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Related Experiment Videos

Scalogram-CNN fusion with interactive 3D parameter exploration for inter-patient ECG classification.

Nurgül Özmen Süzme1,2, Ömer Nezih Gerek3

  • 1Department of Biomedical Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey. nozmen@aku.edu.tr.

Scientific Reports
|July 3, 2026
PubMed
Summary

Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...

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This study converts electrocardiogram (ECG) signals into images for classification, achieving up to 89.78% accuracy in unseen-patient tests. An interactive 3D dashboard aids parameter exploration for cardiac anomaly detection.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence in Medicine

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing heart conditions but are challenging to analyze due to their non-stationary nature.
  • Classifying ECG signals from unseen patients (inter-patient generalization) remains a significant hurdle in clinical applications.

Purpose of the Study:

  • To investigate ECG classification performance using 2D scalogram images derived from ECG signals under unseen-patient conditions.
  • To develop and evaluate an interactive 3D visualization framework for optimizing ECG analysis parameters.

Main Methods:

  • ECG signals were transformed into 2D scalogram images.
  • A Convolutional Neural Network (CNN) was employed for classifying the scalogram images.
Keywords:
3D visualisationCNNECG classificationParameter optimisationScalogramsWavelet transform

Related Experiment Videos

  • An interactive 3D visualization framework using Plotly Dash was developed to explore parameters like wavelet types, feature extraction levels, and scale ranges.
  • Handcrafted statistical and wavelet-derived features were also evaluated on unseen patient data.

    • Main Results:

    • Classification accuracy varied significantly based on the choice of wavelet and feature extraction strategy.
    • Up to 89.78% accuracy was achieved using Complex Morlet wavelets in Experiment II, and 83.04% with Mexican Hat wavelets in Experiment I.
    • The interactive 3D dashboard facilitated efficient parameter selection, result exploration, and analysis, demonstrating consistent performance across unseen-patient test settings.

    Conclusions:

    • The proposed framework effectively converts ECG signals into analyzable 2D images for classification.
    • The interactive visualization tool enhances parameter exploration for ECG analysis, offering potential benefits for telemedicine and remote monitoring.
    • This approach provides a valuable framework for small-data ECG analysis and improving inter-patient generalization.