Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology
Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that...
| Main Authors: | , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2025
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| Online Access: | https://hdl.handle.net/1721.1/157956 |
| _version_ | 1824458293556281344 |
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| author | Nunes, Adonay S. Patterson, Matthew R. Gerstel, Dawid Khan, Sheraz Guo, Christine C. Neishabouri, Ali |
| author2 | McGovern Institute for Brain Research at MIT |
| author_facet | McGovern Institute for Brain Research at MIT Nunes, Adonay S. Patterson, Matthew R. Gerstel, Dawid Khan, Sheraz Guo, Christine C. Neishabouri, Ali |
| author_sort | Nunes, Adonay S. |
| collection | MIT |
| description | Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep–wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). Compared to commonly used sleep algorithms, this model resulted in the smallest error in wake after sleep onset (MAE of 48.7, Cole–Kripke of 86.2, Sadeh of 108.2, z-angle of 57.5) and sleep efficiency (MAE of 11.8, Cole–Kripke of 18.4, Sadeh of 23.3, z-angle of 9.3) outcomes. Despite being around for many years, accelerometer-alone devices continue to be useful due to their low cost, long battery life, and ease of use. Improving the accuracy and generalizability of sleep algorithms for accelerometer wrist devices is of utmost importance. We here demonstrated that domain adversarial convolutional neural networks can improve the overall accuracy, especially the specificity, of sleep–wake classification using wrist-worn accelerometer data, substantiating its use as a scalable and valid approach for sleep outcome assessment in real life. |
| first_indexed | 2025-02-19T04:23:35Z |
| format | Article |
| id | mit-1721.1/157956 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2025-02-19T04:23:35Z |
| publishDate | 2025 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1579562025-01-11T03:24:15Z Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology Nunes, Adonay S. Patterson, Matthew R. Gerstel, Dawid Khan, Sheraz Guo, Christine C. Neishabouri, Ali McGovern Institute for Brain Research at MIT Martinos Imaging Center (McGovern Institute for Brain Research at MIT) Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep–wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.1% (sensitivity 84% and specificity 58%). Compared to commonly used sleep algorithms, this model resulted in the smallest error in wake after sleep onset (MAE of 48.7, Cole–Kripke of 86.2, Sadeh of 108.2, z-angle of 57.5) and sleep efficiency (MAE of 11.8, Cole–Kripke of 18.4, Sadeh of 23.3, z-angle of 9.3) outcomes. Despite being around for many years, accelerometer-alone devices continue to be useful due to their low cost, long battery life, and ease of use. Improving the accuracy and generalizability of sleep algorithms for accelerometer wrist devices is of utmost importance. We here demonstrated that domain adversarial convolutional neural networks can improve the overall accuracy, especially the specificity, of sleep–wake classification using wrist-worn accelerometer data, substantiating its use as a scalable and valid approach for sleep outcome assessment in real life. 2025-01-10T21:24:16Z 2025-01-10T21:24:16Z 2024-12-14 2024-12-27T14:02:51Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/157956 Nunes, A.S.; Patterson, M.R.; Gerstel, D.; Khan, S.; Guo, C.C.; Neishabouri, A. Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology. Sensors 2024, 24, 7982. PUBLISHER_CC http://dx.doi.org/10.3390/s24247982 Sensors Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Nunes, Adonay S. Patterson, Matthew R. Gerstel, Dawid Khan, Sheraz Guo, Christine C. Neishabouri, Ali Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology |
| title | Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology |
| title_full | Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology |
| title_fullStr | Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology |
| title_full_unstemmed | Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology |
| title_short | Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology |
| title_sort | domain adversarial convolutional neural network improves the accuracy and generalizability of wearable sleep assessment technology |
| url | https://hdl.handle.net/1721.1/157956 |
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