Sleep stages classification based on deep transfer learning method using PPG signal

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  • Mohammad Moradi1,
  • Mohammad Fatehi*2,
  • Hassan Masoumi1,
  • Mehdi Taghizadeh2,
  1. Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
  2. Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

Revised: 2021-01-21

Accepted: 2021-02-08

Published in Issue 2021-06-01

How to Cite

Moradi, M., Fatehi, M., Masoumi, H., & Taghizadeh, M. (2021). Sleep stages classification based on deep transfer learning method using PPG signal. Signal Processing and Renewable Energy (SPRE), 5(2), 53-60. https://oiccpress.com/spre/article/view/7843

PDF views: 172

Abstract

Sleep stages classification using the signal analysis includes EEG, EOG, EMG, PPG, and ECG. In this study, the proposed method using transfer learning to sleep stages classification. First, we have used the two PPG signals for this method It is important to use a less complex signal. The PPG signal has the least complexity, and in this article, we used this signal for transitional learning. In this study, we extracted 52 features from two signals and prepared them for the classification stage. This method includes two steps, (a) Train data PPG1 and Test data PPG2, (b) Train data PPG2 and Test data PPG1. Results proved that our method has acceptable reliability for classification. The accuracy of 94.26% and 96.49% has been reached.
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