Related Experiment Videos
Leveraging 3D Heart Visualisation and Data Balancing Techniques for ECG Classification
Kahina Amara1, Oussama Kerdjidj1, Mohamed Amine Guerroudji1
1Centre for Development of Advanced Technologies, Algiers 16081, Algeria.
Insights
This study introduces a deep learning pipeline for automated arrhythmia classification from electrocardiograms (ECG). Novel 3D visualizations enhance diagnostic insight and anatomical localization of cardiac abnormalities.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cardiovascular diseases are a major global health concern.
- Electrocardiogram (ECG) analysis is crucial but can be time-consuming and error-prone.
- Automated methods are needed to improve accuracy and efficiency in cardiac abnormality diagnosis.
Purpose of the Study:
- To develop a deep learning pipeline for automated arrhythmia classification using ECG data.
- To address challenges posed by imbalanced datasets in cardiac analysis.
- To introduce a 3D visualization framework for enhanced diagnostic insight and anatomical localization.
Main Methods:
- A comprehensive deep learning pipeline was designed for automated arrhythmia classification.
- Specific data balancing strategies were implemented to handle imbalanced datasets.
- A novel 3D visualization framework was developed for interactive anatomical rendering.
Main Results:
- The proposed data balancing techniques significantly improved classification performance.
- The automated system achieved competitive or superior results compared to existing methods.
- 3D visualizations provided precise anatomical localization of arrhythmia substrates.
Conclusions:
- The deep learning pipeline offers a promising approach for accurate automated arrhythmia classification.
- The 3D visualization tool enhances clinical diagnosis and medical education.
- Further inter-patient cross-validation is recommended to establish generalizability.
Abstract:
Cardiovascular diseases are among the most prevalent global health conditions, making the accurate diagnosis and classification of cardiac abnormalities crucial for effective treatment and patient management. While the electrocardiogram (ECG) is the primary tool for assessing cardiac electrical activity, its manual analysis is often time-consuming and susceptible to interpretive error. To address these limitations, this work proposes a comprehensive deep learning pipeline for the automated classification of arrhythmias, incorporating specific strategies to mitigate the challenge of imbalanced datasets. Furthermore, we introduce a novel three-dimensional (3D) visualisation framework that provides interactive, anatomically precise renderings of the heart regions implicated by the ECG classification, thereby delivering enhanced diagnostic insight. Our evaluation demonstrates that the proposed data balancing techniques yield significant performance gains, and under our current experimental setup, the results are competitive with or exceed several previously reported methods. We acknowledge that a more rigorous inter-patient cross-validation is needed to fully establish generalisation. The resulting 3D visualisations not only enable precise anatomical localisation of arrhythmia substrates but also serve as a powerful interactive tool for clinical practice and medical education.
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