MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia

Area of Science:

• Cardiology
• Artificial Intelligence
• Medical Imaging

Background:

• Cardiac arrhythmia is a major global cause of death, necessitating early and accurate diagnosis.
• Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac arrhythmias.
• Deep learning models show promise in automated ECG interpretation but often lack clinical transparency due to their "black box" nature.

Purpose of the Study:

• To develop an explainable artificial intelligence (XAI) method for cardiac arrhythmia detection.
• To provide interpretability for both 1D ECG signal and 2D ECG image data.
• To enhance clinical confidence and transparency in AI-based medical diagnostic tools.

Main Methods:

• Developed an innovative MM-GradCAM method combining 1D ECG signal and 2D ECG image data formats.
• Utilized a 17-layer Convolutional Neural Network (CNN) for four-class arrhythmia detection on a dataset of over 10,000 patients.
• Generated separate explainability outputs for each data format (signal and image).

Main Results:

• The CNN model achieved 93.07% accuracy for the signal form and 97.44% accuracy for the image form.
• Explainability maps generated by MM-GradCAM were validated by a cardiologist for interpretability and clinical significance.
• The study demonstrated the effectiveness of MM-GradCAM in providing transparent insights into AI diagnostic decisions.

Conclusions:

• MM-GradCAM offers a pioneering approach to explainability in medical AI, specifically for cardiac arrhythmia diagnosis.
• The method enhances the reliability and transparency of AI applications in healthcare.
• This work has the potential to significantly improve patient outcomes through more trustworthy AI diagnostics.