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ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features |
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| Authors: | MR Homaeinezhad SA Atyabi E Tavakkoli HN Toosi A Ghaffari R Ebrahimpour |
| Affiliation: | a Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran b Cardio Vascular Research Group (CVRG), K.N. Toosi University of Technology, Tehran, Iran c Department of Mechatronic Engineering, Islamic Azad University, South Tehran Branch, Iran d Young Researchers Club, Islamic Azad University, South Tehran Branch, Tehran, Iran e Department of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran, Iran |
| Abstract: | In this study, a new supervised noise-artifact-robust heart arrhythmia fusion classification solution, is introduced. Proposed method consists of structurally diverse classifiers with a new QRS complex geometrical feature extraction technique.Toward this objective, first, the events of the electrocardiogram (ECG) signal are detected and delineated using a robust wavelet-based algorithm. Then, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Next, the curve length of each excerpted segment is calculated and is used as the element of the feature space. Discrimination power of proposed classifier in isolation of different Gold standard beats was assessed with accuracy 98.20%. Also, proposed learning machine was applied to 7 arrhythmias belonging to 15 different records and accuracy 98.06% was achieved. Comparisons with peer-reviewed studies prove a marginal progress in computerized heart arrhythmia recognition technologies. |
| Keywords: | KNN K-nearest neighbors SVM support vector machine ECG electrocardiogram DWT discrete wavelet transforms SNR signal to noise ratio ANN artificial neural network MEN maximum epochs number NHLN number of hidden layer neurons RBF radial basis function MLP-BP multi-layer perceptron back propagation FP false positive FN false negative TP true positive P+ positive predictivity (%) Se sensitivity (%) CPUT CPU time MITDB MIT-BIH Arrhythmia Database SMF smoothing function FIR finite-duration impulse response LBBB left bundle branch block RBBB right bundle branch block PVC premature ventricular contraction APB atrial premature beat VE ventricular escape beat CHECK#0 procedure of evaluating obtained results using MIT-BIH annotation files CHECK#1 procedure of evaluating obtained results consulting with a control cardiologist CHECK#2 procedure of evaluating obtained results consulting with a control cardiologist and also at least with 3 residents |
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