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Classification of electrocardiogram signals using deep learning based on genetic algorithm feature extraction.

Hossein Khezripour1, Saadat Pour Mozaffari2, Midia Reshadi1

  • 1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

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Summary

This study introduces a novel deep learning method for classifying cardiac arrhythmias using electrocardiogram (ECG) signals. The approach achieved high accuracy in identifying various heart rhythm conditions, aiding in timely diagnosis.

Keywords:
adaptive neuro-fuzzy inference system (ANFIS)artificial neural network (ANN)discrete wavelet transformfeature extractiongenetic algorithm

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cardiac arrhythmias require timely diagnosis for effective treatment.
  • Electrocardiogram (ECG) signals are crucial for identifying heart rhythm abnormalities.
  • Accurate classification of diverse arrhythmias remains a challenge in medical research.

Purpose of the Study:

  • To develop and evaluate a new deep learning-based method for classifying cardiac arrhythmias from ECG signals.
  • To enhance the sensitivity and accuracy of ECG signal classification for various heart conditions.
  • To diagnose heart rhythm diseases by classifying ECG signals into distinct rhythm classes.

Main Methods:

  • ECG signals were preprocessed using noise removal filters.
  • Discrete Wavelet Transform (DWT) was employed for feature extraction based on wavelet decomposition energy and PQRS morphological features.
  • Genetic algorithms were utilized to reduce feature vectors and optimize Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) weights.
  • A deep learning framework was implemented for signal classification.

Main Results:

  • The proposed method achieved high learning accuracy: 99.9% for ANN training and 88.92% for ANN testing.
  • ANFIS demonstrated comparable accuracy: 99.8% for training and 88.83% for testing.
  • The classification successfully distinguished between normal heartbeats and various arrhythmias, including congestive heart failure, ventricular arrhythmias, atrial fibrillation, and atrial flutter.

Conclusions:

  • The developed deep learning approach shows significant promise for accurate cardiac arrhythmia classification using ECG signals.
  • The method offers a sensitive and effective tool for the diagnosis of heart rhythm diseases.
  • The combination of DWT, genetic algorithms, and deep learning models (ANN, ANFIS) provides a robust framework for medical signal analysis.