Multimodal ambulatory sleep detection

Weixuan Chen; Akane Sano; Daniel Lopez Martinez; Sara Taylor; Andrew W. McHill; Andrew J.K. Phillips; Laura Barger; Elizabeth B. Klerman; Rosalind W. Picard

doi:10.1109/BHI.2017.7897306

Multimodal ambulatory sleep detection

Weixuan Chen, Akane Sano, Daniel Lopez Martinez, Sara Taylor, Andrew W. McHill, Andrew J.K. Phillips, Laura Barger, Elizabeth B. Klerman, Rosalind W. Picard

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent neural networks for sleep/wake classification, followed by cross-correlation-based template matching for sleep episode on/offset detection. The method achieved a sleep/wake classification accuracy of 96.5%, and sleep episode on/offset detection F1 scores of 0.85 and 0.82, respectively, with mean errors of 5.3 and 5.5 min, respectively, when compared with sleep/wake state and sleep episode on/offset assessed using actigraphy and sleep diaries.

Original language	English (US)
Title of host publication	2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	465-468
Number of pages	4
ISBN (Electronic)	9781509041794
DOIs	https://doi.org/10.1109/BHI.2017.7897306
State	Published - Apr 11 2017
Event	4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 - Orlando, United States Duration: Feb 16 2017 → Feb 19 2017

Publication series

Name	2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017

Conference

Conference	4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
Country/Territory	United States
City	Orlando
Period	2/16/17 → 2/19/17

ASJC Scopus subject areas

Health Informatics
Computer Science Applications
Biomedical Engineering

Access to Document

10.1109/BHI.2017.7897306

Cite this

Chen, W., Sano, A., Martinez, D. L., Taylor, S., McHill, A. W., Phillips, A. J. K., Barger, L., Klerman, E. B., & Picard, R. W. (2017). Multimodal ambulatory sleep detection. In 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 (pp. 465-468). Article 7897306 (2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2017.7897306

Multimodal ambulatory sleep detection. / Chen, Weixuan; Sano, Akane; Martinez, Daniel Lopez et al.
2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 465-468 7897306 (2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, W, Sano, A, Martinez, DL, Taylor, S, McHill, AW, Phillips, AJK, Barger, L, Klerman, EB & Picard, RW 2017, Multimodal ambulatory sleep detection. in 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017., 7897306, 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 465-468, 4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017, Orlando, United States, 2/16/17. https://doi.org/10.1109/BHI.2017.7897306

Chen W, Sano A, Martinez DL, Taylor S, McHill AW, Phillips AJK et al. Multimodal ambulatory sleep detection. In 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 465-468. 7897306. (2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017). doi: 10.1109/BHI.2017.7897306

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abstract = "Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent neural networks for sleep/wake classification, followed by cross-correlation-based template matching for sleep episode on/offset detection. The method achieved a sleep/wake classification accuracy of 96.5%, and sleep episode on/offset detection F1 scores of 0.85 and 0.82, respectively, with mean errors of 5.3 and 5.5 min, respectively, when compared with sleep/wake state and sleep episode on/offset assessed using actigraphy and sleep diaries.",

author = "Weixuan Chen and Akane Sano and Martinez, {Daniel Lopez} and Sara Taylor and McHill, {Andrew W.} and Phillips, {Andrew J.K.} and Laura Barger and Klerman, {Elizabeth B.} and Picard, {Rosalind W.}",

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AU - Sano, Akane

AU - Martinez, Daniel Lopez

AU - Taylor, Sara

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AU - Phillips, Andrew J.K.

AU - Barger, Laura

AU - Klerman, Elizabeth B.

AU - Picard, Rosalind W.

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N2 - Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent neural networks for sleep/wake classification, followed by cross-correlation-based template matching for sleep episode on/offset detection. The method achieved a sleep/wake classification accuracy of 96.5%, and sleep episode on/offset detection F1 scores of 0.85 and 0.82, respectively, with mean errors of 5.3 and 5.5 min, respectively, when compared with sleep/wake state and sleep episode on/offset assessed using actigraphy and sleep diaries.

AB - Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent neural networks for sleep/wake classification, followed by cross-correlation-based template matching for sleep episode on/offset detection. The method achieved a sleep/wake classification accuracy of 96.5%, and sleep episode on/offset detection F1 scores of 0.85 and 0.82, respectively, with mean errors of 5.3 and 5.5 min, respectively, when compared with sleep/wake state and sleep episode on/offset assessed using actigraphy and sleep diaries.

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Multimodal ambulatory sleep detection

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