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

Signal Processing and Renewable Energy

مقاله 4، دوره 5، شماره 2، شهریور 2021، صفحه 53-60   

نویسندگان

Mohammad Mahdi Moradi¹؛ Mohammad H Fatehi^{* 2}؛ Hassan Masoumi¹؛ Mehdi Taghizadeh²

¹Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

²Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

چکیده

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.

کلیدواژه‌ها

PPG signal؛ sleep stages classification؛ deep transfer learning