Sleep and Arousal Scoring for In-Home EEG Signals: A Multitask Learning Approach

Manual sleep and arousal scoring is a labor-intensive task that demands significant time and effort. To speed up this process, several automatic scoring models based on deep learning have been proposed. These models primarily focus on scoring PSG (Polysomnogram) signals by separately classifying sleep stages and arousal events. This study introduces a novel methodology for concurrent sleep stage classification and arousal scoring, employing multitask learning for the analysis of in-home EEG (Electroencephalogram) signals. Our approach led to improvements in overall precision and sensitivity of arousal scoring, with values increasing by 0.3% to 4%. Notably, this approach did not yield improvements in sleep scoring. We validated our methodology on two private datasets collected from in-home loT (internet of Things) EEG devices and achieved consistent outcomes. Collectively, our research underscores the benefits of multitask learning for arousal scoring in in-home EEG signals.