Epileptic Seizure Detection Based on Partial Directed Coherence Analysis
Epileptic Seizure Detection Based on Partial Directed Coherence Analysis
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基于部分定向相干分析的癫痫发作检测
DOI：
10.1109/jbhi.2015.2424074
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发表时间：
2016-05
影响因子：
7.7
通讯作者：
Xiangguo Yan
中科院分区：
文献类型：
--
作者：
Gang Wang;Zhongjiang Sun;Ran Tao;Kuo Li;Gang Bao;Xiangguo Yan
长期的视频脑电癫痫监测可以帮助医生诊断和治疗癫痫。通过癫痫发作的自动检测，可以有效减少医生读取癫痫患者脑电信号的工作量。将部分定向相干(PDC)分析作为特征提取机制应用于癫痫发作检测的头皮脑电记录，可以反映癫痫发作前后脑电活动的生理变化。本研究提出了一种新的基于PDC的癫痫发作间期检测方法。首先，建立移动窗口的多元自回归模型，并基于PDC分析计算信息流的方向和强度。然后，通过对传播到其他脑电通道的信息流的强度进行汇总，得到与某个脑电通道相关的流出信息，从而降低特征维度。最后，根据癫痫发作的病理特征，将外流信息作为支持向量机分类器的输入向量，用于区分癫痫发作间歇期和发作间歇期。该方法的准确率为98.3%，选择性为67.88%，灵敏度为91.44%，特异度为99.34%，平均检测率为95.39%，适用于癫痫患者发作间期的检测。与其他已有的方法相比，基于PDC分析的癫痫发作检测方法在癫痫发作检测方面取得了显著的改进。
Long-term video EEG epilepsy monitoring can help doctors diagnose and cure epilepsy. The workload of doctors to read the EEG signals of epilepsy patients can be effectively reduced by automatic seizure detection. The application of partial directed coherence (PDC) analysis as mechanism for feature extraction in the scalp EEG recordings for seizure detection could reflect the physiological changes of brain activity before and after seizure onsets. In this study, a new approach on the basis of PDC was proposed to detect the seizure intervals of epilepsy patients. First of all, the multivariate autoregressive model was established for a moving window and the direction and intensity of information flow based on PDC analysis was calculated. Then, the outflow information related to certain EEG channel could be obtained by summing up the intensity of information flow propagated to other EEG channels in order to reduce the feature dimensionality. At last, according to the pathological features of epileptic seizures, the outflow information was regarded as the input vectors to a support vector machine classifier for discriminating interictal periods and ictal periods of EEG signals. The proposed method had achieved a good performance with the correct rate of 98.3%, the selectivity rate of 67.88%, the sensitivity rate of 91.44%, the specificity rate of 99.34%, and the average detection rate of 95.39%, which demonstrated that this method was suitable for detecting the seizure intervals of epilepsy patients. By comparing with other existing techniques, the proposed method based on PDC analysis achieved significant improvement in terms of seizure detection.
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DOI：
10.1017/cbo9780511801389.013
发表时间：
2000-03
期刊：
--
影响因子：
--
作者：
N. Cristianini;J. Shawe-Taylor
通讯作者：
N. Cristianini;J. Shawe-Taylor
影响因子：
8.5
作者：
Altunay, Semih;Telatar, Ziya;Erogul, Osman
通讯作者：
Erogul, Osman
利用多重分形奇点谱对表面肌电信号进行分类
DOI：
10.1007/s11517-012-0990-9
发表时间：
2013-03
期刊：
Medical & Biological Engineering & Computing
影响因子：
--
作者：
Wang, Gang;Ren, Doutian
通讯作者：
Ren, Doutian
影响因子：
7.7
作者：
A. Subasi
通讯作者：
A. Subasi
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H