Detection and Classification of R-Peak Using Naïve Bayes Classifier

Authors

  • S Celin
  • K Vasanth

DOI:

https://doi.org/10.14419/ijet.v7i3.27.17982

Published:

2018-08-15

Keywords:

ECG signal, butter worth filter, support vector machine, adaboost, ANN, naïve bayes, MIT-BIH arrhythmia database.

Abstract

Electrocardiogram (ECG) in classification of signals plays a major role in the diagnoses of heart diseases. The main challenging problem is the classification of accurate ECG. Here in this paper the ECG is classified into arrhythmia types. It is very important that detecting the heart disease and finding the treatment for the patient at the earliest must be done accurately. In the ECG classification different classifiers are available. The best accuracy value of 99.7% is produced by using the Bayes classifiers in this paper. ECG databases, classifiers, feature extraction techniques and performance measures are presented in the pre-processing technique. And also the classification of ECG, analysis of input beat selection and the output of classifiers are also discussed in this paper.

 

 

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