Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time–frequency analysis

• Published in: Computers in biology and medicine Vol. 120; p. 103769
• Main Authors: Tripathy, R.K., Gajbhiye, Pranjali, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2020; Elsevier Limited
• Subjects: Apnea; Automation; Bivariate analysis; Bivariate CP signal; Bivariate fast and adaptive EMD; Decomposition; EKG; Electrocardiography; Empirical analysis; Feature extraction; Frequency domain analysis; Heart rate; Internal Medicine; Other; Random forest; Respiration; Sensitivity; Sleep; Sleep apnea; Sleep disorders; Specificity; Spectrum analysis; Support vector machines; SVM; Time domain analysis; Time-frequency analysis; Sensitivity; Specificity; Bivariate CP signal; Sleep apnea; Random forest; SVM; Bivariate fast and adaptive EMD
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2020.103769

Sleep apnea is a sleep related pathology in which breathing or respiratory activity of an individual is obstructed, resulting in variations in the cardio-pulmonary (CP) activity. The monitoring of both cardiac (heart rate (HR)) and pulmonary (respiration rate (RR)) activities are important for the automated detection of this ailment. In this paper, we propose a novel automated approach for sleep apnea detection using the bivariate CP signal. The bivariate CP signal is formulated using both HR and RR signals extracted from the electrocardiogram (ECG) signal. The approach consists of three stages. First, the bivariate CP signal is decomposed into intrinsic mode functions (IMFs) and residuals for both HR and RR channels using bivariate fast and adaptive empirical mode decomposition (FAEMD). Second, the features are extracted using time-domain analysis, spectral analysis, and time–frequency domain analysis of IMFs from CP signal. The time–frequency domain features are computed from the cross time–frequency matrices of IMFs of CP signal. The cross time–frequency matrix of each IMF is evaluated using the Stockwell (S)-transform. Third, the support vector machine (SVM) and the random forest (RF) classifiers are used for automated detection of sleep apnea with the features from the bivariate CP signal. Our proposed approach has demonstrated an average sensitivity and specificity of 82.27% and 78.67%, respectively for sleep apnea detection using the 10-fold cross-validation method. The approach has yielded an average sensitivity and specificity of 73.19% and 73.13%, respectively for the subject-specific cross-validation. The performance of the approach was compared with other CPC features used for the detection of sleep apnea. •A new Approach for the detection of sleep apnea using Cardio-pulmonary signal is proposed.•The time-domain, frequency-domain, and time–frequency domain features are extracted.•A physiological measure such as the predicted apnea hypopnea index (AHI) is used.•The approach demonstrated a sensitivity and specificity values of 82.27% and 78.67% using 10-fold cross-validation.