脑电图
假警报
高斯噪声
计算机科学
稳定分布
头皮
噪音(视频)
语音识别
人工智能
模式识别(心理学)
癫痫发作
数学
心理学
统计
神经科学
医学
图像(数学)
解剖
作者
Yueming Wang,Yu Qi,Yiwen Wang,Zhen Lei,Xiaoxiang Zheng,Gang Pan
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2016-08-22
卷期号:13 (5): 056009-056009
被引量:9
标识
DOI:10.1088/1741-2560/13/5/056009
摘要
There is serious noise in EEG caused by eye blink and muscle activities. The noise exhibits similar morphologies to epileptic seizure signals, leading to relatively high false alarms in most existing seizure detection methods. The objective in this paper is to develop an effective noise suppression method in seizure detection and explore the reason why it works.Based on a state-space model containing a non-linear observation function and multiple features as the observations, this paper delves deeply into the effect of the α-stable distribution in the noise suppression for seizure detection from scalp EEG. Compared with the Gaussian distribution, the α-stable distribution is asymmetric and has relatively heavy tails. These properties make it more powerful in modeling impulsive noise in EEG, which usually can not be handled by the Gaussian distribution. Specially, we give a detailed analysis in the state estimation process to show the reason why the α-stable distribution can suppress the impulsive noise.To justify each component in our model, we compare our method with 4 different models with different settings on a collected 331-hour epileptic EEG data. To show the superiority of our method, we compare it with the existing approaches on both our 331-hour data and 892-hour public data. The results demonstrate that our method is most effective in both the detection rate and the false alarm.This is the first attempt to incorporate the α-stable distribution to a state-space model for noise suppression in seizure detection and achieves the state-of-the-art performance.
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