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John Malik1, Yu-Lun Lo2, Hau-Tieng Wu1,3

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Summary
This summary is machine-generated.

We developed a convolutional neural network (CNN) to classify wake/sleep status using instantaneous heart rate (IHR) series from electrocardiograms (ECG). This non-invasive method effectively monitors physiological state changes.

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Area of Science:

  • Physiological monitoring
  • Machine learning in healthcare
  • Sleep science

Background:

  • Heart rate fluctuations are linked to physiological state changes.
  • Accurate wake/sleep status classification is crucial for health monitoring.

Purpose of the Study:

  • To classify human wake/sleep status using instantaneous heart rate (IHR) series.
  • To develop an effective and scalable method for physiological state recognition via non-invasive heart rate monitoring.

Main Methods:

  • A convolutional neural network (CNN) was employed to analyze IHR series derived from electrocardiograms (ECG).
  • The model predicts wake/sleep status every 30 seconds.
  • Validation was performed on private and public datasets, including comparisons using photoplethysmography (PPG).

Main Results:

  • The CNN achieved high accuracy (89.4% specificity, 52.4% sensitivity) in predicting wake stages on a private dataset.
  • Similar performance was observed on public datasets and when using PPG-derived IHR.
  • Robustness checks confirmed the reliability of the performance statistics.

Conclusions:

  • The CNN model effectively quantifies IHR fluctuations for differentiating wake and sleep stages.
  • This approach offers a scalable and non-invasive method for monitoring physiological state changes.