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3. Sleep Quality Prediction From Wearable Data Using Deep Learning

Author

Sathyanarayana, Aarti, Joty, Shafiq, Fernandez-Luque, Luis, Ofli, Ferda, Srivastava, Jaideep, Elmagarmid, Ahmed, Arora, Teresa, and Taheri, Shahrad

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. ObjectiveThe objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. MethodsActigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). ResultsDeep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). ConclusionsDeep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

Published
2016
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9. A global research priority agenda to advance public health responses to fatty liver disease

Author

Lazarus, Jeffrey V., Mark, Henry E., Allen, Alina M., Arab, Juan Pablo, Carrieri, Patrizia, Noureddin, Mazen, Alazawi, William, Alkhouri, Naim, Alqahtani, Saleh A., Arrese, Marco, Bataller, Ramon, Berg, Thomas, Brennan, Paul N., Burra, Patrizia, Castro-Narro, Graciela E., Cortez-Pinto, Helena, Cusi, Kenneth, Dedes, Nikos, Duseja, Ajay, Francque, Sven M., Hagström, Hannes, Huang, Terry T-K., Wajcman, Dana Ivancovsky, Kautz, Achim, Kopka, Christopher J., Krag, Aleksander, Miller, Veronica, Newsome, Philip N., Rinella, Mary E., Romero, Diana, Sarin, Shiv Kumar, Silva, Marcelo, Spearman, C. Wendy, Tsochatzis, Emmanuel A., Valenti, Luca, Villota-Rivas, Marcela, Zelber-Sagi, Shira, Schattenberg, Jörn M., Wong, Vincent Wai-Sun, Younossi, Zobair M., Aberg, Fredrik, Adams, Leon, Al-Naamani, Khalid, Albadawy, Reda M., Alexa, Zinaida, Allison, Michael, Alnaser, Faisal A., Alswat, Khalid, Alvares-da-Silva, Mario Reis, Alvaro, Domenico, Alves-Bezerra, Michele, Andrade, Raul J., Anstee, Quentin M., Awuku, Yaw Asante, Baatarkhuu, Oidov, Baffy, Gyorgy, Bakieva, Shokhista, Bansal, Meena B., Barouki, Robert, Batterham, Rachel L., Behling, Cynthia, Belfort-DeAguiar, Renata, Berzigotti, Annalisa, Betel, Michael, Bianco, Cristiana, Bosi, Emanuele, Boursier, Jerome, Brunt, Elizabeth M., Bugianesi, Elisabetta, Byrne, Christopher J., Cabrera Cabrejos, Maria Cecilia, Caldwell, Stephen, Carr, Rotonya, Castellanos Fernández, Marlen Ivón, Castera, Laurent, Castillo-López, Maria Gabriela, Caussy, Cyrielle, Cerda-Reyes, Eira, Ceriello, Antonio, Chan, Wah- Kheong, Chang, Yoosoo, Charatcharoenwitthaya, Phunchai, Chavez-Tapia, Norberto, Chung, Raymond T., Colombo, Massimo, Coppell, Kirsten, Cotrim, Helma P., Craxi, Antonio, Crespo, Javier, Dassanayake, Anuradha, Davidson, Nicholas O., De Knegt, Robert, de Ledinghen, Victor, Demir, Münevver, Desalegn, Hailemichael, Diago, Moises, Dillon, John F., Dimmig, Bruce, Dirac, M. Ashworth, Dirchwolf, Melisa, Dufour, Jean-François, Dvorak, Karel, Ekstedt, Mattias, El-Kassas, Mohamed, Elsanousi, Osama M., Elsharkawy, Ahmed M., Elwakil, Reda, Eskridge, Wayne, Eslam, Mohammed, Esmat, Gamal, Fan, Jian- Gao, Ferraz, Maria Lucia, Flisiak, Robert, Fortin, Davide, Fouad, Yasser, Freidman, Scott L., Fuchs, Michael, Gadano, Adrian, Gastaldelli, Amalia, Geerts, Anja, Geier, Andreas, George, Jacob, Gerber, Lynn H., Ghazinyan, Hasmik, Gheorghe, Liana, Kile, Denise Giangola, Girala, Marcos, Boon Bee, George