The eikonal equation has become an indispensable tool for modeling cardiac
electrical activation accurately and efficiently. In principle, by matching
clinically recorded and eikonal-based electrocardiograms (ECGs), it is possible
to build patient-specific models of cardiac electrophysiology in a purely
non-invasive manner. Nonetheless, the fitting procedure remains a challenging
task. The present study introduces a novel method, Geodesic-BP, to solve the
inverse eikonal problem. Geodesic-BP is well-suited for GPU-accelerated machine
learning frameworks, allowing us to optimize the parameters of the eikonal
equation to reproduce a given ECG. We show that Geodesic-BP can reconstruct a
simulated cardiac activation with high accuracy in a synthetic test case, even
in the presence of modeling inaccuracies. Furthermore, we apply our algorithm
to a publicly available dataset of a rabbit model, with very positive results.
Given the future shift towards personalized medicine, Geodesic-BP has the
potential to help in future functionalizations of cardiac models meeting
clinical time constraints while maintaining the physiological accuracy of
state-of-the-art cardiac models.

通过匹配临床记录和基于 eikonal 的心电图 (ECG)，我们引入了一种名为 Geodesic-BP 的新方法来解决逆 eikonal 问题，该方法非常适用于 GPU 加速的机器学习框架，可以优化 eikonal 方程的参数以重现给定的 ECG。我们展示了 Geodesic-BP 在合成测试案例中能够在模型不准确的情况下准确重建模拟的心脏激活，并将我们的算法应用于公开可用的兔子模型数据集，取得了非常积极的结果。鉴于未来朝向个体化医疗的转变，Geodesic-BP 有潜力在满足临床时间约束条件的同时保持先进心脏模型的生理准确性，从而有助于未来心脏模型的功能化。

从表面心电图中数字化心脏电生理模型的测地背向传播方法

Digital twinning of cardiac electrophysiology models from the surface  ECG: a geodesic backpropagation approach

Objective. Deep neural networks (DNNs) have shown unprecedented success in
various brain-machine interface applications such as epileptic seizure
prediction. However, existing approaches typically train models in a
patient-specific fashion due to the highly personalized characteristics of
epileptic signals. Therefore, only a limited number of labeled recordings from
each subject can be used for training. As a consequence, current DNN based
methods demonstrate poor generalization ability to some extent due to the
insufficiency of training data. On the other hand, patient-independent models
attempt to utilize more patient data to train a universal model for all
patients by pooling patient data together. Despite different techniques
applied, results show that patient-independent models perform worse than
patient-specific models due to high individual variation across patients. A
substantial gap thus exists between patient-specific and patient-independent
models. Approach. In this paper, we propose a novel training scheme based on
knowledge distillation which makes use of a large amount of data from multiple
subjects. It first distills informative features from signals of all available
subjects with a pre-trained general model. A patient-specific model can then be
obtained with the help of distilled knowledge and additional personalized data.
Main results. Four state-of-the-art seizure prediction methods are trained on
the Children's Hospital of Boston-MIT sEEG database with our proposed scheme.
The resulting accuracy, sensitivity, and false prediction rate show that our
proposed training scheme consistently improves the prediction performance of
state-of-the-art methods by a large margin. Significance. The proposed training
scheme significantly improves the performance of patient-specific seizure
predictors and bridges the gap between patient-specific and patient-independent
predictors.

本文提出了一种基于知识蒸馏的新型训练方法，该方法利用来自多个受试者的大量数据，从所有可用受试者的信号中提取信息特征，并利用提取出的知识和个性化数据，获得患者特定模型，从而显著提高了患者特异性癫痫预测器的性能，填补了患者特异性和患者非特异性预测器之间的差距。