Support vector regression model for assessing respiratory effort during central apnea events using ECG signals

The aim of the present study is to investigate whether wavelet based features of ECG signals during central sleep apnea (CSA) can act as surrogate of respiratory effort measured by respiratory inductance plethysmography (RIP). Therefore, RIP and ECG signals during 125 pre-scored CSA events and 10 seconds preceding the events were collected from 7 patients. Wavelet decompositions of ECG signals upto 10 levels were used as input to the support vector regression (SVR) model to recognize the drop in RIP signal amplitudes during CSA. Using 25-fold cross validation, an optimal showed that it correctly recognized 115 CSA events (92% detection accuracy) using a subset of selected combination of wavelet decomposition levels (level 9 and 10; 0.12-0.24 Hz) of ECG. Results suggest superior performance of SVR using ECG as the surrogate in recognizing the fall of respiratory effort during CSA.