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

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 ۵۲ features from two signals and prepared them for the classification stage. This method includes two steps, (a) Train data PPG۱ and Test data PPG۲, (b) Train data PPG۲ and Test data PPG۱. Results proved that our method has acceptable reliability for classification. The accuracy of ۹۴.۲۶% and ۹۶.۴۹% has been reached.