BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning

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, and 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 and is an important step in assisting physicians in the diagnosis and treatment of associated sleep disorders. In this research, we offer a unique method and a practical strategy to predict early onset of sleep disorders, such as restless leg syndrome and insomnia, using the twin convolutional model FTC2, based on an algorithm composed of two modules. To provide localized time-frequency information, 30-second-long epochs of electroencephalogram (EEG) recordings are subjected to a fast Fourier transform, and a deep convolutional long short-term networks neural network is trained for sleep stage categorization. Automating sleep stage detection from EEG data offers a great potential to tackle 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 90.43, precision of 77.76, recall of 93,32, F1 score of 89.12, and the final mean false error loss of 0.09. All the source code is available at https://github.com/timothy102/eeg