ecg signals

ecg signal

electrocardiogram ecg

heart sound

ecg_52314

ecg_61226

ecg_118663

af detection

ecg_60423

mit-bih

Pattern Recognition Letters

The extreme deposition of plaque in the inner walls of arteries causes coronary artery disease (CAD). It can be detected by the morphological changes in the electrocardiogram (ECG) signals. The manual analysis of ECG signals is inefficient, as it is laborious and vulnerable to errors. CAD diagnosis tests also demand physical work from patients, which may not be fulfilled as readily by the elderly and physically challenged patients. The use of automated coronary artery disease diagnosis system can help to overcome the aforementioned problems. In this study, ECG segments of different durations (2s and 5s) are employed for the analysis of CAD. We propose the use of a recently developed optimally time-frequency concentrated (OTFC) even-length biorthogonal wavelet filter bank (BWFB) for automatically identifying CAD. The fuzzy entropy (FE) and log-energy (LogE) were extracted from the OTFC decomposed coefficients. Using the Gaussian support vector machine (GSVM) classifier, an average classification accuracy of 99.53% is achieved with 10-fold cross-validation (CV). The average sensitivity and specificity obtained are 98.64% & 99.70%, respectively with the Matthews correlation coefficient(MCC) of 0.983. The classification performance of the proposed model has surpassed most of the state-of-art models. The method presented in this paper can be of great help to clinicians and cardiologists to validate their diagnosis. Our developed model is economical, robust and accurate in diagnosing the CAD.

A new method to identify coronary artery disease with ECG signals and time-Frequency concentrated antisymmetric biorthogonal wavelet filter bank