Non-Contact Sleep Stage Detection Using Canonical Correlation Analysis of Respiratory Sound

• Published in: IEEE journal of biomedical and health informatics 2020-02, Vol.24 (2), p.614-625
• Main Authors: Xue, Biao, Deng, Boya, Hong, Hong, Wang, Zhiyong, Zhu, Xiaohua, Feng, David Dagan
• Format: Article
• Language: English
• Subjects: Algorithms; Audio data; canonical correlation analysis; Classifiers; Correlation; Correlation analysis; Decision trees; Feature extraction; Frequency-domain analysis; Humans; machine learning; Medical treatment; Monitoring; nonlinear features; NREM sleep; Polysomnography - methods; Quality assessment; REM sleep; respiratory sound; Respiratory Sounds; Sleep; Sleep and wakefulness; Sleep apnea; Sleep deprivation; Sleep stage detection; Sleep Stages; Sound; Support Vector Machine; Support vector machines; Time-domain analysis
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2019.2910566

Respiratory sound is able to differentiate sleep stages and provide a non-contact and cost-effective solution for the diagnosis and treatment monitoring of sleep-related diseases. While most of the existing respiratory sound-based methods focus on a limited number of sleep stages such as sleep/wake and wake/rapid eye movement (REM)/non-REM, it is essential to detect sleep stages at a finer level for sleep quality evaluation. In this paper, we for the first time study a sleep stage detection method aiming at classifying sleep states into four sleep stages: wake, REM, light sleep, and deep sleep from the respiratory sound. In addition to extracting time-domain features, frequency-domain features of respiratory sound, non-linear features of snoring sound are devised to better characterize snoring-related signals of respiratory sound. To effectively fuse the three sets of features, a novel feature fusion technique combining the generalized canonical correlation analysis with the Relief-F algorithm is proposed for discriminative feature selection. Final stage detection is achieved with popular classifiers including decision tree, support vector machines, K-nearest neighbor, and the ensemble classifier. To evaluate our proposed method, we built an in-house dataset, which is comprised of 13 nights of sleep audio data from a sleep laboratory. Experimental results indicate that our proposed method outperforms the existing related ones and is promising for large-scale non-contact sleep monitoring.