[1]朱宁宁,李皓,邓小乔,等.基于小波包变换的癫痫脑电棘波检测[J].中国医学物理学杂志,2020,37(11):1428-1435.[doi:DOI:10.3969/j.issn.1005-202X.2020.11.016]
　ZHU Ningning,LI Hao,DENG Xiaoqiao,et al.Detection of epileptic spikes in EEG based on wavelet packet transform[J].Chinese Journal of Medical Physics,2020,37(11):1428-1435.[doi:DOI:10.3969/j.issn.1005-202X.2020.11.016]
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基于小波包变换的癫痫脑电棘波检测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第11期
页码:
1428-1435
栏目:
医学信号处理与医学仪器
出版日期:
2020-12-02

文章信息/Info

Title:
Detection of epileptic spikes in EEG based on wavelet packet transform
文章编号:
1005-202X（2020）11-1428-08
作者:
朱宁宁1; 李皓1; 邓小乔1; 于明2; 李效龙1
1.江苏科技大学电子信息学院， 江苏 镇江 212000； 2.江苏大学附属医院神经内科， 江苏 镇江 212000
Author(s):
ZHU Ningning1;  LI Hao1;  DENG Xiaoqiao1;  YU Ming2;  LI Xiaolong1
1. School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212000, China 2. Department of Neurology, Affiliated Hospital of Jiangsu University, Zhenjiang 212000, China
关键词:
癫痫棘波检测; 小波包变换; 信号重构; 漏检率; 误检率
Keywords:
Keywords: epileptic spike detection wavelet packet transform signal reconstruction missed detection rate false detection rate
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2020.11.016
文献标志码:
A
摘要:
为了能够较好地实现癫痫患者脑电的棘波检测，提出一种将棘波物理特征（幅度、频率）和小波包变换结合的算法，用于癫痫患者脑电信号的棘波检测。首先利用小波包变换对癫痫脑电信号进行小波包分解，将脑电波频率（0~30 Hz）划分为3层；其次根据脑电波的频率范围重构第三层节点频率S（3, 0）（0~10.85 Hz）、S（3, 1）（10.85~21.7 Hz）、S（3, 2）（21.7~32.55 Hz）的脑电信号；最后取棘波的幅度作为检测阈值分别提取癫痫患者健康期、癫痫发作间期及癫痫发作期的棘波。实验结果证明，当数据的采样频率为173.61 Hz、信号长度为23.6 s时，该算法能够提取不同癫痫患者在不同时期的棘波信号，该算法棘波的误检率为12.02%、漏检率为11.70%。因此，本文所采用的算法在癫痫棘波检测中具有良好的效果。
Abstract:
Abstract: An algorithm combining the physical characteristics (amplitude, frequency) of spikes and wavelet packet transform is proposed for better the spike detection in the electroencephalogram (EEG) signals of epilepsy patients. Firstly, wavelet packet transform is used to decompose the epilepsy EEG signals by wavelet packet decomposition the EEG frequency (0-30 Hz) into 3 layers. Secondly, the third layer node frequencies of S(3, 0) (0-10.85 Hz), S(3,1) (10.85-21.70 Hz), S(3,2) (21.70-32.55 Hz) are reconstructed according to the frequency range of EEG signals. Finally, the spike amplitude is taken as the detection threshold to extract the epileptic spikes in healthy period and the intermittent period between epilepsy attacks, and during epilepsy attacks. The experimental results show that when the sampling frequency of the data is 173.61 Hz and the signal length is 23.6 s, the algorithm can extract the spike signals of different epilepsy patients in different periods, and that the rates of false detection and missed detection of the proposed algorithm are 12.02% and 11.70%. The proposed algorithm has a good performance in the detection of epileptic spikes.

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[1]白鹏飞,王利,易子川,等.基于小波包变换的心电图ST段提取[J].中国医学物理学杂志,2013,30(01):3894.[doi:10.3969/j.issn.1005-202X.2013.01.014]
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备注/Memo

备注/Memo:
【收稿日期】2020-04-15 【基金项目】国家自然科学基金（61671221）；江苏省研究生科研与实践创新计划项目（KYCX19_1685） 【作者简介】朱宁宁，硕士研究生，研究方向：生物医学信号处理，E-mail: 172030023@stu.just.edu.cn 【通信作者】李效龙，副教授，硕士生导师，研究方向：微电子与生物医学信号处理，E-mail: lixiaolong@just.edu.cn
更新日期/Last Update: 2020-12-02