Deep learning for next-generation sleep diagnostics

Currently, the diagnosis of sleep disorders relies on polysomnographic recordings with a time-consuming manual analysis with low reliability between different manual scorers. Throughout the night, sleep stages are identified manually in non-overlapping 30-second epochs starting from the onset of the recording based on electroencephalography (EEG), electro-oculography (EOG), and chin electromyography (EMG) signals which require meticulous placement of electrodes. Moreover, the diagnosis of many sleep disorders relies on outdated guidelines. When assessing the severity of obstructive sleep apnea (OSA), the patients are classified based on thresholds of the apnea-hypopnea index (AHI), i.e. the number of respiratory disruptions during sleep. These thresholds are not fully based on solid scientific evidence and remain the same across different measurement techniques.