Department of Cardiology, Erasmus University Medical Centre, Rotterdam, Delft University of Technology, Delft the Netherlands.
Cardiology Service, University Hospitals Geneva, Geneva, Switzerland.
Europace. 2022 Feb 2;24(2):313-330. doi: 10.1093/europace/euab254.
We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter-electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.
我们旨在对应用于(i)心房颤动(AF)标测以揭示 AF 机制和/或确定 AF 治疗靶点和(ii)AF 检测的信号记录和处理技术的基本概念进行批判性评估,以优化技术的使用,激发旨在缩小知识差距的研究,并开发理想的 AF 记录和处理技术。评估 AF 期间电活动的记录和处理技术对于诊断和指导消融治疗至关重要,包括体表心电图(ECG)和心内膜或心外膜电图(EGM)。讨论了(i)单极、双极和多极(复极/Laplacian)记录模式的差异,(ii)记录技术对 EGM 形态的影响,(iii)使用各种类型的 EGM 进行全局或局部标测,包括等时、电压-分馏、偶极密度和转子标测等信号处理技术,能够得出心房率、熵、传导速度/方向等参数,(iv)心外膜和光学标测的价值,(v)包含适用于存储 EGM 的各种检测算法的心脏植入式电子设备检测 AF,(vi)机器学习(ML)对进一步改进信号处理技术的贡献。EGM(或 ECG)的记录和处理是 AF (体表)标测的基石。目前可用的 AF 记录和处理技术主要限于特定应用或具有技术局限性。通过获取用于信号处理(例如滤波、数字化和噪声消除)的最高保真源信号(例如导管-电极组合),对 AF 标测进行改进非常重要。新型采集仪器(与改进的物理建模和 ML 技术相结合的多极导管)将使未来能够增强和自动解释 EGM 记录。