Support vector machines using wavelet-based features of ECG correctly recognized obstructive sleep apnea syndrome with 92.85% accuracy (Cohen's kappa 0.85) in an independent test set.
Effect estimate: Cohen's kappa 0.85
Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique support vector machines (SVMs) for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS - ) and subjects with OSAS (OSAS +), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.
Khandoker et al. (Tue,) conducted a other in Obstructive sleep apnea syndrome (OSAS) (n=125). Support vector machines (SVMs) using wavelet-based features of ECG (HRV and EDR) vs. Clinical diagnosis (OSAS +/-) was evaluated on Automated recognition of OSAS types (accuracy) (Cohen's kappa 0.85). Support vector machines using wavelet-based features of ECG correctly recognized obstructive sleep apnea syndrome with 92.85% accuracy (Cohen's kappa 0.85) in an independent test set.