Interpretation and Classification of Arrhythmia Using Deep Convolutional Network

Electrocardiogram (ECG) signal analysis can be time-consuming, tedious, and error-prone. Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac abnormalities accurately and efficiently. Recently, deep learning (DL) models have shown unprecedented progress and strong arrhythmia classification capabilities, but their deployment in the medical sector is constrained due to their “black-box” nature. This article proposes a robust explainability method to assist in explaining the underlying decision-making process in deep neural networks (DNNs) and help provide feedback on biases that would benefit in improving DNN models. To achieve these objectives, initially, a DL model is trained on the MIT-BIH Arrhythmia Database and their classification performance is evaluated. The classification findings are then interpreted using the post-hoc explanation methods such as the SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM) that interpret the decision rationale. As these methods are initially proposed for image applications therefore a new masking approach is proposed to cater these post-hoc explainability methods for ECG time-series data. After evaluating these methods for ECG arrhythmia classification, several drawbacks are drawn such as they fail to locate a feature’s importance if there are multiple occurrences of the same feature in a signal and also, SHAP and LIME perform random perturbations that sometimes produce unreliable explanations. Therefore, to overcome the drawbacks associated with these post-hoc explainability methods on time-series data, a novel K-GradCam method is proposed. The proposed K-GradCam method ensemble the benefits of these gradient-based and perturbation-based approaches and has demonstrated advantages over SHAP, Grad-CAM and LIME in terms of interpreting the models’ decisions. To compare the proposed technique with post-hoc explainability methods quantitatively, the confidence index of the proposed method is evaluated using dice loss. The proposed method shares 71% similarity with SHAP and 81% similarity with Grad-CAM methods; however, it shares the benefits of both methods and is computationally faster than SHAP and LIME.