Automated detection of arrhythmias using a novel interpretable feature set extracted from 12-lead electrocardiogram.

The availability of large-scale electrocardiogram (ECG) databases and advancements in machine learning have facilitated the development of automated diagnostic systems for cardiac arrhythmias. Deep learning models, despite their potential for high accuracy, have had limited clinical adoption due to their inherent lack of interpretability. This study bridges this gap by proposing a feature-based approach that maintains comparable performance to deep learning while providing enhanced interpretability for clinical utility. The method extracts a total of 654 individual features, classified into 60 feature types from each ECG. The features use mathematical techniques such as the Fourier transform, wavelet transform, and cross-correlation for rigorous evaluation of ECG characteristics. The eXtreme Gradient Boosting framework was employed to classify each ECG into one of nine diagnostic classes. Shapley Additive Explanations (SHAP) value analysis was used to downselect the feature set to the minimal set without incurring a performance reduction (159 features). Overall, the proposed method demonstrated performance comparable to state-of-the-art deep learning classifiers, achieving a weighted F1 score of 81% during cross-validation and 68% on the external test dataset, while offering greater ease of implementation and adaptability to diverse clinical applications. Notably, the proposed method demonstrated superior accuracy in identifying atrial fibrillation and block-related abnormalities, achieving overall F1 scores of 89% and 87% during cross-validation and 79% and 75% on the external test dataset, respectively. SHAP value analysis of the testing results revealed the top-performing features for each diagnostic class aligned with standard clinical diagnostic processes, highlighting the clinical interpretability of our approach.