Deep learning has achieved expert-level performance in automated electrocardiogram (ECG) diagnosis, yet the "black-box" nature of these models hinders their clinical deployment. Trust in medical AI requires not just high accuracy but also transparency regarding the specific physiological features driving predictions. Existing explainability methods for ECGs typically rely on post-hoc approximations (e.g., Grad-CAM and SHAP), which can be unstable, computationally expensive, and unfaithful to the model's actual decision-making process. In this work, we propose the ECG-IMN, an Interpretable Mesomorphic Neural Network tailored for high-resolution 12-lead ECG classification. Unlike standard classifiers, the ECG-IMN functions as a hypernetwork: a deep convolutional backbone generates the parameters of a strictly linear model specific to each input sample. This architecture enforces intrinsic interpretability, as the decision logic is mathematically transparent and the generated weights (W) serve as exact, high-resolution feature attribution maps. We introduce a transition decoder that effectively maps latent features to sample-wise weights, enabling precise localization of pathological evidence (e.g., ST-elevation, T-wave inversion) in both time and lead dimensions. We evaluate our approach on the PTB-XL dataset for classification tasks, demonstrating that the ECG-IMN achieves competitive predictive performance (AUROC comparable to black-box baselines) while providing faithful, instance-specific explanations. By explicitly decoupling parameter generation from prediction execution, our framework bridges the gap between deep learning capability and clinical trustworthiness, offering a principled path toward "white-box" cardiac diagnostics.

翻译：深度学习在自动化心电图（ECG）诊断中已达到专家级性能，但这些模型的“黑箱”特性阻碍了其临床部署。医疗人工智能要获得信任，不仅需要高精度，还需对驱动预测的具体生理特征保持透明。现有的心电图可解释性方法通常依赖于事后近似（如Grad-CAM和SHAP），这些方法可能不稳定、计算成本高，且无法忠实反映模型的实际决策过程。在本工作中，我们提出了ECG-IMN，一种专为高分辨率12导联心电图分类设计的可解释中态神经网络。与标准分类器不同，ECG-IMN作为一个超网络运行：一个深度卷积主干网络为每个输入样本生成严格线性模型的参数。该架构强制实现了内在可解释性，因为其决策逻辑在数学上是透明的，且生成的权重（W）可作为精确的高分辨率特征归因图。我们引入了一个过渡解码器，能有效将潜在特征映射到样本级权重，从而在时间和导联维度上精确定位病理证据（如ST段抬高、T波倒置）。我们在PTB-XL数据集上评估了该方法在分类任务中的表现，结果表明ECG-IMN在提供忠实、实例特异性解释的同时，实现了具有竞争力的预测性能（AUROC与黑箱基线模型相当）。通过明确将参数生成与预测执行解耦，我们的框架弥合了深度学习能力与临床可信度之间的差距，为“白箱”心脏诊断提供了一条原则性路径。