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In this paper, we propose a novel method and a practical approach topredicting early onsets of sleep syndromes, including restless leg syndrome,insomnia, based on an algorithm that is comprised of two modules. A FastFourier Transform is applied to 30 seconds long epochs of EEG recordings toprovide localized time-frequency information, and a deep convolutional LSTMneural network is trained for sleep stage classification. Automating sleepstages detection from EEG data offers great potential to tackling sleepirregularities on a daily basis. Thereby, a novel approach for sleep stageclassification is proposed which combines the best of signal processing andstatistics. In this study, we used the PhysioNet Sleep European Data Format(EDF) Database. The code evaluation showed impressive results, reaching anaccuracy of 86.43, precision of 77.76, recall of 93,32, F1-score of 89.12 withthe final mean false error loss of 0.09.

Sleep syndromes onset detection based on automatic sleep staging algorithm