Sleep syndromes onset detection based on automatic sleep staging algorithm
This work addresses sleep disorder prediction for patients, but it appears incremental as it combines existing signal processing and deep learning techniques.
The paper tackles early detection of sleep syndromes like restless leg syndrome and insomnia by developing an automatic sleep staging algorithm using EEG data, achieving an accuracy of 86.43% and an F1-score of 89.12.
In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, and a deep convolutional LSTM neural network is trained for sleep stage classification. Automating sleep stages detection from EEG data offers great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is proposed which combines the best of signal processing and statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching an accuracy of 86.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss of 0.09.