Document Type : Original Research Paper
Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun , Iran
Department of Electrical Engineering, Kazerun, Islamic Azad University, Kazerun, Iran
Department of Biomedical Engineering, Kazerun, Islamic Azad University, Kazerun, Iran
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 signal that is less complex. The PPG signal has the least complexity, and in this article we used this signal for transitional learning. n this study, we extracted 52 features from two signals and prepared 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.