Detection of Myocardial Infarction from Multi-lead ECG using Dual-Q Tunable Q-Factor Wavelet Transform

Electrocardiography (ECG) signal analysis is an effective method for diagnosis of heart disease. However, the quality of ECG, corrupted by artifacts, limits the automatic ECG classification. In order to extract good quality ECG, we proposed a new ECG enhancement method based on tunable Q-factor wavelet transform (TQWT). In the proposed method, the original ECG signal was decomposed into high Q-factor component and low Q-factor component with dual-Q TQWT. According to the morphological of P, QRS, T waves in ECG, low Q-factor component was chosen for the representation of ECG. The proposed method was tested on 52 healthy volunteers and 52 myocardial infarction patients from the openly dataset of PTB diagnostic ECG. A total of 288 features, covering time, frequency, nonlinear, and entropy domains, were extracted from R-R interval and ECG (in a window of 5s) across 12 leads. The features were selected by Relief method, and 22 discriminative features were fed into five different classifiers. The classification accuracy for dual-Q TQWT was 86.3%, which was 4.7% higher than the filtered data based on k-nearest neighbors (KNN) algorithm. The comparison results verified that the proposed dual-Q TQWT method provides good feasibility for ECG de-noising.