Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings

被引:243
作者
Khandoker, Ahsan H. [1 ]
Palaniswami, Marimuthu [1 ]
Karmakar, Chandan K. [1 ]
机构
[1] Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3010, Australia
来源
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE | 2009年 / 13卷 / 01期
基金
澳大利亚研究理事会;
关键词
ECG-derived respiration (EDR); heart rate variability (HRV); obstructive sleep apnea; support vector machines (SVMs); wavelet; HEART-RATE-VARIABILITY; SPECTRAL-ANALYSIS; ELECTROCARDIOGRAM; CLASSIFICATION; FREQUENCY; IDENTIFICATION; SPECTROGRAM; ALGORITHMS; AGREEMENT; PRESSURE;
D O I
10.1109/TITB.2008.2004495
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:37 / 48
页数:12
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