From Black Boxes to Clarity: Arrhythmia Detection with Explainable AI Models

Arrhythmia, characterized by irregular heart rhythms, is a global health concern. Traditional diagnosis via electrocardiograms (ECGs) is labor-intensive and can be error-prone. Artificial Intelligence (AI), particularly machine learning and deep learning, offers promise in improving accuracy and efficiency in arrhythmia diagnosis. However, AI's "black-box" nature, where decision-making remains opaque, hinders its clinical integration. We explore the need for transparency and ethical considerations in AI, including bias mitigation and regulatory compliance. Explainable AI (XAI) emerges as a solution. We investigate various XAI techniques such as Class-Activation Maps (CAM), SHAP values, Attention Mechanisms (AMs), Saliency Maps (SMs), Learned Internal Parameters (LIPs), Feature Importance (FI), Occlusion Maps (OMs), Layer-wise Relevance Propagations (LRPs), Local Interpretable Model-agnostic Explanations (LIME), and Example-based Explanations (EB). These techniques illuminate AI's decision processes, enhancing trust, bias identification, and collaboration with medical experts. The combination of AI's computational abilities and XAI's transparency strives to improve diagnostic accuracy while maintaining human comprehension in arrhythmia diagnosis.

Keywords:

Artificial Intelligence , Machine Learning , Arrhythmia , ECG , Explainable AI