ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition

ECG signals reflect all the electrical activities of the heart. Consequently, it plays a key role in the diagnosis of the cardiac disorder and arrhythmia detection. Based on tiny alterations in the amplitude, duration and morphology of the ECG, computer-aided diagnosis has become a recognized approach to classifying the heartbeats of different types of arrhythmia. In this study, a classification approach was developed based on the non-linearity and nonstationary decomposition methods due to the nature of the ECG signal. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to obtain intrinsic mode functions (IMFs). Established on those IMFs, four parameters have been computed to construct the feature vector. Average power, coefficient of dispersion, sample entropy and singular values have been calculated as parameters from the first six IMFs. Then, ANN has been adopted to apply the feature vector using them and classify five different arrhythmia heartbeats downloaded from Physionet in the MIT–BIH database. To evaluate the performance of the proposed method and compare it with previous algorithms, confusion matrix, sensitivity (SEN), specificity (SPE), accuracy (ACC) and ROC have been used. It has been found that performance from the CEEMDAN and ANN is better than all existing methods, where the SEN is 99.7%, SPE is 99.9%, ACC is 99.9%, and ROC is 01.0%.