Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
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DOI：
10.1016/j.knosys.2015.02.005
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发表时间：
2015-06-01
影响因子：
8.8
通讯作者：
Sankaranarayanan, Meena
中科院分区：
文献类型：
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作者：
Acharya, U. Rajendra;Sudarshan, Vidya K.;Sankaranarayanan, Meena
糖尿病 (DM) 是一种慢性终生疾病，其特点是血糖水平升高。由于糖尿病无法治愈，因此重点在于控制疾病。因此，DM的诊断和治疗具有重要意义。 DM 最常见的并发症包括视网膜病变、神经病变、肾病和心肌病。糖尿病会导致心血管自主神经病变，从而影响心率变异性 (HRV)。因此，在没有其他原因的情况下，HRV 分析可用于诊断糖尿病。目前的工作旨在开发一种自动化系统，通过使用从心电图（ECG）信号中提取的心率（HR）信息来对正常和糖尿病类别进行分类。 HRV 频谱分析可识别患有自主神经糖尿病神经病变的患者，并提供自主神经系统 (ANS) 损伤的早期诊断。通过使用离散小波变换 (DWT) 方法获得的 HRV 频谱指数观察到与受损 ANS 的显着相关性。这里，为了自动诊断和检测DM，我们进行了高达5级的DWT分解，并提取了DWT的各个详细系数级别的能量、样本熵、近似熵、峰度和偏度特征。我们已经提取了从 HR 信号中提取的 DWT 系数的第 5 级相对小波能量和熵特征。使用各种排序方法对这些特征进行排序，即 Bhattacharyya 空间算法、t 检验、Wilcoxon 检验、接收器操作曲线 (ROC) 和熵。然后将排序后的特征输入到不同的分类器中，包括决策树 (DT)、K 最近邻 (KNN)、朴素贝叶斯 (NBC) 和支持向量机 (SVM)。我们的结果显示，通过使用最少数量的特征，可以获得最大的诊断区分性能。通过我们的系统，通过使用 DT 分类器和十倍交叉验证，我们获得了 92.02% 的平均准确度、92.59% 的灵敏度和 91.46% 的特异性。 (C) 2015 Elsevier B.V. 保留所有权利。
Diabetes Mellitus (DM), a chronic lifelong condition, is characterized by increased blood sugar levels. As there is no cure for DM, the major focus lies on controlling the disease. Therefore, DM diagnosis and treatment is of great importance. The most common complications of DM include retinopathy, neuropathy, nephropathy and cardiomyopathy. Diabetes causes cardiovascular autonomic neuropathy that affects the Heart Rate Variability (HRV). Hence, in the absence of other causes, the HRV analysis can be used to diagnose diabetes. The present work aims at developing an automated system for classification of normal and diabetes classes by using the heart rate (HR) information extracted from the Electrocardiogram (ECG) signals. The spectral analysis of HRV recognizes patients with autonomic diabetic neuropathy, and gives an earlier diagnosis of impairment of the Autonomic Nervous System (ANS). Significant correlations with the impaired ANS are observed of the HRV spectral indices obtained by using the Discrete Wavelet Transform (DWT) method. Herein, in order to diagnose and detect DM automatically, we have performed DWT decomposition up to 5 levels, and extracted the energy, sample entropy, approximation entropy, kurtosis and skewness features at various detailed coefficient levels of the DWT. We have extracted relative wavelet energy and entropy features up to the 5th level of DWT coefficients extracted from HR signals. These features are ranked by using various ranking methods, namely, Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC) and entropy.The ranked features are then fed into different classifiers, that include Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayes (NBC) and Support Vector Machine (SVM). Our results have shown maximum diagnostic differentiation performance by using a minimum number of features. With our system, we have obtained an average accuracy of 92.02%, sensitivity of 92.59% and specificity of 91.46%, by using DT classifier with ten-fold cross validation. (C) 2015 Elsevier B.V. All rights reserved.