, DX, Leeson, P, Noble, JA, Doan, H-L, Boubrit, L, Cheikh-Khalifa, R, Laveau, F, Djebbar, M, Pousset, F, Isnard, R, Hammoudi, N, Lisi, M, Cameli, M, Di Tommaso, C, Curci, V, Reccia, R, Maccherini, M, Henein, M Y, Mondillo, S, Leitman, M, Vered, Z, Rashid, H, Yalcin, M U, Gurses, K M, Kocyigit, D, Evranos, B, Yorgun, H, Sahiner, L, Kaya, B, Aytemir, K, Ozer, N, Bertella, E, Petulla', M, Baggiano, A, Mushtaq, S, Russo, E, Gripari, P, Innocenti, E, Andreini, D, Tondo, C, Pontone, G, Necas, J, Kovalova, S, Hristova, K, Shiue, I, Bogdanva, V, Teixido Tura, G, Sanchez, V, Rodriguez-Palomares, J, Gutierrez, L, Gonzalez-Alujas, T, Garcia-Dorado, D, Forteza, A, Evangelista, A, Timoteo, A T, Aguiar Rosa, S, Cruz Ferreira, R, Campbell, R, Carrick, D, Mccombe, C, Tzemos, N, Berry, C, Sonecki, P, Noda, M, Setoguchi, M, Ikenouchi, T, Nakamura, T, Yamamoto, Y, Murakami, T, Katou, Y, Usui, M, Ichikawa, K, Isobe, M, Kwon, BJ, Roh, JW, Kim, HY, Ihm, SH, Barron, A J, Francis, DP, Mayet, J, Wensel, R, Kosiuk, J, Dinov, B, Bollmann, A, Hindricks, G, Breithardt, OA, Rio, P, Moura Branco, L, Galrinho, A, Cacela, D, Pinto Teixeira, P, Afonso Nogueira, M, Pereira-Da-Silva, T, Abreu, J, Teresa Timoteo, A, Cruz Ferreira, R, Pavlyukova, EN, Tereshenkova, EK, Karpov, RS, Piatkowski, R, Kochanowski, J, Opolski, G, Barbier, P, Mirea, O, Guglielmo, M, Savioli, G, Cefalu, C, Pudil, R, Horakova, L, Rozloznik, M, Balestra, C, P37/03, PRVOUK, Rimbas, RC, Enescu, OA, Calin, S, Vinereanu, D, POSDRU/159/1.5/S/141531, Grant, Karsenty, C, Hascoet, S, Hadeed, K, Semet, F, Dulac, Y, Alacoque, X, Leobon, B, Acar, P, Dharma, S, Sukmawan, R, Soesanto, AM, Vebiona, KPP, Firdaus, I, Danny, SS, Driessen, M M P, Sieswerda, GTJ, Post, MC, Snijder, RJ, Van Dijk, APJ, Leiner, T, Meijboom, FJ, Chrysohoou, C, Tsitsinakis, G, Tsiachris, D, Aggelis, A, Herouvim, E, Vogiatzis, I, Pitsavos, C, Koulouris, G, Stefanadis, C, Erdei, T, Edwards, J, Braim, D, Yousef, Z, Fraser, AG, Cardiff, Investigators, MEDIA, Avenatti, E, Magnino, C, Omede', P, Presutti, D, Moretti, C, Iannaccone, A, Ravera, A, Gaita, F, Milan, A, Veglio, F, Barbier, P, Scali, MC, Simioniuc, A, Guglielmo, M, Savioli, G, Cefalu, C, Mirea, O, Fusini, L, Dini, F, Okura, H, Murata, E, Kataoka, T, Mikaelpoor, A, Ojaghi Haghighi, SH, Ojaghi Haghighi, SZ, Alizadeasl, A, Sharifi-Zarchi, A, Zaroui, A, Ben Halima, M, Mourali, MS, Mechmeche, R, Rodriguez Palomares, J F, Gutierrez, LG, Maldonado, GM, Garcia, GG, Otaegui, IO, Garcia Del Blanco, BGB, Teixido, GT, Gonzalez Alujas, MTGA, Evangelista, AE, Garcia Dorado, DGD, Godinho, A R, Correia, AS, Rangel, I, Rocha, A, Rodrigues, J, Araujo, V, Almeida, PB, Macedo, F, Maciel, MJ, Rekik, B, Mghaieth, F, Aloui, H, Boudiche, S, Jomaa, M, Ayari, J, Tabebi, N, Farhati, A, Mourali, S, Dekleva, M, Markovic-Nikolic, N, Zivkovic, M, Stankovic, A, Boljevic, D, Korac, N, Beleslin, B, Arandjelovic, A, Ostojic, M, Galli, E, Guirette, Y, Auffret, V, Daudin, M, Fournet, M, Mabo, P, Donal, E, Chin, C W L, Luo, E, Hwan, J, White, A, Newby, D, Dweck, M, Carstensen, H G, Larsen, L H, Hassager, C, Kofoed, K F, Jensen, J