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Objective: The aim of this study is to develop an automated classificationalgorithm for polysomnography (PSG) recordings to detect non-apneic andnon-hypopneic arousals. Our particular focus is on detecting the respiratoryeffort-related arousals (RERAs) which are very subtle respiratory events thatdo not meet the criteria for apnea or hypopnea, and are more challenging todetect. Methods: The proposed algorithm is based on a bidirectional longshort-term memory (BiLSTM) classifier and 465 multi-domain features, extractedfrom multimodal clinical time series. The features consist of a set ofphysiology-inspired features (n = 75), obtained by multiple steps of featureselection and expert analysis, and a set of physiology-agnostic features (n =390), derived from scattering transform. Results: The proposed algorithm isvalidated on the 2018 PhysioNet challenge dataset. The overall performance interms of the area under the precision-recall curve (AUPRC) is 0.50 on thehidden test dataset. This result is tied for the second-best score during thefollow-up and official phases of the 2018 PhysioNet challenge. Conclusions: Theresults demonstrate that it is possible to automatically detect subtlenon-apneic/non-hypopneic arousal events from PSG recordings. Significance:Automatic detection of subtle respiratory events such as RERAs together withother non-apneic/non-hypopneic arousals will allow detailed annotations oflarge PSG databases. This contributes to a better retrospective analysis ofsleep data, which may also improve the quality of treatment.

Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network