Findings in the Area of Sleep Disorders Reported from Xihua University (A Multimodal Attention-fusion Convolutional Neural Network for Automatic Detection of Sleep Disorders).

A study conducted at Xihua University in Chengdu, China, has developed a new method for detecting sleep disorders using a Multimodal Attention-Fusion Convolutional Neural Network. Traditional methods of sleep disorder detection, such as questionnaires and scale assessments, are subjective and time-consuming. The proposed network uses multiple signals, including electroencephalography, electrooculography, electrocardiography, and electromyography, to identify healthy individuals and five sleep disorders. The accuracy of the network on the Cyclic Alternating Pattern sleep dataset was found to be 99.56%, with higher performance compared to existing studies. This research provides a promising approach to the detection of sleep disorders. [Extracted from the article]