Simultaneous Sleep Events Detection and Stages Classification Using a Multi-Task Deep-Learning Approach

Accurate detection of sleep stages and sleep events is essential for the effective diagnosis and treatment of sleep disorders. However, current state-of-the-art methods often fall short in integrating these multiple tasks simultaneously. This study introduces a novel multi-task deep-learning approach for the joint detection of sleep events and hypnogram construction in a single pass. The proposed method adapts state-of-the-art single-shot object detection techniques to multi-channel time-series data, enabling simultaneous classification and detection of sleeping events. Experiments were conducted using diverse input channel combinations, including electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), oxygen saturation, airflow, and thoracic-abdominal movements, as well as multiple output configurations regarding sleep stages, EEG arousals, and respiratory events, both individually and combined. Results demonstrate improved accuracy in detecting respiratory events and sleep stages with additional channels. The multi-task approach enhanced overall performance, benefiting from shared knowledge across tasks.