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使用 -NN 分类器通过分析时间序列脑电图信号预测癫痫发作

Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using -NN Classifier.

作者信息

Hasan Md Kamrul, Ahamed Md Asif, Ahmad Mohiuddin, Rashid M A

机构信息

Department of EEE, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.

FSTK, University Sultan Zainal Abidin (UniSZA), 21300 Kuala Terengganu, Terengganu, Malaysia.

出版信息

Appl Bionics Biomech. 2017;2017:6848014. doi: 10.1155/2017/6848014. Epub 2017 Aug 13.

DOI:10.1155/2017/6848014
PMID:28894351
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5574243/
Abstract

Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistical analysis of EEG signal that is capable of recognizing epileptic seizure with a high degree of accuracy and helps to provide automatic detection of epileptic seizure for different ages of epilepsy. To accomplish the target research, we extract various epileptic features namely approximate entropy (ApEn), standard deviation (SD), standard error (SE), modified mean absolute value (MMAV), roll-off (), and zero crossing (ZC) from the epileptic signal. The -nearest neighbours (-NN) algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to the age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG.

摘要

脑电图信号是一种代表信号，包含有关大脑活动的信息，由于癫痫发作是由大脑电生理活动紊乱引起的，因此该信号用于癫痫检测。癫痫发作的预测通常需要对脑电图进行详细且有经验的分析。在本文中，我们介绍了一种脑电图信号的统计分析方法，该方法能够高度准确地识别癫痫发作，并有助于为不同年龄段的癫痫患者提供癫痫发作的自动检测。为了完成目标研究，我们从癫痫信号中提取各种癫痫特征，即近似熵（ApEn）、标准差（SD）、标准误差（SE）、修正平均绝对值（MMAV）、滚降（）和过零率（ZC）。然后使用k近邻（k-NN）算法进行癫痫分类，再使用回归分析来预测不同年龄段患者的癫痫水平。利用统计参数和回归分析，提出了一个原型数学模型，该模型有助于找到不同受试者年龄相关的癫痫随机性。这个原型方程的准确性取决于对癫痫脑电图动态信息的恰当分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/736988d8cf8b/ABB2017-6848014.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/367b4822715e/ABB2017-6848014.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/127ed107a6d4/ABB2017-6848014.004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/40af39c4e7dd/ABB2017-6848014.007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/367b4822715e/ABB2017-6848014.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/2ce5a0cbe6ec/ABB2017-6848014.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/56bab5e3d794/ABB2017-6848014.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/127ed107a6d4/ABB2017-6848014.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/a524f99ce801/ABB2017-6848014.005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8475/5574243/736988d8cf8b/ABB2017-6848014.008.jpg

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