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ECG Signal Classification Using Various Machine Learning Techniques

Journal of Medical Systems volume 42, Article number: 241 (2018) Cite this article

Abstract

Electrocardiogram (ECG) signal is a process that records the heart rate by using electrodes and detects small electrical changes for each heat rate. It is used to investigate some types of abnormal heart function including arrhythmias and conduction disturbance. In this paper the proposed method is used to classify the ECG signal by using classification technique. First the Input signal is preprocessed by using filtering method such as low pass, high pass and butter worth filter to remove the high frequency noise. Butter worth filter is to remove the excess noise in the signal. After preprocessing peak points are detected by using peak detection algorithm and extract the features for the signal are extracted using statistical parameters. Finally, extracted features are classified by using SVM, Adaboost, ANN and Naïve Bayes classifier to classify the ECG signal database into normal or abnormal ECG signal. Experimental result shows that the accuracy of the SVM, Adaboost, ANN and Naïve Bayes classifier is 87.5%, 93%, 94 and 99.7%. Compared to other classifier naïve bayes classifier accuracy is high.

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Affiliations

  1. Satyabama Institute of Science and Technology, Chennai, India

    S Celin

  2. Vidya Jyothi Institute of Technology, Hyderabad, Telangana, 500075, India

    K. Vasanth

Authors
  1. S Celin
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  2. K. Vasanth
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Contributions

First author: Celin S. collected materials and prepared the draft under the supervision of Profs. Vasanth K. The manuscript has gone through several rounds of internal revisions.

Corresponding author

Correspondence to S Celin.

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This article content has no conflict of interest regarding Publication.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

This article is part of the Topical Collection on Image & Signal Processing

S. Celin is a research scholar and K. Vasanth is a professor.

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Cite this article

Celin, S., Vasanth, K. ECG Signal Classification Using Various Machine Learning Techniques. J Med Syst 42, 241 (2018). https://doi.org/10.1007/s10916-018-1083-6

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  • DOI: https://doi.org/10.1007/s10916-018-1083-6

Keywords

  • ECG signal
  • Butter worth filter
  • SVM
  • Adaboost
  • ANN
  • Naïve bayes