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This study introduces a machine learning approach for detecting cardiac arrhythmias using electrocardiograms (ECG). The proposed method enhances arrhythmia detection accuracy by 20% compared to traditional systems.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Signal Processing

Background:

  • Electrocardiograms (ECG) are vital for diagnosing cardiac arrhythmias but require signal processing for efficient data management.
  • Extracting temporal morphological features from ECG signals is challenging for traditional visual analysis.
  • Machine learning and signal processing are key methodologies in biomedical research for ECG analysis.

Purpose of the Study:

  • To investigate machine learning classification algorithms for enhanced ECG analysis and arrhythmia detection.
  • To develop an automated system for accurate identification of cardiac arrhythmias.
  • To improve the efficiency of ECG signal reduction for data storage and transmission.

Main Methods:

  • Feature extraction using Fast Fourier Transform (FFT) on ECG signals.
  • Classification of four types of cardiac arrhythmias using an improved AlexNet (a type of Convolutional Neural Network - CNN) classifier.
  • Comparison of the proposed CNN algorithm's performance against other machine learning algorithms.

Main Results:

  • The proposed ECG arrhythmia classification method demonstrates superior effectiveness across various parameters.
  • The improved AlexNet classifier accurately distinguishes between four distinct arrhythmia types.
  • The developed system shows a 20% improvement in detecting deviations compared to traditional methods.

Conclusions:

  • The integration of FFT for feature extraction and an improved AlexNet classifier offers a highly effective solution for ECG-based arrhythmia detection.
  • This machine learning approach significantly enhances diagnostic accuracy for cardiac arrhythmias.
  • The proposed system represents a valuable advancement in electronic health systems for cardiac monitoring.