S, Mogelvang, R, Kowalczyk, M, Debska, M, Kolesnik, A, Dangel, J, Kawalec, W, Migliore, RA, Adaniya, ME, Barranco, MA, Miramont, G, Gonzalez, S, Tamagusuku, H, Davidsen, E S, Kuiper, K K J, Matre, K, Gerdts, E, Igual Munoz, B, Maceira Gonzalez, AMG, Erdociain Perales, MEP, Estornell Erill, JEE, Valera Martinez, FVM, Miro Palau, VMP, Piquer Gil, MPG, Sepulveda Sanchez, PSS, Cervera Zamora, ACZ, Montero Argudo, AMA, Placido, R, Silva Marques, J, Magalhaes, A, Guimaraes, T, Nobre E Menezes, M, Goncalves, S, Ramalho, A, Robalo Martins, S, Almeida, AG, Nunes Diogo, A, Abid, L, Ben Kahla, S, Charfeddine, S, Abid, D, Kammoun, S, Tounsi, A, Abid, LEILA, Abid, DORRA, Charfeddine, SALMA, Hammami, RANIA, Triki, FETEN, Akrout, MALEK, Mallek, SOUAD, Hentati, MOURAD, Kammoun, SAMIR, Sirbu, C F, Berrebi, A, Huber, A, Folliguet, T, Yang, L-T, Shih, JY, Liu, YW, Li, YH, Tsai, LM, Luo, CY, Tsai, WC, Babukov, R, Bartosh, F, Bazilev, V, Muraru, D, Cavalli, G, Addetia, K, Miglioranza, MH, Veronesi, F, Mihaila, S, Tadic, M, Cucchini, U, Badano, L, Lang, RM, Miyazaki, S, Slavich, M, Miyazaki, T, Figini, F, Lativ, A, Chieffo, A, Montrfano, M, Alfieri, O, Colombo, A, Agricola, E, Liu, D, Hu, K, Herrmann, S, Stoerk, S, Kramer, B, Ertl, G, Bijnens, B, Weidemann, F, Brand, M, Butz, T, Tzikas, S, Van Bracht, M, Roeing, J, Wennemann, R, Christ, M, Grett, M, Trappe, H-J, Scherzer, S, Geroldinger, AG, Krenn, L, Roth, C, Gangl, C, Maurer, G, Rosenhek, R, Neunteufl, T, Binder, T, Bergler-Klein, J, Martins, E, Pinho, T, Leite, S, Azevedo, O, Belo, A, Campelo, M, Amorim, S, Rocha-Goncalves, F, Goncalves, L, Silva-Cardoso, J, Ahn, HS, Kim, KT, Jeon, HK, Youn, HJ, Haland, T, Saberniak, J, Leren, IS, Edvardsen, T, Haugaa, KH, Ziolkowska, L, Boruc, A, Kowalczyk, M, Turska-Kmiec, A, Zubrzycka, M, Kawalec, W, Monivas Palomero, V, Mingo Santos, S, Goirigolzarri Artaza, J, Rodriguez Gonzalez, E, Rivero Arribas, B, Castro Urda, V, Dominguez Rodriguez, F, Mitroi, C, Gracia Lunar, I, Fernadez Lozano, I, Palecek, T, Masek, M, Kuchynka, P, Fikrle, M, Spicka, I, Rysava, R, Linhart, A, Saberniak, J, Hasselberg, NE, Leren, IS, Haland, T, Borgquist, R, Platonov, PG, Edvardsen, T, Haugaa, KH, Ancona, R, Comenale Pinto, S, Caso, P, Coopola, MG, Arenga, F, Rapisarda, O, D'onofrio, A, Sellitto, V, Calabro, R, Rosca, M, Popescu, BA, Calin, A, Mateescu, A, Beladan, CC, Jalba, M, Rusu, E, Zilisteanu, D, Ginghina, C, Pressman, G, Cepeda-Valery, B, Romero-Corral, A, Moldovan, R, Saenz, A, Orban, M, Samuel, SP, Fijalkowski, M, Fijalkowska, M, Gilis-Siek, N, Blaut, K, Galaska, R, Sworczak, K, Gruchala, M, Fijalkowski, M, Nowak, R, Gilis-Siek, N, Fijalkowska, M, Galaska, R, Gruchala, M, Ikonomidis, I, Triantafyllidi, H, Trivilou, P, Tzortzis, S, Papadopoulos, C, Pavlidis, G, Paraskevaidis, I, Lekakis, J, Padiyath, A, Li, L, Xiao, Y, Danford, DA, Kutty, S, Kaymaz, C, Aktemur, T, Poci, N, Ozturk, S, Akbal, O, Yilmaz, F, Tokgoz Demircan, HC, Kirca, N, Tanboga, IH, Ozdemir, N, Investigators, EUPHRATES, Greiner, S, Jud, A, Aurich, M, Hess, A, Hilbel, T, Hardt, S, Katus, HA, D'ascenzi, F, Cameli, M, Alvino, F, Lisi, M, Focardi, M, Solari, M, Bonifazi, M, Mondillo, S, Konopka, M, Krol, W, Klusiewicz, A, Burkhard, K, Chwalbinska, J, Pokrywka, A, Dluzniewski, M, Braksator, W, King, G