Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.
Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy.
复制标题
DOI：
10.1371/journal.pone.0133900
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Worrell GA
中科院分区：
文献类型：
--
作者：
Brinkmann BH;Patterson EE;Vite C;Vasoli VM;Crepeau D;Stead M;Howbert JJ;Cherkassky V;Wagenaar JB;Litt B;Worrell GA
一个可靠的警报系统能够在癫痫发作前提醒患者，使患者能够调整活动或药物，这将极大地有助于对耐药局灶性癫痫的管理。这样的系统需要成功地识别癫痫或癫痫易发状态。采用支持向量机（SVM）算法研究了自然癫痫犬连续长时程颅内脑电图（iEEG）记录中预测状态的识别。研究犬植入16通道动态脑电图记录装置，平均（st. dev.）为380.4（+87.5）天，产生220.2（+104.1）天的颅内脑电图，记录在400 Hz下进行分析。脑电图记录有51.6次（+52.8次）癫痫发作，其中35.8次（+30.4次）癫痫发作前有超过4小时的无癫痫发作数据。记录的脑电图数据被分层成11个连续的、不重叠的频段，并被分类成1分钟的同步特征进行分析。使用5倍交叉验证方法评估SVM分类器的性能，其中预测训练数据取自90分钟窗口，具有5分钟的预发作偏移量。通过在一系列预测窗口内重复交叉验证并比较结果来分析最佳预测训练时间。我们发现，特征选择的优化在每个受试者中都有所不同，即算法是特定于受试者的，但在分析的5/5只狗中，其预测性能明显优于时间匹配泊松随机预测器（p<0.05）。
Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (p<0.05) in 5/5 dogs analyzed.