期刊
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
卷 13, 期 1, 页码 37-48出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITB.2008.2004495
关键词
ECG-derived respiration (EDR); heart rate variability (HRV); obstructive sleep apnea; support vector machines (SVMs); wavelet
类别
资金
- Australian Research Council [LP0454378]
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 It 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 oil 42 subjects showed that it correctly recognized 24 out of 26 OSAS+ subjects and 15 out of 16 OSAS- subjects (accuracy = 92.8%; 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.
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