Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Comput Methods Programs Biomed. 2018 Jul;161:133-143. doi: 10.1016/j.cmpb.2018.04.018. Epub 2018 Apr 20.
Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.
心血管疾病 (CVDs) 是全球死亡的主要原因。通过早期检测和治疗干预，可以降低死亡率。临床上，心电图 (ECG) 信号提供了有关心脏异常的有用信息，因此被用作检测各种 CVDs 的诊断方式。然而，这些时间序列中的细微变化表明存在特定的疾病。因此，手动检查这些心电图节拍可能单调、耗时且压力大。为了克服手动 ECG 信号分析的局限性，本文使用了一种新颖的离散小波变换 (DWT) 方法结合非线性特征，用于 CVDs 的自动特征描述。正常、扩张型心肌病 (DCM)、肥厚型心肌病 (HCM) 和心肌梗死 (MI) 的 ECG 信号经过五层次的 DWT。从 DWT 系数中提取四个非线性特征（模糊熵、样本熵、分形维数和信号能量）的相对小波。这些特征被馈送到顺序前向选择 (SFS) 技术，然后使用 ReliefF 方法进行排序。我们的方法使用 K-最近邻 (kNN) 分类器，通过 ReliefF 方法排序的 15 个特征，获得了 99.27%的最大分类准确率 (acc)、99.74%的灵敏度 (sen) 和 98.08%的特异性 (spec)。该方法可以帮助临床工作人员更快、更准确地诊断 CVDs。因此，通过早期检测和治疗 CVDs，可以显著提高生存率。