Goh, Goossens, Nicolas, Graupera, Isabel, Grønbæk, Henning, Hamid, Saeed, Hebditch, Vanessa, Henry, Zachary, Hickman, Ingrid J., Hobbs, L. Ansley, Hocking, Samantha L., Hofmann, Wolf Peter, Idilman, Ramazan, Iruzubieta, Paula, Isaacs, Scott, Isakov, Vasily A., Ismail, Mona H., Jamal, Mohammad H., Jarvis, Helen, Jepsen, Peter, Jornayvaz, François, Sudhamshu, K.C., Kakizaki, Satoru, Karpen, Saul, Kawaguchi, Takumi, Keating, Shelley E., Khader, Yousef, Kim, Seung Up, Kim, Won, Kleiner, David E., Koek, Ger, Joseph Komas, Narcisse Patrice, Kondili, Loreta A., Koot, Bart G., Korenjak, Marko, Kotsiliti, Eleni, Koulla, Yiannoula, Kugelmas, Carina, Kugelmas, Marcelo, Labidi, Asma, Lange, Naomi F., Lavine, Joel E., Lazo, Mariana, Leite, Nathalie, Lin, Han-Chieh, Lkhagvaa, Undram, Long, Michelle T., Lopez-Jaramillo, Patricio, Lozano, Adelina, Macedo, Maria Paula, Malekzadeh, Reza, Marchesini, Giulio, Marciano, Sebastian, Martinez, Kim, Martínez Vázquez, Sophia E., Mateva, Lyudmila, Mato, José M., Nlombi, Charles Mbendi, McCary, Alexis Gorden, McIntyre, Jeff, McKee, Martin, Mendive, Juan M., Mikolasevic, Ivana, Miller, Pamela S., Milovanovic, Tamara, Milton, Terri, Moreno-Alcantar, Rosalba, Morgan, Timothy R., Motala, Ayesha, Muris, Jean, Musso, Carla, Nava-González, Edna J., Negro, Francesco, Nersesov, Alexander V., Neuschwander-Tetri, Brent A., Nikolova, Dafina, Norris, Suzanne, Novak, Katja, Ocama, Ponsiano, Ong, Janus P., Ong-Go, Arlinking, Onyekwere, Charles, Padilla, Martin, Pais, Raluca, Pan, Calvin, Panduro, Arturo, Panigrahi, Manas K., Papatheodoridis, Georgios, Paruk, Imran, Patel, Keyur, Gonçalves, Carlos Penha, Figueroa, Marlene Pérez, Pérez-Escobar, Juanita, Pericàs, Juan M., Perseghin, Gianluca, Pessoa, Mário Guimarães, Petta, Salvatore, Marques Souza de Oliveira, Claudia Pinto, Prabhakaran, Dorairaj, Pyrsopoulous, Nikolaos, Rabiee, Atoosa, Ramji, Alnoor, Ratziu, Vlad, Ravendhran, Natarajan, Ray, Katrina, Roden, Michael, Romeo, Stefano, Romero-Gómez, Manuel, Rotman, Yaron, Rouabhia, Samir, Rowe, Ian A., Sadirova, Shakhlo, Alkhatry, Maryam Salem, Salupere, Riina, Satapathy, Sanjaya K., Schwimmer, Jeffrey B., Sebastiani, Giada, Seim, Lynn, Seki, Yosuke, Serme, Abdel Karim, Shapiro, David, Sharvadze, Lali, Shaw, Jonathan E., Shawa, Isaac Thom, Shenoy, Thrivikrama, Shibolet, Oren, Shimakawa, Yusuke, Shubrook, Jay H., Singh, Shivaram Prasad, Sinkala, Edford, Skladany, Lubomir, Skrypnyk, Igor, Song, Myeong Jun, Sookoian, Silvia, Sridharan, Kannan, Stefan, Norbert, Stine, Jonathan G., Stratakis, Nikolaos, Sheriff, Dhastagir Sultan, Sundaram, Shikha S., Svegliati-Baroni, Gianluca, Swain, Mark G., Tacke, Frank, Taheri, Shahrad, Tan, Soek-Siam, Tapper, Elliot B., Targher, Giovanni, Tcaciuc, Eugen, Thiele, Maja, Tiniakos, Dina, Tolmane, Ieva, Torre, Aldo, Torres, Esther A., Treeprasertsuk, Sombat, Trenell, Michael, Turcan, Svetlana, Turcanu, Adela, Valantinas, Jonas, van Kleef, Laurens A., Velarde Ruiz Velasco, Jose Antonio, Vesterhus, Mette, Vilar-Gomez, Eduardo, Waked, Imam, Wattacheril, Julia, Wedemeyer, Heiner, Wilkins, Fonda, Willemse, José, Wong, Robert J., Yilmaz, Yusuf, Yki-Järvinen, Hannele, Yu, Ming-Lung, Yumuk, Volkan, Zeybel, Müjdat, Zheng, Kenneth I., Zheng, Ming-Hua, and Huang, Terry T.-K.

