Characterization of Cardiovascular Diseases Using Wavelet Packet Decomposition and Nonlinear Measures of Electrocardiogram Signal

Cardiovascular diseases (CVDs) remain as the primary causes of disability and mortality worldwide and are predicted to continue rise in the future due to inadequate preventive actions. Electrocardiogram (ECG) signal contains vital clinical information that assists significantly in the diagnosis of CVDs. Assessment of subtle ECG parameters that indicate the presence of CVDs are extremely difficult and requires long hours of manual examination for accurate diagnosis. Hence, automated computer-aided diagnosis systems might help in overcoming these limitations. In this study, a novel algorithm is proposed based on the combination of wavelet packet decomposition (WPD) and nonlinear features. The proposed method achieved classification results of 97.98% accuracy, 99.61% sensitivity and 94.84% specificity with 8 reliefF ranked features. The proposed methodology is highly efficient in helping clinical staff to detect cardiac abnormalities using a single algorithm.

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