ECG Signal Classification Based On Deep Learning Classifier

Authors

  • Kavitha R Department of Computer Science, Government Arts College, Udumalpet, India
  • Christopher T Department of Computer Science, Government Arts College, Udumalpet, India

DOI:

https://doi.org/10.26438/ijcse/v6i9.438441

Keywords:

ECG, Arrhythmia, MIT-BIH, CNN

Abstract

ECG (Electrocardiogram), non-stationary biomedical signal measures the electrical activity of the human heart. This ECG signal helps the professional as a diagnostic tool to predict the cardiac disorder and the function of the human heart. According to the report of WHO (World Health Organization), most of the humans suffer from the cardiac disorder and passed due to the cardiac illness.ECG signal analysis is an important factor in this prediction and this work proposes an automatic classification of the signal which identifies the normal and abnormal signal. The ECG signals are taken from the MIT BIH database. The ECG signal is identified as normal and abnormal with the deep learning classifier CNN. The CNN is a convolution neural network which requires minimal pre-processing compared with traditional machine learning classifier. This work focus on the prediction of the cardiac disorder with automatic classification.

References

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[7] R.Kavitha, Dr.T.Christopher " Predicting Accuracy in ECG signal classification: A comparative method for feature selection", Journal of advanced research and dynamic control systems, vol.10 pp:273-281,2018.

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Published

2018-09-30
CITATION
DOI: 10.26438/ijcse/v6i9.438441
Published: 2018-09-30

How to Cite

[1]
R. Kavitha and T. Christopher, “ECG Signal Classification Based On Deep Learning Classifier”, Int. J. Comp. Sci. Eng., vol. 6, no. 9, pp. 438–441, Sep. 2018.

Issue

Vol. 6 No. 9 (2018): IJCSE September Editor

Section

Review Article

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