Herrero Martin Clara, Oved Alon, Chowdhury Rasheda A, Ullmann Elisabeth, Peters Nicholas S, Bharath Anil A, Varela Marta
Department of Bioengineering, Imperial College London, London, United Kingdom.
ITACA Institute, Universitat Politècnica de València, Valencia, Spain.
Front Cardiovasc Med. 2022 Feb 3;8:768419. doi: 10.3389/fcvm.2021.768419. eCollection 2021.
Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation. It is, however, notoriously difficult to perform. We present EP-PINNs (Physics Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation from sparse amounts of EP data. We demonstrate, using 1D and 2D data, how EP-PINNs are able to reconstruct the spatio-temporal evolution of action potentials, whilst predicting parameters related to action potential duration (APD), excitability and diffusion coefficients. EP-PINNs are additionally able to identify heterogeneities in EP properties, making them potentially useful for the detection of fibrosis and other localised pathology linked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological preparations, by characterising the effect of anti-arrhythmic drugs on APD using optical mapping data. EP-PINNs are a promising clinical tool for the characterisation and potential treatment guidance of arrhythmias.
从动作电位记录中准确推断潜在的电生理（EP）组织特性，有望在心房颤动等心律失常的诊断和治疗中发挥临床作用。然而，这一过程非常困难。我们提出了EP-PINNs（物理信息神经网络），这是一种用于从少量EP数据中进行准确动作电位模拟和EP参数估计的新型工具。我们使用一维和二维数据展示了EP-PINNs如何能够重建动作电位的时空演变，同时预测与动作电位持续时间（APD）、兴奋性和扩散系数相关的参数。EP-PINNs还能够识别EP特性中的异质性，使其有可能用于检测纤维化和其他与心律失常相关的局部病变。最后，我们通过使用光学映射数据表征抗心律失常药物对APD的影响，展示了EP-PINNs在生物制剂上的有效性。EP-PINNs是一种用于心律失常表征和潜在治疗指导的有前景的临床工具。
