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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
CnnBoost: a multilevel explainable stacked ensemble framework for effective detection of Myocardial Infarction from
Pillai Lekshmi Ashokan1,1, S Siva Sathya1,2, Santhosh Satheesh1,1
1Department of Computer Science, School of Engineering & Technology, Pondicherry University, Kalapet, Puducherry, 605014 India.
Insights
This study introduces CNNBoost, an explainable AI framework that accurately classifies myocardial infarction (MI) from ECG images. The model identifies critical ECG leads, enhancing diagnostic accuracy and clinical decision support for heart conditions.
Area of Science:
- Cardiology
- Artificial Intelligence
- Machine Learning
Background:
- Electrocardiogram (ECG) is a vital non-invasive tool for diagnosing cardiovascular diseases.
- Interpreting ECGs requires specialized expertise, creating a need for automated diagnostic assistance.
- Automated detection of heart abnormalities can improve diagnostic accuracy and efficiency.
Purpose of the Study:
- To develop an explainable machine-learning framework for classifying myocardial infarction (MI) and other heart abnormalities using ECG images.
- To integrate deep learning and ensemble methods for enhanced ECG analysis.
- To improve the interpretability of AI models in cardiovascular diagnostics.
Main Methods:
- Utilized a dataset of South Asian ECG images across four classes: Normal, Abnormal, MI, and Previous History of MI.
- Developed CNNBoost, a Multilevel Explainable Stacked Ensemble model combining CNN spatial features with time-series data for XGBoost.
- Employed SHapley Additive Explanations (SHAP) for identifying diagnostically significant ECG leads, validated by a cardiologist.
Main Results:
- Achieved 99% accuracy, 98.58% AUC, and 96.47% AUPRC for MI classification.
- The explainability component successfully identified critical ECG leads, enhancing model trustworthiness.
- Demonstrated effective classification of various heart abnormalities.
Conclusions:
- The CNNBoost framework integrates deep learning and ensemble learning to enhance ECG classification accuracy and clinical relevance.
- The model's ability to learn spatial and temporal features improves interpretability and decision support for clinicians.
- Cardiologist validation supports the framework's potential for real-world healthcare applications, reducing misdiagnoses and aiding clinical decision-making.
Purpose:
Electrocardiogram (ECG) is the most commonly used non-invasive diagnostic tool for cardiovascular diseases. However, its interpretation requires significant expertise, which may not be available across all medical specializations. Automated detection of heart abnormalities can assist clinicians in making accurate and efficient diagnoses. This study aims to develop an explainable machine-learning framework for classifying myocardial infarction (MI) and other heart abnormalities using ECG images.
Methods:
Publicly available ECG images from health centers in South Asia were used for this study. The dataset consists of four classes: Normal, Abnormal, Myocardial Infarction (MI), and Previous History of MI. The ECG images were preprocessed and transformed into time-series signals for efficient computation. These signals were then used to train an ensemble model, XGBoost, and a deep learning model, Convolutional Neural Network (CNN). To enhance learning of both spatial and temporal features, we proposed CNNBoost, a Multilevel Explainable Stacked Ensemble model where CNN-extracted spatial features were concatenated with time-series data and processed by XGBoost. SHapley Additive Explanations (SHAP) were used to identify diagnostically significant ECG leads, validated by a senior cardiologist.
Results:
The proposed framework achieved 99% accuracy, 98.58% AUC, and 96.47% AUPRC, demonstrating effective MI classification. The explainability component identified critical ECG leads, reinforcing model trustworthiness.
Conclusion:
By integrating deep learning and ensemble learning, our approach enhances ECG classification while ensuring clinical relevance. CNNBoost's ability to learn spatial and temporal features improves interpretability and decision support. Cardiologist validation confirms its potential for real-world healthcare applications, reducing misdiagnoses and aiding clinical decision-making.
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