Comparison and Performance Evaluation of ECG Classification Techniques Trained with Shorter Database

Authors

  • Indasukshisha Wankhar  Department of Biomedical Engineering, School of Technology, North Eastern Hill University, Shillong793022, Meghalaya, India.
  • IbalawanpynhunWriang  Department of Biomedical Engineering, School of Technology, North Eastern Hill University, Shillong793022, Meghalaya, India.
  • Priya Debnath  Department of Biomedical Engineering, School of Technology, North Eastern Hill University, Shillong793022, Meghalaya, India.
  • Preetisha Bordoloi  Department of Biomedical Engineering, School of Technology, North Eastern Hill University, Shillong793022, Meghalaya, India.
  • R. P. Tripathi  Department of Biomedical Engineering, School of Technology, North Eastern Hill University, Shillong793022, Meghalaya, India.
  • Dinesh Bhatia  Department of Biomedical Engineering, School of Technology, North Eastern Hill University, Shillong793022, Meghalaya, India.
  • Sristi Jha  Department of Biomedical Engineering, School of Technology, North Eastern Hill University, Shillong793022, Meghalaya, India.

Keywords:

Electrocardiogram, Comparison, Artificial neural network, Naive Bayes Classifier, Support Vector Machine.

Abstract

Diagnosis of cardiovascular diseases can be done effectively with the classification based on analysis of different electrocardiogram (ECG) features. Different algorithms for different data mining techniques have been put forward by the researchers for diagnosing the heart diseases. In our research we have compared three classification techniques namely, Pattern Recognition, Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) in classification of different heart diseases. Based on the performance we have concluded that which one of the above techniques is much effective and accurate especially if a short database of ECG features is used for training the model. The data used in this study is collected from the cardiac patients from a hospital. The observations indicate that NBC is found to be more accurate and effective than SVM and Pattern Recognition when the model is trained by a sort training database.

References

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International Journal of Scientific Research in Science and Technology

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Published

2018-06-30

Issue

Volume 4, Issue 8, May-June-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Indasukshisha Wankhar, IbalawanpynhunWriang, Priya Debnath, Preetisha Bordoloi, R. P. Tripathi, Dinesh Bhatia, Sristi Jha "Comparison and Performance Evaluation of ECG Classification Techniques Trained with Shorter Database" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 4, Issue 8, pp.419-425, May-June-2018.