Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
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A Method of Analyzing ECG to Diagnose Heart Abnormality utilizing SVM and DWT

  • Shdefat, Ahmed (Department of Computer Engineering, INJE University) ;
  • Joo, Moonil (Department of Computer Engineering, INJE University) ;
  • Kim, Heecheol (Department of Computer Engineering, INJE University)
  • Received : 2016.06.10
  • Accepted : 2016.08.17
  • Published : 2016.06.30
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Abstract

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

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References

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