Published
2023
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11. Qatar Diabetes Mobile Application Trial (QDMAT): an open-label randomised controlled trial to examine the impact of using a mobile application to improve diabetes care in type 2 diabetes mellitus—a study protocol

Author

Suleiman, Noor, Alkasem, Meis, Al Amer, Zaina, Salameh, Obada, Al-Thani, Noora, Hamad, Mohammad Khair, Baagar, Khaled, Abdalhakam, Ibrahem, Othman, Manal, Dughmosh, Ragae, Al-Mohanadi, Dabia, Al Sanousi, Ali, Bashir, Mohammed, Chagoury, Odette, Taheri, Shahrad, and Abou-Samra, Abdul-Badi

Published
2022
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12. Metabolism

Author

Leong, Wen B., Taheri, Shahrad, Stone, Anna M., Thomas, G. Neil, Phillips, Anna C., Section editor, and Gellman, Marc D., editor

Published
2020
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13. Impact of Physical Activity on Sleep:A Deep Learning Based Exploration

Author

Sathyanarayana, Aarti, Joty, Shafiq, Fernandez-Luque, Luis, Ofli, Ferda, Srivastava, Jaideep, Elmagarmid, Ahmed, Taheri, Shahrad, and Arora, Teresa

Subjects
Computer Science - Learning
Abstract

The importance of sleep is paramount for maintaining physical, emotional and mental wellbeing. Though the relationship between sleep and physical activity is known to be important, it is not yet fully understood. The explosion in popularity of actigraphy and wearable devices, provides a unique opportunity to understand this relationship. Leveraging this information source requires new tools to be developed to facilitate data-driven research for sleep and activity patient-recommendations. In this paper we explore the use of deep learning to build sleep quality prediction models based on actigraphy data. We first use deep learning as a pure model building device by performing human activity recognition (HAR) on raw sensor data, and using deep learning to build sleep prediction models. We compare the deep learning models with those build using classical approaches, i.e. logistic regression, support vector machines, random forest and adaboost. Secondly, we employ the advantage of deep learning with its ability to handle high dimensional datasets. We explore several deep learning models on the raw wearable sensor output without performing HAR or any other feature extraction. Our results show that using a convolutional neural network on the raw wearables output improves the predictive value of sleep quality from physical activity, by an additional 8% compared to state-of-the-art non-deep learning approaches, which itself shows a 15% improvement over current practice. Moreover, utilizing deep learning on raw data eliminates the need for data pre-processing and simplifies the overall workflow to analyze actigraphy data for sleep and physical activity research.

Published
2016
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14. Robust Automated Human Activity Recognition and its Application to Sleep Research

Author

Sathyanarayana, Aarti, Ofli, Ferda, Fernandes-Luque, Luis, Srivastava, Jaideep, Elmagarmid, Ahmed, Arora, Teresa, and Taheri, Shahrad

Subjects
Computer Science - Learning
Abstract

Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour. Applying HAR to wearable sensors can provide new insights by enriching the feature set in health studies, and enhance the personalisation and effectiveness of health, wellness, and fitness applications. Wearable devices provide an unobtrusive platform for user monitoring, and due to their increasing market penetration, feel intrinsic to the wearer. The integration of these devices in daily life provide a unique opportunity for understanding human health and wellbeing. This is referred to as the "quantified self" movement. The analyses of complex health behaviours such as sleep, traditionally require a time-consuming manual interpretation by experts. This manual work is necessary due to the erratic periodicity and persistent noisiness of human behaviour. In this paper, we present a robust automated human activity recognition algorithm, which we call RAHAR. We test our algorithm in the application area of sleep research by providing a novel framework for evaluating sleep quality and examining the correlation between the aforementioned and an individual's physical activity. Our results improve the state-of-the-art procedure in sleep research by 15 percent for area under ROC and by 30 percent for F1 score on average. However, application of RAHAR is not limited to sleep analysis and can be used for understanding other health problems such as obesity, diabetes, and cardiac diseases.

