Electrocardiography (ECG) is an essential tool for diagnosing multiple cardiac anomalies: it provides valuable clinical insights, while being affordable, fast and available in many settings. However, in the current literature, the role of ECG analysis is often unclear: many approaches either rely on additional imaging modalities, such as Computed Tomography Pulmonary Angiography (CTPA), which may not always be available, or do not effectively generalize across different classification problems. Furthermore, the availability of public ECG datasets is limited and, in practice, these datasets tend to be small, making it essential to optimize learning strategies. In this study, we investigate the performance of multiple neural network architectures in order to assess the impact of various approaches. Moreover, we check whether these practices enhance model generalization when transfer learning is used to translate information learned in larger ECG datasets, such as PTB-XL and CPSC18, to a smaller, more challenging dataset for pulmonary embolism (PE) detection. By leveraging transfer learning, we analyze the extent to which we can improve learning efficiency and predictive performance on limited data. Code available at https://github.com/joaodsmarques/Are-ECGs-enough-Deep-Learning-Classifiers .

本研究解决了心电图（ECG）在诊断心脏异常时常常依赖其他成像技术的不足。通过比较多种神经网络架构并采用迁移学习方法，发现模型在小型心电图数据集上的预测性能显著提升。研究结果表明，优化学习策略可以提高心脏异常检测的效率和准确性，尤其是在数据有限的情况下。

心电图是否足够？仅使用心电图进行心脏异常的深度学习分类