sis 20(26): e6

Research Article

Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals

Download222 downloads
  • @ARTICLE{10.4108/eai.13-7-2018.163095,
        author={Ritu Singh and Navin Rajpal and Rajesh Mehta},
        title={Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={26},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={2},
        keywords={Electrocardiogram, MIT/BIH, Discrete Wavelet Transform, Kernel, classifiers},
        doi={10.4108/eai.13-7-2018.163095}
    }
    
  • Ritu Singh
    Navin Rajpal
    Rajesh Mehta
    Year: 2020
    Wavelet and kernel dimensional reduction on arrhythmia classification of ECG signals
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.163095
Ritu Singh1, Navin Rajpal1, Rajesh Mehta2
  • 1: University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka, New-Delhi, India
  • 2: Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

Abstract

Electrocardiogram (ECG) monitoring is continuously required to detect cardiac ailments. At times it is challenging to interpret the differences in the P- QRS-T curve. The proposed approach aims to show the excellence of kernel capabilities of Kernel Principal Component Analysis (KPCA) and Kernel Independent Component Analysis (KICA) in the wavelet domain. In this work, experiments are performed using five different categories of cardiac beats. The supervised classifiers like feed-forward neural network (FNN), backpropagation neural network (BPNN), and K nearest neighbor (KNN) statistically evaluates the impact of discrete wavelet with KPCA and KICA on extracted beats. The performance evaluation also compares the outcomes with existing techniques. The obtained results justify the supremacy of the combination of wavelet, kernel, and KNN approach, yielding a 99.7 % classification success rate. The five-fold crossvalidation scheme is used for measuring the efficacy of classifiers.