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Detection of Normal and Abnormal ECG Signal Using ANN

  • Sourav Mondal
  • Prakash Choudhary
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 807)

Abstract

The normality & abnormality of the heart is normally monitored by ECG. Several algorithms are proposed to classify ECG signals. In this paper, discrete wavelet transform is used for extracting some statistical features and Multilayer perceptron (MLP) neural network with Back-propagation performs the classification task. Two types of ECG signals (normal and abnormal) can be detected in this work from each database. The records from MIT-BIH Arrhythmias and Apnea ECG database from physionet have been used for training and testing our neural network based classifier. 90% healthy and 100% abnormal are detected in MIT-BIH Arrhythmias database with the overall accuracy of 94.44%. In Apnea-ECG database, 96% normal and 95.6% abnormal ECG signals are detected and achieves 95.7% classification rate.

Keywords

ECG Daubechies Discrete wavelet transform Neural network MIT-BIH Arrhythmia database Apnea ECG database 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNIT ManipurManipurIndia

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