  Objective: The aim of this study is to develop an automated classification
algorithm for polysomnography (PSG) recordings to detect non-apneic and
non-hypopneic arousals. Our particular focus is on detecting the respiratory
effort-related arousals (RERAs) which are very subtle respiratory events that
do not meet the criteria for apnea or hypopnea, and are more challenging to
detect. Methods: The proposed algorithm is based on a bidirectional long
short-term memory (BiLSTM) classifier and 465 multi-domain features, extracted
from multimodal clinical time series. The features consist of a set of
physiology-inspired features (n = 75), obtained by multiple steps of feature
selection and expert analysis, and a set of physiology-agnostic features (n =
390), derived from scattering transform. Results: The proposed algorithm is
validated on the 2018 PhysioNet challenge dataset. The overall performance in
terms of the area under the precision-recall curve (AUPRC) is 0.50 on the
hidden test dataset. This result is tied for the second-best score during the
follow-up and official phases of the 2018 PhysioNet challenge. Conclusions: The
results demonstrate that it is possible to automatically detect subtle
non-apneic/non-hypopneic arousal events from PSG recordings. Significance:
Automatic detection of subtle respiratory events such as RERAs together with
other non-apneic/non-hypopneic arousals will allow detailed annotations of
large PSG databases. This contributes to a better retrospective analysis of
sleep data, which may also improve the quality of treatment.