J, Coen, K, Gannon, S, Fahy, N, Kindler, H, Clarke, J, Iliuta, L, Rac-Albu, M, Placido, R, Robalo Martins, S, Guimaraes, T, Nobre E Menezes, M, Cortez-Dias, N, Francisco, A, Silva, G, Goncalves, S, Almeida, AG, Nunes Diogo, A, Kyu, K, Kong, WKF, Songco, GG, Galupo, MJ, Castro, MD, Shin Hnin, W, Ronald Lee, CH, Poh, KK, Milazzo, V, Di Stefano, C, Tosello, F, Leone, D, Ravera, A, Sabia, L, Sobrero, G, Maule, S, Veglio, F, Milan, A, Jamiel, A M, Ahmed, A M, Farah, I, Al-Mallah, M H, Petroni, R, Magnano, R, Bencivenga, S, Di Mauro, M, Petroni, S, Altorio, SF, Romano, S, Penco, M, Kumor, M, Lipczynska, M, Klisiewicz, A, Wojcik, A, Konka, M, Kozuch, K, Szymanski, P, Hoffman, P, Rimbas, RC, Rimbas, M, Enescu, OA, Mihaila, S, Calin, S, Vinereanu, D, 112/2011, Grant CNCSIS, 159/1.5/S/141531, Grant POSDRU, Donal, E, Reynaud, A, Lund, LH, Persson, H, Hage, C, Oger, E, Linde, C, Daubert, JC, investigators, KaRen, Maria Oliveira Lima, M, Costa, H, Gomes Da Silva, M, Noman Alencar, MC, Carmo Pereira Nunes, M, Costa Rocha, MO, Abid, L, Charfeddine, S, Ben Kahla, S, Abid, D, Siala, A, Hentati, M, Kammoun, S, Kovalova, S, Necas, J, Ozawa, K, Funabashi, N, Takaoka, H, Kobayashi, Y, Matsumura, Y, Wada, M, Hirakawa, D, Yasuoka, Y, Morimoto, N, Takeuchi, H, Kitaoka, H, Sugiura, T, Lakkas, L, Naka, KK, Ntounousi, E, Gkirdis, I, Koutlas, V, Bechlioulis, A, Pappas, K, Katsouras, CS, Siamopoulos, K, Michalis, LK, Naka, KK, Evangelou, D, Kalaitzidis, R, Bechlioulis, A, Lakkas, L, Gkirdis, I, Tzeltzes, G, Nakas, G, Katsouras, CS, Michalis, LK, Generati, G, Bandera, F, Pellegrino, M, Labate, V, Alfonzetti, E, Guazzi, M, Zagatina, A, Zhuravskaya, N, Al-Mallah, M, Alsaileek, A, Qureshi, W, Karsenty, C, Hascoet, S, Peyre, M, Hadeed, K, Alacoque, X, Amadieu, R, Leobon, B, Dulac, Y, Acar, P, Yamanaka, Y, Sotomi, Y, Iwakura, K, Inoue, K, Toyoshima, Y, Tanaka, K, Oka, T, Tanaka, N, Orihara, Y, Fujii, K, Soulat-Dufour, L, Lang, S, Boyer-Chatenet, L, Van Der Vynckt, C, Ederhy, S, Adavane, S, Haddour, N, Boccara, F, Cohen, A, Huitema, MP, Boerman, S, Vorselaars, VMM, Grutters, JC, Post, MC, Gopal, A S, Saha, SK, Toole, RS, Kiotsekoglou, A, Cao, JJ, Reichek, N, Meyer, C G, Altiok, E, Al Ateah, G, Lehrke, M, Becker, M, Lotfi, S, Autschbach, R, Marx, N, Hoffmann, R, Frick, M, Nemes, A, Sepp, R, Kalapos, A, Domsik, P, Forster, T, Caro Codon, J, Blazquez Bermejo, Z, Lopez Fernandez, T, Valbuena Lopez, S C, Iniesta Manjavacas, A M, De Torres Alba, F, Dominguez Melcon, F, Pena Conde, L, Moreno Yanguela, M, Lopez-Sendon, J L, Nemes, A, Lengyel, C, Domsik, P, Kalapos, A, Orosz, A, Varkonyi, TT, Forster, T, Rendon, J, Saldarriaga, C I, Duarte, N, Nemes, A, Domsik, P, Kalapos, A, Forster, T, Nemes, A, Domsik, P, Kalapos, A, Sepp, R, Foldeak, D, Borbenyi, Z, Forster, T, Hamdy, AM, Fereig, HM, Nabih, MA, Abdel-Aziz, A, Ali, AA, Broyd, CJ, Wielandts, J-Y, De Buck, S, Michielsen, K, Louw, R, Garweg, C, Nuyts, J, Ector, J, Maes, F, Heidbuchel, H, Gillis, K, Bala, G, Tierens, S, Cosyns, B, Maurovich-Horvat, P, Horvath, T, Jermendy, A, Celeng, C, Panajotu, A, Bartykowszki, A, Karolyi, M, Tarnoki, AD, Jermendy, G, and Merkely, B

