Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings
Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings
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DOI：
10.1109/titb.2008.2004495
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发表时间：
2009-01-01
影响因子：
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通讯作者：
Karmakar, Chandan K.
中科院分区：
文献类型：
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作者：
Khandoker, Ahsan H.;Palaniswami, Marimuthu;Karmakar, Chandan K.
阻塞性睡眠呼吸暂停综合征（OSAS）与心血管疾病的发病率以及白天过度嗜睡和生活质量差有关。在这项研究中，我们应用机器学习技术[支持向量机（SVM）]从夜间ECG记录中自动识别OSAS类型。分析了从正常受试者（OSAS-）和OSAS受试者（OSAS+）获得的共125组夜间ECG记录，每组持续时间约为8 It。从连续小波系数水平提取的特征，小波分解后的信号，由于心率变异性（HRV）从RR间期和心电图衍生的呼吸（EDR）从R波的QRS幅度被用作输入到支持向量机识别OSAS+/-科目。使用留一法，83个训练集的分类的最大准确率被发现是100%的支持向量机使用的一个子集的HRV和EDR功能的选择组合。42名受试者的独立测试结果显示，它正确识别了26名OSAS+受试者中的24名和16名OSAS-受试者中的15名（准确率= 92.8%; Cohen's kappa.值为0.85）。为了估计OSAS的相对严重程度，计算SVM输出的后验概率，并与相应的呼吸暂停/低通气指数进行比较。这些结果表明，支持向量机在基于小波的ECG特征支持下的OSAS识别中具有上级性能。结果表明，在基于ECG的筛查设备中应用SVM具有相当大的潜力，可以帮助睡眠专家对疑似OSAS患者进行初步评估。
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.