Published
2016
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22. Prediction of Cardiometabolic Health Through Changes in Plasma Proteins With Intentional Weight Loss in the DiRECT and DIADEM-I Randomized Clinical Trials of Type 2 Diabetes Remission

Author

Sattar, Naveed, primary, Taheri, Shahrad, additional, Astling, David P., additional, Chadwick, Jessica, additional, Hinterberg, Michael A., additional, Holmes, Michael V., additional, Troth, Emma V., additional, Welsh, Paul, additional, Zaghloul, Hadeel, additional, Chagoury, Odette, additional, Lean, Mike, additional, Taylor, Roy, additional, and Williams, Steve, additional

Published
2023
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32. Metabolism

Author

Leong, Wen B., primary, Taheri, Shahrad, additional, Stone, Anna M., additional, and Thomas, G. Neil, additional

Published
2018
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35. A multiomic approach to examine the molecular signatures differentiating people with obesity alone from those with obesity and metabolic complications

Author

Mir, Fayaz, primary, Mall, Raghvendra, additional, Ullah, Ehsan, additional, Iskandarani, Ahmad, additional, Cyprian, Farhan, additional, Samra, Tareq A., additional, Alkasem, Meis, additional, Abdalhakam, Ibrahem, additional, Farooq, Faisal, additional, Taheri, Shahrad, additional, and Abou-Samra, Abdul-Badi, additional

Published
2023
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39. Additional file 1 of An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study

Author

Mir, Fayaz Ahmad, Mall, Raghvendra, Ullah, Ehsan, Iskandarani, Ahmad, Cyprian, Farhan, Samra, Tareq A., Alkasem, Meis, Abdalhakam, Ibrahem, Farooq, Faisal, Taheri, Shahrad, and Abou-Samra, Abdul-Badi

Abstract

Additional file 1: Figure S1. Flowchart highlighting different number of MiRNAs, PCGs, Metabolites analyzed in our dataset and steps taken to identify key MiRNAs, PCGs, Metabolites involved in significantly enriched metabolic pathways between OBM vs OBO. Figure S2. Correlation between clinical traits and enriched metabolic pathways. Here each block represents correlation between the enrichment profile of a metabolic pathway and a clinical characteristic based on their values across the 39 participant profiles. Here we only considered those pathways which were significantly enriched and those clinical traits which were highlighted in Fig. 3A. Figure S3. Gene expression of 25 identified genes (differentially expressed in the samples) across multiple tissue types in the GTEx portal. ( https://gtexportal.org/ ). The expression of these genes is higher across multiple tissue types and not biased towards blood tissue from which samples were collected.

Published
2023
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41. Metabolism

Author

Leong, Wen B., Taheri, Shahrad, Thomas, G. Neil, Gellman, Marc D., editor, and Turner, J. Rick, editor

Published
2013
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42. Sleep and Immunity in Older Age

Author

Hampson, Peter, Rossi, Alessandra, Arora, Teresa, Lord, Janet M., Taheri, Shahrad, Bosch, Jos A., editor, Phillips, Anna C., editor, and Lord, Janet M., editor

Published
2013
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45. Type 2 Diabetes Remission: A New Mission in Diabetes Care.

Author

Taheri, Shahrad

Abstract

The article focuses on the potential for Type 2 Diabetes Mellitus (T2DM) remission through weight loss interventions, challenging the traditional belief in the irreversibility of T2DM. Topics discussed include the efficacy of lifestyle interventions, metabolic/bariatric surgery, and the need for standardized criteria to define T2DM remission, emphasizing the importance of embracing remission as a key therapeutic goal in diabetes care.

Published
2024
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48. Diabetes Intervention Accentuating Diet and Enhancing Metabolism (DIADEM-I): a randomised controlled trial to examine the impact of an intensive lifestyle intervention consisting of a low-energy diet and physical activity on body weight and metabolism in early type 2 diabetes mellitus: study protocol for a randomized controlled trial

Author

Taheri, Shahrad, Chagoury, Odette, Zaghloul, Hadeel, Elhadad, Sara, Ahmed, Salma Hayder, Omar, Omar, Payra, Sherryl, Ahmed, Salma, El Khatib, Neda, Amona, Rasha Abou, El Nahas, Katie, Bolton, Matthew, Chaar, Henem, Suleiman, Noor, Jayyousi, Amin, Zirie, Mahmoud, Janahi, Ibrahim, Elhag, Wahiba, Alnaama, Abdulla, Zainel, Abduljaleel, Hassan, Dahlia, Cable, Tim, Charlson, Mary, Wells, Martin, Al-Hamaq, Abdulla, Al-Abdulla, Samya, and Abou-Samra, Abdul Badi

Published
2018
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50. Narcolepsy and Autoimmunity

Author

Taheri, Shahrad, Paterno, Jose, Lin, Ling, Mignot, Emmauel, Lydic, Ralph, editor, Baghdoyan, Helen A., editor, Nishino, Seiji, editor, and Sakurai, Takeshi, editor

Published
2005
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