Abstract

Purpose: 3D echocardiography (3DE) enables fast 3D acquisition but subsequent manual navigation to find 2D diagnostic planes can be time consuming. We have developed and validated an automated machine learning-based technique to find apical 2-, 3- and 4-chamber (A2C, A3C, A4C) views that enables fast volume navigation and analysis. Methods: 3DE volumes were acquired (Philips iE33: X3-1 and X5-1 probes) from 30 healthy volunteers and 36 clinical patients with suspected valve disease and coronary heart disease. 66 end diastolic volumes were used to assess the accuracy of apical standard view finding by our method against manual plane finding. To do this, dedicated software was developed with a machine learning approach and a 3-fold cross validation of results was performed. Results: Automatic A4C view detection was possible in 60/66 (91%) of volumes; detection failures were due to suboptimal myocardium wall integrity or lack of right ventricle in the scan. A2C and A3C views were extracted from the A4C view using the known geometrical relationships between apical standard views (A2C to A3C: 30°~40° and A2C to A4C: 90° of rotation over the left ventricle long axis, as shown in the Figure). In average, our method accurately found the heart apex and mitral valve centre with a 7.1 ± 5.7 mm and 7.2 ± 5.3 mm error, respectively. Conclusions: In order to automate clinical workflow, we have developed a new and fully automatic machine learning strategy for apical standard view finding which performed well (91% detection accuracy) on volunteer and clinical 3D echocardiograms. Figure

