A Biomathematical Model for Classifying Sleep Stages Using Deep Learning Techniques

Abstract

A biomathematical model is a framework that calculates corresponding indices based on biological and physiological parameters, and can be used to study the fatigue states of submarine crew members during long-duration operations. Submarine personnel are prone to fatigue and decreased vigilance, leading to unnecessary risks. Sleep quality plays a crucial role in assessing human vigilance; however, traditional biomathematical models generally categorize human sleep into two different pressure stages based on circadian rhythms. To accurately classify sleep stages based on physiological signals, this article proposes a novel deep learning architecture using single-channel EEG signals. This architecture comprises four modules: beginning with a feature preliminary extraction module employing a multiscale convolutional neural network (MSCNN), followed by a feature aggregation module combining reparameterizable large kernel network with temporal convolutions network (RepLKnet), then utilizing a multivariate weighted recurrent network as the tensor encoder (MWRN), and finally, decoding with a dynamic graph convolutional neural network (DGCNN). The output is provided by a final classifier. We assessed the effectiveness of the proposed model using two publicly available datasets. The results demonstrate that our model surpasses current leading benchmarks.