Ryu Kyungmoo, Sahadevan Jayakumar, Khrestian Celeen M, Stambler Bruce S, Waldo Albert L
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
J Cardiovasc Electrophysiol. 2006 Feb;17(2):198-206. doi: 10.1111/j.1540-8167.2005.00320.x.
Different analysis techniques have been developed to help understand and characterize the mechanisms responsible for atrial arrhythmias. We tested the hypothesis that Fast Fourier Transform (FFT) analysis of recorded atrial electrograms (AEGs) will rapidly and accurately identify regular and irregular patterns of atrial activation, and, thereby, may provide evidence suggestive of underlying mechanisms of atrial tachyarrhythmias.
During induced atrial tachyarrhythmias in both the canine sterile pericarditis model and canine rapid ventricular pacing-induced congestive heart failure model; 380-404 AEGs were recorded simultaneously from epicardial electrodes on both atria. From AEGs, atrial activation sequences were determined during atrial flutter (AFL), focal atrial tachycardia (AT), and atrial fibrillation (AF). Four-second recording segments of each AEG were subjected to FFT analysis. Frequencies found during FFT analyses in all studies precisely corresponded to the cycle lengths of the AEGs. In AFL and AT, one dominant frequency peak was found throughout both atria. In AF due to multiple unstable reentry circuits, multiple and broad frequency peaks were found in both atria. In AF due to a stable rapid rhythm (driver) in the left atrium with fibrillatory conduction to the rest of the atria, one dominant frequency peak in areas with 1:1 conduction from the driver, and multiple and/or broad frequency peaks in areas with fibrillatory conduction produced by the driver were found. Computation time for all FFT analyses took <5 minutes.
FFT analysis accurately and rapidly identifies global atrial activation patterns during AFL, AT, and AF, thereby assisting in determining arrhythmia mechanisms.
已经开发出不同的分析技术来帮助理解和表征导致房性心律失常的机制。我们检验了这样一个假设,即对记录的心房电图(AEG)进行快速傅里叶变换(FFT)分析将快速且准确地识别心房激动的规则和不规则模式,从而可能提供提示房性快速性心律失常潜在机制的证据。
在犬无菌性心包炎模型和犬快速心室起搏诱导的充血性心力衰竭模型中诱发房性快速性心律失常期间,同时从两个心房的心外膜电极记录380 - 404个AEG。从AEG中确定心房扑动(AFL)、局灶性房性心动过速(AT)和心房颤动(AF)期间的心房激动序列。对每个AEG的4秒记录片段进行FFT分析。在所有研究的FFT分析中发现的频率与AEG的周期长度精确对应。在AFL和AT中,整个两个心房均发现一个主导频率峰值。在由多个不稳定折返环引起的AF中,两个心房均发现多个且宽泛的频率峰值。在由左心房稳定的快速节律(驱动灶)伴向其余心房的颤动样传导引起的AF中,在来自驱动灶1:1传导的区域发现一个主导频率峰值,在由驱动灶产生的颤动样传导区域发现多个和/或宽泛的频率峰值。所有FFT分析的计算时间均小于5分钟。
FFT分析可准确且快速地识别AFL、AT和AF期间的整体心房激动模式,从而有助于确定心律失常机制。