A multimodal attention-fusion convolutional neural network for automatic detection of sleep disorders

Sleep is essential for human physical and mental health. Sleep disorders are a significant threat to human health, and a large number of people in the world suffer from sleep disorders. Effective detection of sleep disorders is essential for the treatment of sleep disorders. Questionnaires and scale assessments are traditional methods of sleep disorder detection, which are subjective, time-consuming and prone to misdiagnosis. To detect sleep disorders quickly and accurately, a Multimodal Attention-Fusion Convolutional Neural Network is proposed in this paper. The network uses electroencephalography, electrooculography, electrocardiography, and electromyography signals to automatically identify healthy and five sleep disorders, namely insomnia, narcolepsy, periodic leg movement, rapid eye movement behaviour disorder and nocturnal frontal lobe epilepsy. First, multiple convolutional neural network branches are used to extract time-invariant features of multimodal signals. Then, a multi-scale attention module based on dilated convolutional networks and a squeeze and excite block is proposed for further extracting features with different scales and fusing feature information. Finally, a prediction module consisting of fully connected layers is used to detect sleep disorders. The accuracy, F1 score, and Kappa coefficient obtained on the Cyclic Alternating Pattern sleep dataset are 99.56%, 99.49% and 0.9942, respectively. Compared to the existing state-of-the-art studies, the method proposed in this paper has higher performance in sleep disorder detection.