Published
2014
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4. Oral Abstract session: New insights in ventricular function: Friday 5 December 2014, 14:00-15:30 * Location: Agora

Author

Guglielmo, M, Cefalu', C, Savioli, G, Mirea, O, Fusini, L, Scali, MC, Simioniuc, A, Dini, F, Barbier, P, Hasselberg, NE, Haugaa, KH, Bernard-Brunet, A, Kongsgaard, E, Donal, E, Edvardsen, T, Mada, RO, Lysyansky, P, Winter, S, Fehske, W, Stankovic, I, Voigt, JU, Domingos, JS, Boardman, H, Leeson, P, Noble, JA, Kou, S, Caballero, L, Henri, C, Dulgheru, R, Magne, J, Daimon, M, Watanabe, H, Ito, H, Yoshikawa, J, Lancellotti, P, Brunet Bernard, A, Donal, E, Leclercq, C, Schnell, F, Fournet, M, Reynaud, A, Thebault, C, Mabo, P, Daubert, JC, Hernandez, A, Park, J, Naksuk, N, Thongprayoon, C, Gaba, P, Sharma, S, Rosenbaum, A, Hu, T, Kapa, S, Bruce, C, Asirvatham, S, Kosmala, W, Rojek, A, Karolko, B, Mysiak, A, and Przewlocka-Kosmala, M

Abstract

Purpose. We previously re-validated noninvasive estimation of pulmonary wedge pressure (PWP) measuring the CW pulmonary valve regurgitation end-diastolic pressure gradient (PWPecho). Using the latter as surrogate of PWP, we sought to test accuracy of left ventricular (LV) filling pressures estimation by the EAE guidelines algorithm (EAEalg) in a large non-selected population. Methods. We studied 1019 patients in sinus rhythm with GE Vivid7/9 systems (age: 10-93 y.; EF%: 13-83%, normal, n= 827 and reduced <50%, n= 192), in whom PWPecho could be measured (feasibility 75%), with normal pulmonary vascular resistances (WU< 2). The EAEalg combined E/e' (average), left atrial volume (LAV), E/A, Edec, pulmonary venous systolic fraction (SF), and echo-derived pulmonary systolic pressure (PSPe) to obtain 3 groups: normal, high PWP and not classifiable. These were compared to the PWPecho estimate. Results: Feasibility was high for all variables (E/E' 90%, LAV 93%, E/A 95%, Edec 90%, SF 91%, PSPe 92%), and for the EAEAlg (94%). Using the EAEAlg, 17% (n=137) of patients with normal in contrast to 10% (n=19) of patients with EF<50% were not classifiable, in the former secondary to the combination of a E/E'= 9-13 range, and LAV≥ 34ml/m2. In the remaining (classified, 84%) patients, utility of EAEalg even when limited to patients with EF<50% was still hampered by a low positive predictive value (PPV) (Table). Further, when only E/e' was tested in the same patients at ROC analysis (cutoff= 15; AUC=0.72, CI:0.6-0.8), accuracy was still impaired by a low PPV (53%), albeit a fair negative predictive value (NPV) (79%). Correlation between PWPecho and E/e' was modest even in patients with EF<50% (r=0.4, p<0.001), and at multiple regression analysis, E/e' was independently determined by age and mitral regurgitation in all patients, and by LV end-diastolic volume in EF<50% (r= 0.7, p<0.001) and by LV mass index in EF>50% (r= 0.64, p<.001). Conclusions. Noninvasive estimation of PWP by EAE guidelines is limited by a low PPV in both patients with and without reduced LV EF. In this setting, utility of the E/e' is limited, it being influenced by patient age, preload and LV mass. 1 SensitivitySpecificityPPVNPVEF≥50%72%78%18%98%EF<50%71%80%65%84%

Published
2014
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5. Automated Myocardial Wall Motion Classification using Handcrafted Features vs a Deep CNN-based mapping.

