Epileptic Seizure Prediction Method Based on Empirical Mode Decomposition and Kolmogorov Complexity

王晶 1; 徐光华 2; 张庆 1

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Epileptic Seizure Prediction Method Based on Empirical Mode Decomposition and Kolmogorov Complexity

更新时间：2025-12-08

- Epileptic Seizure Prediction Method Based on Empirical Mode Decomposition and Kolmogorov Complexity
- Vol. 41, Issue 11, Pages: 1364-1367+1386(2007)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： R318.04
- Online First：10 November 2007，
  
  Published：2007
- 稿件说明：
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王晶 1, 徐光华 2, 张庆 1. Epileptic Seizure Prediction Method Based on Empirical Mode Decomposition and Kolmogorov Complexity[J]. 2007, 41(11): 1364-1367+1386.
DOI：

王晶 1, 徐光华 2, 张庆 1. Epileptic Seizure Prediction Method Based on Empirical Mode Decomposition and Kolmogorov Complexity[J]. 2007, 41(11): 1364-1367+1386. DOI：

摘要

针对头皮脑电信噪比低的缺点

提出了一种新的癫痫发作预测算法.首先对头皮脑电进行经验模态分解

去除伪差

保留包含主要癫痫预测信息的固有模态分量

然后用Kolmogorov测度来反映大脑的非线性动力学特征变化

并发现在癫痫发作之前

仅位于病灶区域附近导联的Kolmogorov测度明显降低.通过对3例癫痫病人共5段长程头皮脑电信号的分析表明

这3例病人的平均发作预测时间为338 s

敏感性为66.7%

特异性为19.2%

因此该算法具有良好的临床应用前景.

Abstract

Aiming at the deficiency of scalp electroencephalogram(EEG)

a new epileptic seizure prediction method is proposed

where the empirical mode decomposition(EMD)is utilized to remove the artifacts in scalp EEG signals

and the intrinsic mode functions containing the essential information to epileptic seizure prediction is retained. Then

Kolmogorov complexity is employed to reflect the non-linear dynamic characteristics of brains

which reveals that only the Kolmogorov complexity of electrodes near the epileptogenic area decreases significantly before seizures. The algorithm is validated by the clinical data collected from 3 epilepsy patients. The results show that the average prediction period gets 338 seconds

the mean sensitivity reaches to 66.7% and the specificity 19.2%.

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references

Iasemidis L D, Shiau D S, Pardalos P M, et al. Long-term prospective on-line real-time seizure prediction [J]. Clinical Neurophysiology, 2005,116(3): 532-544.

Alessandro M D, Esteller R, Vachtsevanos G, et al. Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients [J]. IEEE Trans on Biomedical Engineering, 2003,50(5): 603-615.

Huang N E, Shen Zheng, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proc R Soc Lond: A, 1998, 454(1971): 903-995.

Lempel A, Ziv J. On the complexity of finite sequences [J]. IEEE Trans on Information Theory, 1976, 22(1): 75-81.

Jia Wenyan, Kong Na, Li Fei, et al. An epileptic seizure prediction algorithm based on second-order complexity measure [J]. Physiological Measurement, 2005,26(5): 609-625.

大雄辉熊. 脑电图判读step by step入门篇 [M]. 周锦华, 译. 北京:科学出版社,2001:143-154.

白冬梅,邱天爽,鲍海平.基于经验模式分解与样本熵的癫痫预测方法 [J].中国生物医学工程学报, 2006,25(5):527-531.

Bai Dongmei, Qiu Tianshuang, Bao Haiping. A new epileptic prediction method based on EMD and sample entropy [J]. Chinese Journal of Biomedical Engineering, 2006,25(5): 527-531.

Mormann F, Elger C E, Lehnertz K. Seizure anticipation: from algorithm to clinical practice [J]. Current Opinion in Neurology, 2006,19(2): 187-193.

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