Kroll M W, Fulton K W
AngeMed, Plymouth, Minnesota 55447.
J Electrocardiol. 1992;24 Suppl:97-101. doi: 10.1016/s0022-0736(10)80025-1.
Various studies have shown that a low variability in heart rate is associated with increased risk of ventricular fibrillation. Low chaotic (correlation) dimension in the heart rate also appears to predict fibrillation risk. However, these results have been based on intergroup comparisons and have not been found useful for predicting when a patient may fibrillate with any degree of sensitivity, specificity, or temporal accuracy. There are two primary limitations in using dimensional analysis to predict imminent fibrillation. The first is that the standard algorithms (for correlation dimension) assume stationarity of the system. The second limitation is that these algorithms require 10,000-50,000 data points to achieve good accuracy. Thus, even if stationarity were not an issue, there would be a lag of 2.4-12 hours to warn of impending fibrillation. An algorithm has been developed to calculate an accurate pointwise correlation dimension of heart rate data. The slope filtered pointwise correlation dimension algorithm requires as few as 1,000 points of data. Using this algorithm, it was found that the correlation dimension dropped from 2.50 +/- 0.81 to 1.07 +/- 0.18 in the minute before fibrillation in conscious pigs with an occluded coronary artery. In clinical studies, Holter tapes from patients that had suffered fatal fibrillation were also analyzed along with healthy controls and nonfibrillation ventricular patients. The fibrillation patients all had excursions of low dimension (less than 1.5), while the majority of the others did not. In the minutes before fibrillation, the correlation dimension dropped to a steady range of 0.8-1.3. Drops in the slope filtered pointwise correlation dimension appear to predict fibrillation in animals and patients.
多项研究表明，心率变异性低与心室颤动风险增加有关。心率的低混沌（相关性）维度似乎也能预测颤动风险。然而，这些结果是基于组间比较得出的，尚未发现对预测患者何时会发生任何程度的颤动具有敏感性、特异性或时间准确性。使用维度分析来预测即将发生的颤动存在两个主要局限性。第一个是标准算法（用于相关性维度）假定系统是平稳的。第二个局限性是这些算法需要10000 - 50000个数据点才能达到良好的准确性。因此，即使平稳性不是问题，在警告即将发生的颤动时也会有2.4 - 12小时的延迟。已经开发出一种算法来计算心率数据的精确逐点相关性维度。斜率滤波逐点相关性维度算法所需的数据点少至1000个。使用该算法发现，在冠状动脉闭塞的清醒猪发生颤动前一分钟，相关性维度从2.50±0.81降至1.07±0.18。在临床研究中，还对发生致命颤动的患者的动态心电图磁带以及健康对照和非颤动性心室患者进行了分析。发生颤动的患者的维度波动均较低（小于1.5），而其他大多数人则没有。在颤动前的几分钟内，相关性维度降至0.8 - 1.3的稳定范围。斜率滤波逐点相关性维度的下降似乎可以预测动物和患者的颤动。
