Enhancing Arrhythmia Diagnosis with Data-Driven Methods: A 12-Lead ECG-Based Explainable AI Model

Abstract

Accurate and early prediction of arrhythmias using Electrocardiograms (ECG) presents significant challenges due to the non-stationary nature of ECG signals and inter-patient variability, posing difficulties even for seasoned cardiologists. Deep Learning (DL) methods offer precision in identifying diagnostic ECG patterns for arrhythmias, yet they often lack the transparency needed for clinical application, thus hindering their broader adoption in healthcare. This study introduces an explainable DL-based prediction model using ECG signals to classify nine distinct arrhythmia categories. We evaluated various DL architectures, including ResNet, DenseNet, and VGG16, using raw ECG data. The ResNet34 model emerged as the most effective, achieving an Area Under the Receiver Operating Characteristic (AUROC) of 0.98 and an F1-score of 0.826. Additionally, we explored a hybrid approach that combines raw ECG signals with Heart Rate Variability (HRV) features. Our explainability analysis, utilizing the SHAP technique, identifies the most influential ECG leads for each arrhythmia type and pinpoints critical signal segments for individual disease prediction. This study emphasizes the importance of explainability in arrhythmia prediction models, a critical aspect often overlooked in current research, and highlights its potential to enhance model acceptance and utility in clinical settings.