Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2

Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient's neurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, laborious process. Because of the limits of human sleep stage scoring, there is a greater need for creating Automatic Sleep Stage Classification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleep is an important step in assisting physicians in the diagnosis treatment of associated sleep disorders. In this research, we offer a unique method a practical strategy to predicting early onsets of sleep disorders, such as restless leg syndrome insomnia, using the Twin Convolutional Model FTC2, based on an algorithm composed of two modules. To provide localised time-frequency information, 30 second long epochs of EEG recordings are subjected to a Fast Fourier Transform, a deep convolutional LSTM neural network is trained for sleep stage categorization. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is pro- posed which combines the best of signal processing statistics. In this study, we used the PhysioNet Sleep European  Data Format (EDF) Database. The code evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.