Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.

ABSTRACT: Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19-82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compa

SUBMITTER: Morokuma S 

PROVIDER: S-EPMC10584883 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature