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Sleep-wake classification via quantifying heart rate variability by convolutional neural network.

Publication ,  Journal Article
Malik, J; Lo, Y-L; Wu, H-T
Published in: Physiological measurement
August 2018

Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series.We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities.On our private database of 27 participants, our accuracy, sensitivity, specificity, and [Formula: see text] values for predicting the wake stage are [Formula: see text], 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics.This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.

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Published In

Physiological measurement

DOI

EISSN

1361-6579

ISSN

0967-3334

Publication Date

August 2018

Volume

39

Issue

8

Start / End Page

085004

Related Subject Headings

  • Wakefulness
  • Sleep
  • Signal Processing, Computer-Assisted
  • Neural Networks, Computer
  • Male
  • Humans
  • Heart Rate
  • Healthy Volunteers
  • Female
  • Electrocardiography
 

Citation

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Malik, J., Lo, Y.-L., & Wu, H.-T. (2018). Sleep-wake classification via quantifying heart rate variability by convolutional neural network. Physiological Measurement, 39(8), 085004. https://doi.org/10.1088/1361-6579/aad5a9
Malik, John, Yu-Lun Lo, and Hau-Tieng Wu. “Sleep-wake classification via quantifying heart rate variability by convolutional neural network.Physiological Measurement 39, no. 8 (August 2018): 085004. https://doi.org/10.1088/1361-6579/aad5a9.
Malik J, Lo Y-L, Wu H-T. Sleep-wake classification via quantifying heart rate variability by convolutional neural network. Physiological measurement. 2018 Aug;39(8):085004.
Malik, John, et al. “Sleep-wake classification via quantifying heart rate variability by convolutional neural network.Physiological Measurement, vol. 39, no. 8, Aug. 2018, p. 085004. Epmc, doi:10.1088/1361-6579/aad5a9.
Malik J, Lo Y-L, Wu H-T. Sleep-wake classification via quantifying heart rate variability by convolutional neural network. Physiological measurement. 2018 Aug;39(8):085004.
Journal cover image

Published In

Physiological measurement

DOI

EISSN

1361-6579

ISSN

0967-3334

Publication Date

August 2018

Volume

39

Issue

8

Start / End Page

085004

Related Subject Headings

  • Wakefulness
  • Sleep
  • Signal Processing, Computer-Assisted
  • Neural Networks, Computer
  • Male
  • Humans
  • Heart Rate
  • Healthy Volunteers
  • Female
  • Electrocardiography