Author

Omar HA, Patra A, Domingos JS, Leeson P, and Noblel AJ

Subjects
Deep Learning, Information Storage and Retrieval, Magnetic Resonance Imaging, Neural Networks, Computer, Tomography, X-Ray Computed, Myocardium
Abstract

Compared to other modalities such as computed tomography or magnetic resonance imaging, the appearance of ultrasound images is highly dependent on the expertise of the sonographer or clinician making the image acquisition, as well as the machine used, making it a challenge to analyze due to the frequent presence of artefacts, missing boundaries, attenuation, shadows, and speckle. In addition, manual contouring of the epicardial and endocardial walls exhibits large inconsistencies and variations as it is strongly dependent on the sonographer's training and expertise. Hence, in this paper we propose a fully automated image analysis framework to ultimately perform wall motion abnormality classification in 2D+T images. We explore both traditional Random Forests classification with handcrafted features and spatio-temporal hierarchical aggregation of information with a deep learning CNN-based approach. Regarding the later classifier, we also investigate the effect of local phase information retrieval through the use of Feature Asymmetry (FA), and demonstrate that pre-processing videos with FA enables the spatio-temporal CNN to better discover relevant left ventricle endocardial abstractions from low-level features to high-level representations automatically.

Published
2018
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6. Improving Visual Detection of Wall Motion Abnormality with Echocardiographic Image Enhancing Methods.

Author

Omar HA, Domingos JS, Patra A, Leeson P, and Noble JA

Subjects
Heart, Humans, Coronary Artery Disease, Echocardiography, Myocardial Ischemia
Abstract

Analysis of wall motion abnormality using echocardiography is an established method for detecting myocardial ischemia. We describe a hybrid approach of enhancing 2D+T echo datasets with border detection and Eulerian motion magnification to improve the visual assessment of wall motion. We implemented a local phase-based approach using the monogenic signal and its derived features, either feature asymmetry (FA) or oriented feature symmetry (OFS), to detect boundaries of the heart structure. We enhanced the 2D+T datasets using either an intensity-based or phase-based Eulerian Motion Magnification (EMM) video processing technique, and identified among eight different types of enhancements the best performing method as OFS with an accuracy of 78% versus the original B-Mode with an accuracy of 71%.

Published
2018
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7. Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.

Author

Bernard O, Bosch JG, Heyde B, Alessandrini M, Barbosa D, Camarasu-Pop S, Cervenansky F, Valette S, Mirea O, Bernier M, Jodoin PM, Domingos JS, Stebbing RV, Keraudren K, Oktay O, Caballero J, Shi W, Rueckert D, Milletari F, Ahmadi SA, Smistad E, Lindseth F, van Stralen M, Wang C, Smedby O, Donal E, Monaghan M, Papachristidis A, Geleijnse ML, Galli E, and D'hooge J

Subjects
Humans, Algorithms, Echocardiography, Three-Dimensional methods, Heart Ventricles diagnostic imaging, Image Processing, Computer-Assisted methods
Abstract

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.

Published
2016
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8. Respiratory rate estimation from the oscillometric waveform obtained from a non-invasive cuff-based blood pressure device.

