J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals

Abstract

As a new important index of the electrocardiogram (ECG) of ventricular bipolar play, J wave plays an increasingly significant role in the clinical diagnosis. The existence of J wave hints at potential crisis of fatal disease and even death. Nowadays, however, it can hardly meet the clinical needs where the diagnosis of J wave variation only depends on experience of clinicians. Therefore, a new technique which is capable of detecting J wave using analytic time-frequency flexible wavelet transformation (ATFFWT) is proposed in this paper. We have used ATFFWT to decompose the processed ECG signals into the desired subbands. Further, Fuzzy Entropy (FE) is computed from each subband to capture more hidden and meaningful information. Feature scoring method is applied to select optimal feature set. Finally, the extracted features are fed to Least Squares-Support Vector Machine (LS-SVM) classifier. The 10-fold cross validation is used to obtain reliable and stable performance and to avoid the overfitting of the model. Our proposed algorithm has achieved accuracy of 97.61% for Morlet Wavelet (MW) kernel in comparison to 97.56% for Radial Basis Function (RBF) kernel. The developed effective algorithm can be used to design an expert system to aid clinicians in their regular diagnosis.