Forecasting epilepsy from the heart rate signal
Forecasting epilepsy from the heart rate signal
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DOI：
10.1007/bf02345960
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发表时间：
2005-03-01
影响因子：
3.2
通讯作者：
Geva, AB
中科院分区：
文献类型：
--
作者：
Kerem, DH;Geva, AB
通过将无监督模糊聚类算法应用于N个连续间隔持续时间或持续时间差的绝对值的N维相空间来寻求特定于发作前期的R-R间隔序列中所包含的信息。数据来源是颞叶癫痫患者的个别、复杂的部分性发作和高压氧致癫痫大鼠的全身性发作。在癫痫发作前10分钟至30秒的时间范围内，预测成功率分别为86%和82%（耐药大鼠为零假阳性）。虽然某些预测集群在患者组中占主导地位，而不同的预测集群在动物组中占主导地位，但总体上预测是特定的。这种方法的高预测灵敏度，这与基于EEG的方法相匹配，似乎很有前途。据信，在线版本的算法，训练每个病人的发作期心电图，可以作为一个简单的癫痫报警系统的基础。
Information contained in the R-R interval series, specific to the pre-ictal period, was sought by applying an unsupervised fuzzy clustering algorithm to the N-dimensional phase space of N consecutive interval durations or the absolute value of duration differences. Data sources were individual, complex partial seizures of temporal-lobe epileptics and generalised seizures of rats rendered epileptic with hyperbaric oxygen. Forecasting success was 86% and 82% (zero false positives in resistant rats), respectively, at times ranging from 10 min to 30 s prior to seizure onset. Although certain forecasting clusters predominated in the patient group and different ones predominated in the animal group, forecasting on the whole was seizure-specific. The high prediction sensitivity of this method, which matches that of EEG-based methods, seems promising. It is believed that an on-line version of the algorithm, trained on each patient's peri-ictal ECG, could serve as a basis for a simple seizure alarm system.