Author

Pimentel MA, Santos MD, Arteta C, Domingos JS, Maraci MA, and Clifford GD

Subjects
Adult, Blood Pressure Monitors, Electrocardiography, Female, Healthy Volunteers, Humans, Male, Oscillometry instrumentation, Oscillometry standards, Photoplethysmography, Reference Values, Oscillometry methods, Respiratory Rate physiology
Abstract

The presence of respiratory activity in the electrocardiogram (ECG), the pulse oximeter's photoplethysmo-graphic and continuous arterial blood pressure signals is a well-documented phenomenon. In this paper, we demonstrate that such information is also present in the oscillometric signal acquired from automatic non-invasive blood pressure monitors, and may be used to estimate the vital sign respiratory rate (RR). We propose a novel method that combines the information from the two respiratory-induced variations (frequency and amplitude) via frequency analysis to both estimate RR and eliminate estimations considered to be unreliable because of poor signal quality. The method was evaluated using data acquired from 40 subjects containing ECG, respiration and blood pressure waveforms, the latter acquired using an in-house built blood pressure device that is able to connect to a mobile phone. Results demonstrated a good RR estimation accuracy of our method when compared to the reference values extracted from the reference respiration waveforms (mean absolute error of 2.69 breaths/min), which is comparable to existing methods in the literature that extract RR from other physiological signals. The proposed method has been implemented in Java on the Android device for use in an mHealth platform.

Published
2014
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9. A review of current sleep screening applications for smartphones.

Author

Behar J, Roebuck A, Domingos JS, Gederi E, and Clifford GD

Subjects
Actigraphy, Humans, Social Control, Formal, Surveys and Questionnaires, Cell Phone legislation & jurisprudence, Polysomnography instrumentation, Polysomnography methods, Sleep physiology
Abstract

Sleep disorders are a common problem and contribute to a wide range of healthcare issues. The societal and financial costs of sleep disorders are enormous. Sleep-related disorders are often diagnosed with an overnight sleep test called a polysomnogram, or sleep study involving the measurement of brain activity through the electroencephalogram. Other parameters monitored include oxygen saturation, respiratory effort, cardiac activity (through the electrocardiogram), as well as video recording, sound and movement activity. Monitoring can be costly and removes the patients from their normal sleeping environment, preventing repeated unbiased studies. The recent increase in adoption of smartphones, with high quality on-board sensors has led to the proliferation of many sleep screening applications running on the phone. However, with the exception of simple questionnaires, no existing sleep-related application available for smartphones is based on scientific evidence. This paper reviews the existing smartphone applications landscape used in the field of sleep disorders and proposes possible advances to improve screening approaches.

Published
2013
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10. A comprehensive and suitable method for determining major ions from atmospheric particulate matter matrices.

Author

Domingos JS, Regis AC, Santos JV, de Andrade JB, and da Rocha GO

Subjects
Anions analysis, Carboxylic Acids chemistry, Limit of Detection, Linear Models, Reproducibility of Results, Air analysis, Chromatography, Ion Exchange methods, Particulate Matter analysis
Abstract

The present study proposes an analytical methodology that employs ion chromatography-conductivity detection for simultaneous quantification of inorganic (F(-), Cl(-), NO(3)(-), SO(4)(2-), and PO(3)(-)), monocarboxylate (HCOO(-), CH(3)COO(-), propionate, n-butyrate, lactate, and pyruvate), dicarboxylate (oxalate and succinate), and tricarboxylate anions (citrate), as well as crustal cations (Li(+), Na(+), K(+), NH(4)(+), Ca(2+), Mg(2+)) at low pgm(-3) range in airborne particle samples in one single run. The optimized conditions for anions were as follows: 0.6 mmol L(-1) KOH for 0-14 min, 0.6-15 mmol L(-1) KOH 14-20 min, 15-38 mmol L(-1) KOH during 20-32 min and finally returned to 0.6 mmol L(-1) for a period of 3 min, thereafter the eluent flow rate was 0.38 mL min(-1). Similarly, for cations, isocratic elution was adjusted to 0.36 mL min(-1) at 17.5 mmol L(-1) H(2)SO(4). LOD ranged 3.0-130 pgm(-3) and LOQ was within 10-400 pgm(-3) (Li(+) and PO(4)(3-), respectively) as well as recoveries ranged 89% (Ca(2+)) to 120% (Li(+)). Major ions were successfully determined in real PM1 and PM2.5 samples. The method used here was found to be a comprehensive, simple, cheap and reliable procedure for studying ions in particulate matter (PM) samples even those from remote areas or near ecosystem natural conditions., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Published
2012
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