Patel Nirav, Kay G Neal, Sanchez Javier, Ideker Raymond E, Smith William M
Department of Biomedical Engineering, University of Alabama at Birmingham, 1670 University Boulevard, Birmingham, AL 35294, USA.
J Cardiovasc Electrophysiol. 2003 Jul;14(7):698-704. doi: 10.1046/j.1540-8167.2003.03074.x.
Ablation of muscular fascicles around the ostium of pulmonary veins (PVs) resulting in electrical isolation of the veins may prove to be an effective treatment for atrial fibrillation (AF). Correctly discriminating atrial and PV potentials is necessary to effectively isolate PVs from the left atrium in patients with paroxysmal AF.
A training set of 151 electrode recordings obtained from 10 patients with AF was used to develop an algorithm to discriminate atrial and PV potentials. Bipolar electrograms were collected from a multielectrode basket catheter placed sequentially into each PV. Amplitude, slope, and normalized slopes of both bipolar and quadripolar electrograms (difference between adjacent bipoles) were entered into a binary logistic regression model. A receiver operating characteristic curve was used to define a threshold able to effectively discriminate atrial and PV potentials. The normalized slopes of both domains, bipolar and quadripolar, produced a logistic function that discriminated atrial and PV potentials against a threshold (0.38) with 97.8% sensitivity and 94.9% specificity. The algorithm then was evaluated on a test set of 214 electrode recordings from four patients who also had paroxysmal AF. These patient electrograms also were evaluated by two independent electrophysiologists. The algorithm and electrophysiologists matched identification of activation origin in 84% of electrograms.
Atrial and PV potentials acquired from a multielectrode basket catheter can be discriminated using the normalized slopes of bipolar and quadripolar electrograms. These additional parameters need to be included by physicians determining the preferential ablation site within PVs.
消融肺静脉(PV)口周围的肌束从而实现静脉电隔离可能是治疗心房颤动(AF)的一种有效方法。准确区分心房电位和肺静脉电位对于有效隔离阵发性房颤患者的肺静脉与左心房至关重要。
使用从10例房颤患者获得的151个电极记录的训练集来开发一种区分心房电位和肺静脉电位的算法。从依次放入每个肺静脉的多电极篮状导管收集双极电图。双极和四极电图的振幅、斜率以及归一化斜率(相邻双极之间的差值)被输入二元逻辑回归模型。使用受试者工作特征曲线来定义一个能够有效区分心房电位和肺静脉电位的阈值。双极和四极两个区域的归一化斜率产生了一个逻辑函数,该函数以97.8%的灵敏度和94.9%的特异性区分心房电位和肺静脉电位与阈值(0.38)。然后在来自另外4例阵发性房颤患者的214个电极记录的测试集上对该算法进行评估。这些患者的电图也由两名独立的电生理学家进行评估。该算法与电生理学家在84%的电图中对激动起源的识别相匹配。
使用双极和四极电图的归一化斜率可以区分从多电极篮状导管获取的心房电位和肺静脉电位。确定肺静脉内优先消融部位的医生需要纳入这些额外参数。