Modeling respiratory movement signals during central and obstructive sleep apnea events using electrocardiogram

Obstructive sleep apnea (OSA) causes a pause in airflow with continuing breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. CSA is recognized when respiratory effort falls below 15% of pre-event peak-to-peak amplitude of the respiratory effort. The aim of this study is to investigate whether a combination of respiratory sinus arrhythmia (RSA), ECG-derived respiration (EDR) from R-wave amplitudes and wavelet-based features of ECG signals during OSA and CSA can act as surrogate of changes in thoracic movement signal measured by respiratory inductance plethysmography (RIP). Therefore, RIP and ECG signals during 250 pre-scored OSA and 150 pre-scored CSA events, and 10 s preceding the events were collected from 17 patients. RSA, EDR, and wavelet decomposition of ECG signals at level 9 (0.15-0.32 Hz) were used as input to the support vector regression (SVR) model to recognize the RIP signals and classify OSA from CSA. Using cross-validation test, an optimal SVR (radial basis function kernel; C = 2(8) and ε = 2(-2) where C is the coefficient for trade-off between empirical and structural risk and ε is the width of ε-insensitive region) showed that it correctly recognized 243/250 OSA and 139/150 CSA events (95.5% detection accuracy). Independent test was performed on 80 OSA and 80 CSA events from 12 patients. The independent test accuracies of OSA and CSA detections were found to be 92.5 and 95.0%, respectively. Results suggest superior performance of SVR using ECG as the surrogate in recognizing the reduction of respiratory movement during OSA and CSA. Results also indicate that ECG-based SVR model could act as a potential surrogate signal of respiratory movement during sleep-disordered breathing.