Advancing cardiac diagnostics: Exceptional accuracy in abnormal ECG signal classification with cascading deep learning and explainability analysis

Arrhythmias, cardiac rhythm disorders, demand precise diagnosis for effective treatment planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation. While deep learning excels in ECG classification, its interpretability remains a challenge. Addressing this issue, our research introduces an innovative approach employing image-based ECG representations and cascading deep neural networks (CDNNs) to enhance arrhythmia detection precision. Initiating with the transformation of 12-lead ECG time-series data, particularly focusing on Lead II, into image-based representations using relative positioning matrices (RPM), this novel conversion captures spatial and temporal information concurrently, enriching the depth of our data analysis. Subsequently, CDNNs play a pivotal role in feature extraction, designed to automatically extract impactful features from image-based ECG representations, providing profound insights into cardiac electrical activity. Extracted features serve as inputs for the subsequent deep neural network for classification. Experimental results on the Shaoxing–Chapman ECG database, encompassing over 10,000 recordings with an 80/10/10 split, 80/20 split, 60/40 split, and 10-fold cross-validation, showcase a remarkable 100% accuracy in distinguishing between seven and four different arrhythmia classes. Incorporating SHapley Additive exPlanations (SHAP) values, our methodology offers comprehensive insights into the model's decision-making, ensuring interpretability. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) aids feature visualization, elucidating key features driving predictions. By amalgamating image-based representation, CDNNs, and interpretability techniques, our research not only attains exceptional classification performance but also boosts model transparency and trustworthiness. These findings hold promise for advancing arrhythmia diagnosis and enhancing interpretability in deep learning-based medical applications.