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Current methods for arrhythmia detection often lack accessible, educational tools for understanding heart conditions. This study addresses that gap by developing a minimalist, user-friendly arrhythmia detection system, particularly suited for educational purposes, utilizing a three-dimensional human heart model in a Unity environment. The model receives electrocardiogram (ECG) data from a CSV file containing timestamps and mV values from two leads (based on the MIT-BIH arrhythmia database) and visualizes the heart’s beat-by-beat activity. The system can detect four types of arrhythmias, although the visualizations currently focus on Premature Atrial Contractions (PAC) and Premature Ventricular Contractions (PVC). A convolutional neural network (CNN) is used to detect arrhythmias with an overall accuracy of 99.7%. This tool enhances understanding of cardiac activity, offering a more engaging, educational alternative to traditional ECG readings, helping users visualize and learn about heart function more effectively.

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3-Dimensional ECG Visualisation and Arrhythmia Detection via Convolutional Neural Network Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Ahmad Thariq,
  • Mohd Shahrizal Sunar,
  • Herman Tolle

摘要

Current methods for arrhythmia detection often lack accessible, educational tools for understanding heart conditions. This study addresses that gap by developing a minimalist, user-friendly arrhythmia detection system, particularly suited for educational purposes, utilizing a three-dimensional human heart model in a Unity environment. The model receives electrocardiogram (ECG) data from a CSV file containing timestamps and mV values from two leads (based on the MIT-BIH arrhythmia database) and visualizes the heart’s beat-by-beat activity. The system can detect four types of arrhythmias, although the visualizations currently focus on Premature Atrial Contractions (PAC) and Premature Ventricular Contractions (PVC). A convolutional neural network (CNN) is used to detect arrhythmias with an overall accuracy of 99.7%. This tool enhances understanding of cardiac activity, offering a more engaging, educational alternative to traditional ECG readings, helping users visualize and learn about heart function more effectively.