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 Paper › Research › peer-review

13 Citations (Scopus)

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
Title of host publication	2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Subtitle of host publication	4th IEEE International Conference on Biomedical and Health Informatics
Place of Publication	Piscataway, NJ
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	465-468
Number of pages	4
ISBN (Electronic)	9781509041794
DOIs	https://doi.org/10.1109/BHI.2017.7897306
Publication status	Published - 11 Apr 2017
Externally published	Yes
Event	IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2017 - Orlando, United States of America Duration: 16 Feb 2017 → 19 Feb 2017 Conference number: 4th https://ieeexplore.ieee.org/xpl/conhome/7890912/proceeding (Proceedings)

Conference

Conference	IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2017
Abbreviated title	BHI 2017
Country/Territory	United States of America
City	Orlando
Period	16/02/17 → 19/02/17
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7890912/proceeding (Proceedings)

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10.1109/BHI.2017.7897306

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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 & Health Informatics (BHI): 4th IEEE International Conference on Biomedical and Health Informatics (pp. 465-468). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BHI.2017.7897306

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title = "Multimodal ambulatory sleep detection",

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.}",

year = "2017",

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doi = "10.1109/BHI.2017.7897306",

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booktitle = "2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2017, BHI 2017 ; Conference date: 16-02-2017 Through 19-02-2017",

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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 & Health Informatics (BHI): 4th IEEE International Conference on Biomedical and Health Informatics. IEEE, Institute of Electrical and Electronics Engineers, Piscataway, NJ, pp. 465-468, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2017, Orlando, Florida, United States of America, 16/02/17. 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 & Health Informatics (BHI): 4th IEEE International Conference on Biomedical and Health Informatics. Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 465-468.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Multimodal ambulatory sleep detection

AU - Chen, Weixuan

AU - Sano, Akane

AU - Martinez, Daniel Lopez

AU - Taylor, Sara

AU - McHill, Andrew W.

AU - Phillips, Andrew J.K.

AU - Barger, Laura

AU - Klerman, Elizabeth B.

AU - Picard, Rosalind W.

N1 - Conference code: 4th

PY - 2017/4/11

Y1 - 2017/4/11

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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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 & Health Informatics (BHI): 4th IEEE International Conference on Biomedical and Health Informatics. Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 465-468 doi: 10.1109/BHI.2017.7897306

Multimodal ambulatory sleep detection

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