Department of Engineering, University of Leicester, UK; Department of Cardiovascular Science, University of Leicester, UK.
Biomedical Engineering, Center for Engineering, Modelling and Applied Social Sciences, Universidade Federal do ABC, Brazil; Bioengineering Division, Heart Institute (InCor), Brasil.
Comput Methods Programs Biomed. 2017 Apr;141:83-92. doi: 10.1016/j.cmpb.2017.01.011. Epub 2017 Jan 25.
Optimal targets for persistent atrial fibrillation (persAF) ablation are still debated. Atrial regions hosting high dominant frequency (HDF) are believed to participate in the initiation and maintenance of persAF and hence are potential targets for ablation, while rotor ablation has shown promising initial results. Currently, no commercially available system offers the capability to automatically identify both these phenomena. This paper describes an integrated 3D software platform combining the mapping of both frequency spectrum and phase from atrial electrograms (AEGs) to help guide persAF ablation in clinical cardiac electrophysiological studies.
30s of 2048 non-contact AEGs (EnSite Array, St. Jude Medical) were collected and analyzed per patient. After QRST removal, the AEGs were divided into 4s windows with a 50% overlap. Fast Fourier transform was used for DF identification. HDF areas were identified as the maximum DF to 0.25Hz below that, and their centers of gravity (CGs) were used to track their spatiotemporal movement. Spectral organization measurements were estimated. Hilbert transform was used to calculate instantaneous phase.
The system was successfully used to guide catheter ablation for 10 persAF patients. The mean processing time was 10.4 ± 1.5min, which is adequate comparing to the normal electrophysiological (EP) procedure time (120∼180min).
A customized software platform capable of measuring different forms of spatiotemporal AEG analysis was implemented and used in clinical environment to guide persAF ablation. The modular nature of the platform will help electrophysiological studies in understanding of the underlying AF mechanisms.
持续性心房颤动（persAF）消融的最佳靶点仍存在争议。高主导频率（HDF）的心房区域被认为参与了 persAF 的起始和维持，因此是消融的潜在靶点，而转子消融已显示出有希望的初步结果。目前，没有商业上可用的系统提供自动识别这两种现象的能力。本文描述了一种集成的 3D 软件平台，该平台结合了心房电图（AEGs）的频谱和相位映射，以帮助指导临床心脏电生理研究中的 persAF 消融。
每位患者采集和分析 30s 的 2048 个非接触式 AEG（EnSite Array，St. Jude Medical）。QRST 去除后，AEG 被分为 4s 窗口，重叠 50%。快速傅里叶变换用于识别 DF。HDF 区域被定义为最大 DF 至低于该值 0.25Hz 的区域，其重心（CG）用于跟踪其时空运动。估计了频谱组织测量值。希尔伯特变换用于计算瞬时相位。
该系统成功用于指导 10 例 persAF 患者的导管消融。平均处理时间为 10.4±1.5min，与正常电生理（EP）程序时间（120∼180min）相比足够。
实现了一种能够测量不同形式的时空 AEG 分析的定制软件平台，并在临床环境中用于指导 persAF 消融。该平台的模块化性质将有助于电生理研究理解潜在的 AF 机制。
