Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals

Abstract

This study aims to address the need for reliable diagnosis of coronary artery disease (CAD) using artificial intelligence (AI) models. Despite the progress made in mitigating opacity with explainable AI (XAI) and uncertainty quantification (UQ), understanding the real-world predictive reliability of AI methods remains a challenge. In this study, we propose a novel indicator called the Spatial Uncertainty Estimator (SUE) to assess the prediction reliability of classification networks in practical Electrocardiography (ECG) scenarios. SUE quantifies the spatial overlap of critical Grad-CAM (Gradient-weighted Class Activation Mapping) features, offering a confidence score for predictions. To validate SUE, we designed a deep learning network that integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) mechanisms for precise ECG signal classification of CAD. This network achieved high accuracy, sensitivity, and specificity rates of 99.6%, 99.8%, and 98.2%, respectively. During test time, SUE accurately distinguishes between correctly classified and misclassified ECG segments, demonstrating the superiority of the proposed network over existing methods. The study highlights the potential of combining XAI and UQ techniques to enhance ECG analysis. The evaluation of spatial overlap among discriminative features provides quantitative insights into the network’s robustness, encompassing both current prediction accuracy and the repeatability of predictions.