X-Beat：一种用于心电图图像分类的可解释框架
X-Beat: An Explainable Framework for ECG Image Classification
浏览论文内容
中文总结 AI 辅助
提出X-Beat框架，结合迁移学习与Grad-CAM解释及可靠性评估，用于心电图图像分类，ResNet-50达91.94%准确率，提升模型可解释性与可信度。
中文摘要 AI 辅助
准确的心电图（ECG）自动解读对于早期检测心肌梗死和心律异常等心脏疾病至关重要。然而，许多高性能深度学习模型由于透明度有限且缺乏可靠性验证，难以在临床环境中部署。在本工作中，我们提出了X-Beat，一个面向心电图图像分类的可解释且具有可靠性意识的基准框架，旨在支持医疗保健领域的可信人工智能系统。该框架将迁移学习与事后可解释性以及系统性的可靠性评估相结合，覆盖四个心脏类别：异常心跳、心肌梗死病史、心肌梗死和正常心跳。多个基于ImageNet预训练的CNN骨干网络，包括EfficientNet-B0、ResNet-50、DenseNet-121和MobileNetV3-Large，在统一的训练协议下进行了评估。除了标准性能指标外，我们还整合了基于Grad-CAM的视觉解释以及其他分析，包括解释稳定性、区域敏感性和基于置信度的可靠性评估，以检验模型预测是否得到临床相关证据的支持。实验结果表明，ResNet-50取得了最佳性能，准确率达到91.94%，宏F1分数为0.9098，且类别可分性强（AUC最高达0.995）。解释分析表明，模型主要关注波形相关区域，而可靠性评估则表明大多数错误预测发生在较低置信度下。总体而言，本工作为心电图图像分类中的预测性能和解释可靠性评估提供了一个结构化且可复现的基准，有助于为临床决策支持系统开发可信且可解释的人工智能组件。
英文摘要
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.
发表机构
- Texas A&M University-Kingsville（得克萨斯A&M大学金斯维尔分校）
机构由 AI 辅助整理，请以论文原文为准。
