cs.CVSep 18, 2026

X-Beat: An Explainable Framework for ECG Image Classification

Authors: Mohammad Sadman Tahsin, Haitham Y. Adarbah, Afzel Noore

Organizations: AI-Based Autonomous System Research Lab Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville (TAMUK) Kingsville, TX, USA

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Abstract

Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction and rhythm abnormalities. However, many high-performing deep learning models remain difficult to deploy in clinical settings due to limited transparency and lack of reliability validation. In this work, we present X- Beat, an explainable and reliability-aware benchmark framework for ECG image classification designed to support trustworthy AI systems in healthcare. The proposed framework combines transfer learning with post-hoc explainability and systematic reliability evaluation across four cardiac classes: Abnormal Heartbeat, History of Myocardial Infarction, Myocardial In- farction, and Normal Heartbeat. Multiple ImageNet-pretrained CNN backbones, including EfficientNet-B0, ResNet-50, DenseNet- 121, and MobileNetV3-Large, are evaluated under a unified training protocol. Beyond standard performance metrics, we incorporate Grad-CAM-based visual explanations together with additional analyses, including explanation stability, regional sen- sitivity, and confidence-based reliability assessment, to examine whether model predictions are supported by clinically meaningful evidence. Experimental results show that ResNet-50 achieves the best performance, reaching 91.94% accuracy and a macro F1- score of 0.9098, with strong class separability (AUC up to 0.995). Explanation analyses indicate that the model primarily focuses on waveform-relevant regions, while reliability evaluation suggests that most incorrect predictions occur with lower confidence. Overall, this work provides a structured and reproducible bench- mark for evaluating both predictive performance and explanation reliability in ECG image classification, contributing toward the development of trustworthy and interpretable AI components for clinical decision support systems.

Figures & tables

Fig. 1: Block diagram of the proposed transfer-learning and explainability framework for ECG image classification. ECG images are collected, preprocessed, and split into training, validation, and test sets. Multiple pretrained CNN backbones are then fine-tuned and compared under a common pipeline, the best model is selected, and Grad-CAM is applied to highlight the image regions most responsible for the final class decision.
ModelClassPrecisionRecallF1-scoreMacro PrecisionMacro RecallMacro F1 ScoreAccuracy
ResNet-50Abnormal Heartbeat1.00000.85110.91950.92410.90390.90980.9194
History of Myocardial Infarction0.89660.76470.8254
Myocardial Infarction0.92311.00000.9600
Normal Heartbeat0.87691.00000.9344
DenseNet-121Abnormal Heartbeat1.00000.72340.83950.90520.89410.89250.9032
History of Myocardial Infarction0.82860.85290.8406
TABLE I: Class-wise precision, recall, and F1-score of different CNN backbones, with macro-averaged metrics and overall accuracy.
Fig. 2: Training, validation, and test accuracy curves over 25 epochs for the selected ECG classification model.
Fig. 3: One-vs-rest ROC curves for the four ECG classes. The classifier shows strong discriminative performance across all classes.
Fig. 4: Confusion matrix of the four-class ECG classifier on the test set.
Fig. 5: Representative input ECG images and corresponding Grad-CAM overlays for four test examples from different diagnostic classes. For each case, the heatmap localizes the waveform regions and lead segments that contribute most to the model’s prediction (warmer colors indicate higher contribution). The examples illustrate class-specific attention patterns, with concentrated activation over discriminative complexes and segment-level abnormalities that drive the final classification.
CategoryMetricValue
Explanation stabilityCorr. / IoU@20%−0.038-0.038 / 0.1390.139
Regional sensitivityBorder / center drop0.5590.559 / 0.5990.599
Transfer learningPretrained ResNet-50 acc. / F10.9190.919 / 0.9110.911
Transfer learningSimpleECGCNN acc. / F10.2530.253 / 0.1010.101
Transfer learningResNet-50 scratch acc. / F10.2530.253 / 0.1010.101
Confidence reliabilityMean conf. corr. / incorr.0.7680.768 / 0.5970.597
TABLE II: Summary of the additional empirical analyses. The table reports Grad-CAM explanation stability under randomization, confidence changes under regional masking, performance differences between pretrained and from-scratch baselines, and confidence-based reliability statistics on the test set. These results complement the main classification metrics by providing further evidence on explanation behavior, spatial sensitivity, transfer learning effects, and predictive reliability.
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