Sleep stages classification based on deep transfer learning method using PPG signal
Publish place: Signal Processing and Renewable Energy، Vol: 5، Issue: 2
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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JR_SPRE-5-2_004
تاریخ نمایه سازی: 17 خرداد 1400
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 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.
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Authors
Mohammad Moradi
Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
Mohammad Fatehi
Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
Hassan Masoumi
Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
Mehdi Taghizadeh
Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran