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Feature Extraction of ECG Signals using Discrete Wavelet Transform and MFCC

Electrocardiogram (ECG) is a diagnostic tool that has been widely used for detecting cardiovascular diseases (CVDs). It works by recording the electrical activity of the heart that can reveal the abnormalities that present in it. In this study, a diagnostic model is proposed by combining two well-known feature extraction methods called discrete wavelet transform (DWT) and mel-filter cepstrum coefficient (MFCC) to extract features from the ECG signal. K-nearest neighbor (KNN) using euclidean distance is applied as for the classifier. The performance of the model is tested and evaluated on myocardial infarction (MI) labeled as abnormal and healthy signals labeled as normal obtained from the Physikalisch-Technische Bundesanstalt (PTB) database. The dataset contains 200 data, 148 data abnormal and 52 data normal. To achieve generalized results, in the last stage of the analysis, K-fold cross-validation is also applied. According to the experimental, the proposes model show encouraging results, accuracy, sensitivity, and specificity were achieved 88%, 90%, and 80% respectively.