Cardiac arrhythmia recognition with robust discrete wavelet-based and geometrical feature extraction via classifiers of SVM and MLP-BP and PNN neural networks

Introduction: An ECG signal has important information that can help for reflecting cardiac activity of a patient and medical diagnosis. Consistent or periodical heart rhythm disorders can result cardiac arrhythmias so classification algorithm for recognizing arrhythmias with satisfactory accuracy is necessary. Aims: In this study, a robust wavelet based algorithm for detection and delineation of events in ECG signal is applied and then a new synthesis of MLP-BP and PNN neural networks for heart arrhythmia classification was described Methods: As a matter of fact any changes in the morphology of an ECG due to the arrhythmia are observed in time and frequency analysis so multi resolution analysis is applied for feature detection. First, noise and artifact is rejected by a discrete wavelet transform (DWT) and multi lead ECG is obtained Then QRS complexes of signal is extracted and the signal is decomposed so corresponding DWT scales are segmented Next curve length and high order moment order based feature extraction are calculated for each excerpted segment and elements of feature vector for regulating the parameters of classifiers are obtained After generation of feature source and segmentation, Multi-Layer Perceptron-Back Propagation (MLP-BP) neural networks, Probabilistic Neural Network (PNN) and support vector machine (SVM) were designed and tuned and their results were compared Results: The proposed algorithm was tested to all 48 record of the MIT-BIH arrhythmia database and also the proposed topology of classifiers and its related parameters is optimized by searching of best value of parameters. The average value of accuracy of each classifier over all records of MIT-BIH for arrhythmias recognition is Acc=97.42, Acc=98. 24 and Acc=97. 42 for S VM, MLP and PNN classifiers respectively and also obtained results were compared with similar peer-reviewed studies in this subject.