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

الموضوعات :

Mohammad Moradi ¹ , Mohammad Fatehi ² , Hassan Masoumi ³ , Mehdi Taghizadeh ⁴

1 - Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
2 - Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
3 - Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
4 - Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

تاريخ الإرسال : 08 الخميس , جمادى الثانية, 1442 تاريخ التأكيد : 26 الإثنين , جمادى الثانية, 1442 تاريخ الإصدار : 20 الثلاثاء , شوال, 1442

الکلمات المفتاحية: PPG signal, sleep stages classification, deep transfer learning,

ملخص المقالة :

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.