Feng Lei, Ju Ki Hwan, Chon Ki H
Dept. of Biomed. Eng., State Univ. of New York, Stony Brook, NY, USA.
Conf Proc IEEE Eng Med Biol Soc. 2004;2004:869-72. doi: 10.1109/IEMBS.2004.1403296.
A method to identify switching dynamics in time series, based on annealed competition of experts algorithm (ACE), has been developed by J. Kohlmorgen, et al (2000). Incorrect selection of embedding dimension and time delay of the signal significantly affect the performance of the ACE method, however. We utilize systematic approaches based on mutual information and false nearest neighbor to determine appropriate embedding dimension and time delay. Moreover, we obtained further improvements to the original ACE method by incorporating a phase space closeness measure during the training procedure as well as deterministic annealing approach. Using these ameliorated implementations, we have enhanced the performance of the ACE algorithm in determining the location of the switching of dynamic modes in time series. The application of the improved ACE method to RR interval data obtained from rats during control and administration of double autonomic blockade conditions indicate that the improved ACE algorithm is able to segment dynamic mode changes with pinpoint accuracy and that its performance is far superior to the original ACE algorithm.
J. 科尔摩根等人（2000年）开发了一种基于专家退火竞争算法（ACE）来识别时间序列中切换动态的方法。然而，信号嵌入维度和时间延迟的错误选择会显著影响ACE方法的性能。我们利用基于互信息和虚假最近邻的系统方法来确定合适的嵌入维度和时间延迟。此外，我们通过在训练过程中纳入相空间接近度度量以及确定性退火方法，对原始ACE方法进行了进一步改进。使用这些改进后的实现方式，我们提高了ACE算法在确定时间序列中动态模式切换位置方面的性能。将改进后的ACE方法应用于在双重自主神经阻滞条件下从大鼠获得的RR间期数据，结果表明改进后的ACE算法能够极其精确地划分动态模式变化，其性能远远优于原始ACE算法。
