ECG arrhythmia recognition via a neuro-SVM–KNN hybrid classifier with virtual QRS image-based geometrical features

摘要

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.

论文关键词：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,Feature extraction,Curve length method,Support vector machine,K-nearest neighbors,Multi layer perceptron,Fusion (hybrid) classification,Arrhythmia classification,Supervised learning machine