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

John Malik, Yu Lun Lo, Hau Tieng Wu

Research output: Contribution to journalArticlepeer-review


Objective: 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. Approach: 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. Main results: On our private database of 27 participants, our accuracy, sensitivity, specificity, and values for predicting the wake stage are , 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. Significance: 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.

Original languageEnglish (US)
Article number085004
JournalPhysiological Measurement
Issue number8
StatePublished - Aug 20 2018


  • convolutional neural network
  • heart rate variability
  • instantaneous heart rate
  • sleep stage

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)


Dive into the research topics of 'Sleep-wake classification via quantifying heart rate variability by convolutional neural network'. Together they form a unique fingerprint.

Cite this