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 proceedingConference contribution

8 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 languageEnglish (US)
Title of host publication2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages465-468
Number of pages4
ISBN (Electronic)9781509041794
DOIs
StatePublished - Apr 11 2017
Externally publishedYes
Event4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 - Orlando, United States
Duration: Feb 16 2017Feb 19 2017

Publication series

Name2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017

Conference

Conference4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
Country/TerritoryUnited States
CityOrlando
Period2/16/172/19/17

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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