Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey.

IF 17.2 1区 工程技术 Q1 ENGINEERING, BIOMEDICAL IEEE Reviews in Biomedical Engineering Pub Date : 2024-10-25 DOI:10.1109/RBME.2024.3486439
Lei Li, Julia Camps, Blanca Rodriguez, Vicente Grau
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Abstract

Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. A critical component of CDT development is solving the ECG inverse problem, which enables the reconstruction of cardiac sources and the estimation of patient-specific electrophysiology (EP) parameters from surface ECG data. Despite challenges from complex cardiac anatomy, noisy ECG data, and the ill-posed nature of the inverse problem, recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference, strengthening the fidelity of CDTs. This paper aims to provide a comprehensive review of the methods of solving ECG inverse problem, the validation strategies, the clinical applications, and future perspectives. For the methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including both conventional and deep learning-based techniques. Integrating physics laws with deep learning models holds promise, but challenges such as capturing dynamic electrophysiology accurately, accessing accurate domain knowledge, and quantifying prediction uncertainty persist. Integrating models into clinical workflows while ensuring interpretability and usability for healthcare professionals is essential. Overcoming these challenges will drive further research in CDTs.

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解决心脏数字双胞胎心电图的逆问题:调查。
心脏数字双胞胎(CDTs)是一种个性化的虚拟表征,用于了解复杂的心脏机制。CDT 开发的一个重要组成部分是解决心电图逆问题,该问题可以重建心脏信号源,并从表面心电图数据中估算出患者特定的电生理学(EP)参数。尽管复杂的心脏解剖结构、嘈杂的心电图数据和逆问题的非假设性质带来了挑战,但计算方法的最新进展大大提高了心电图逆推理的准确性和效率,增强了 CDT 的保真度。本文旨在全面综述解决心电图逆问题的方法、验证策略、临床应用和未来展望。在方法论方面,我们将最先进的方法大致分为两类:确定性方法和概率性方法,包括传统技术和基于深度学习的技术。将物理定律与深度学习模型相结合大有可为,但在准确捕捉动态电生理学、获取准确的领域知识和量化预测不确定性等方面仍存在挑战。将模型整合到临床工作流程中,同时确保医疗保健专业人员的可解释性和可用性至关重要。克服这些挑战将推动 CDT 的进一步研究。
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来源期刊
IEEE Reviews in Biomedical Engineering
IEEE Reviews in Biomedical Engineering Engineering-Biomedical Engineering
CiteScore
31.70
自引率
0.60%
发文量
93
期刊介绍: IEEE Reviews in Biomedical Engineering (RBME) serves as a platform to review the state-of-the-art and trends in the interdisciplinary field of biomedical engineering, which encompasses engineering, life sciences, and medicine. The journal aims to consolidate research and reviews for members of all IEEE societies interested in biomedical engineering. Recognizing the demand for comprehensive reviews among authors of various IEEE journals, RBME addresses this need by receiving, reviewing, and publishing scholarly works under one umbrella. It covers a broad spectrum, from historical to modern developments in biomedical engineering and the integration of technologies from various IEEE societies into the life sciences and medicine.
期刊最新文献
A Survey of Few-Shot Learning for Biomedical Time Series. The Physiome Project and Digital Twins. Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey. Data- and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies. Exhaled Breath Analysis: from Laboratory Test to Wearable Sensing.
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