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ECG Signal Classification Using Various Machine Learning Techniques.

S Celin1, K Vasanth2

  • 1Satyabama Institute of Science and Technology, Chennai, India. celin10sam@gmail.com.

Journal of Medical Systems
|October 19, 2018
PubMed
Summary

This study presents a novel method for classifying electrocardiogram (ECG) signals using various machine learning algorithms. The Naïve Bayes classifier achieved the highest accuracy, demonstrating its effectiveness in detecting abnormal heart conditions.

Keywords:
ANNAdaboostButter worth filterECG signalNaïve bayesSVM

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing heart conditions like arrhythmias.
  • Accurate ECG signal classification is essential for effective patient management.
  • Existing methods require robust feature extraction and classification techniques.

Purpose of the Study:

  • To develop and evaluate a classification method for ECG signals.
  • To compare the performance of different machine learning classifiers for ECG analysis.
  • To identify the most accurate classifier for distinguishing normal from abnormal ECG signals.

Main Methods:

  • ECG signals were preprocessed using low-pass, high-pass, and Butterworth filters to reduce noise.
  • Feature extraction was performed using statistical parameters after peak point detection.
  • Classifiers including Support Vector Machine (SVM), Adaboost, Artificial Neural Network (ANN), and Naïve Bayes were employed.

Main Results:

  • The Naïve Bayes classifier achieved the highest accuracy at 99.7%.
  • ANN and Adaboost classifiers showed high accuracies of 94% and 93%, respectively.
  • SVM classifier demonstrated an accuracy of 87.5%.

Conclusions:

  • The proposed method effectively classifies ECG signals into normal or abnormal categories.
  • Naïve Bayes classifier significantly outperforms SVM, Adaboost, and ANN for ECG signal classification.
  • This approach holds promise for improving automated diagnosis of cardiac abnormalities.