Premature Ventricular Contractions Classification using Machine Learning Approach


In this paper, Premature Ventricular Contractions [PVCs] beat classification is proposed for detecting the ventricular arrhythmia. ECG arrhythmia records are considered from MITBIH AD and denoised by using the discrete wavelet transform (DWT). Thereafter, two stage median filter is used to eliminate the baseline wander to obtain the clean and smooth ECG signal. Proposed method has calculated the statistical features of extracted QRS complex of both PVCs and normal beats. KNN and SVM algorithms are used for performance evaluation of the proposed method. Overall SVM algorithm using Gaussian function with kernel scale =0.56 achieved the Sp = 99.71 %, Se =99.80 %, +P =99.71 % and Acc = 99.75 %. The results obtained have shown that the PVCs classification method is more accurate and reliable, and can be used for automatic classification of arrhythmia.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL