Webber W R, Litt B, Wilson K, Lesser R P
Johns Hopkins Epilepsy Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287.
Electroencephalogr Clin Neurophysiol. 1994 Sep;91(3):194-204. doi: 10.1016/0013-4694(94)90069-8.
We have developed and tested "off-line" an artificial neural network (ANN) that successfully detects epileptiform discharges (EDs) when trained on EEG records marked by an electroencephalographer (EEGer). The system was trained on both parameterized and raw EEG data and can process 49 channels of EEG data in real time on an 80486/33 MHz personal computer, making it capable of processing EEG on-line in long-term monitoring units. Our detector consists of 2 stages: (1) a threshold detector identifies candidate EDs in 4-channel bipolar chains within the recording montage, parameterizes them and then passes these data to the second stage; (2) a 3-layer feed-forward ANN decides if a candidate wave form is an ED. The intersection of detector sensitivity and selectivity curves, or crossover threshold, for 10 patients from our Epilepsy Monitoring Unit occurred at 73% for parameterized EEG data and at 46% for "raw" EEG data. The ANN could be adapted to different EEGers' styles by changing the ANN output threshold for accepting candidate wave forms as EDs. In this "proof of principle" study the detector was trained on EEGs from 10 Johns Hopkins Hospital Epilepsy Monitoring Unit (JHH EMU) patients. We used different EEGs from the same patients for testing. Current testing should demonstrate that the ANN detector can generalize to previously "unseen" patients. This study shows that ANNs offer a practical solution for automated, real time ED detection that uses, standard, inexpensive computers, is easily adjustable to individual EEGer style and can produce sensitivities and selectivities similar to those of EEGers.
我们已经开发并“离线”测试了一种人工神经网络（ANN），该网络在由脑电图技师（EEGer）标记的脑电图记录上进行训练时，能够成功检测出癫痫样放电（EDs）。该系统在参数化和原始脑电图数据上均进行了训练，并且能够在一台80486/33 MHz的个人计算机上实时处理49通道的脑电图数据，使其能够在长期监测单元中进行在线脑电图处理。我们的检测器由两个阶段组成：（1）一个阈值检测器在记录蒙太奇中的4通道双极链中识别候选EDs，对其进行参数化，然后将这些数据传递到第二阶段；（2）一个3层前馈人工神经网络确定候选波形是否为ED。我们癫痫监测单元中10名患者的检测器灵敏度和选择性曲线的交点，即交叉阈值，对于参数化脑电图数据为73%，对于“原始”脑电图数据为46%。通过改变人工神经网络接受候选波形作为ED的输出阈值，可以使人工神经网络适应不同脑电图技师的风格。在这项“原理验证”研究中，检测器在来自10名约翰霍普金斯医院癫痫监测单元（JHH EMU）患者的脑电图上进行了训练。我们使用了同一患者的不同脑电图进行测试。当前的测试应证明人工神经网络检测器能够推广到以前“未见过”的患者。这项研究表明，人工神经网络为自动、实时的ED检测提供了一种实用的解决方案，该方案使用标准、廉价的计算机，易于根据个体脑电图技师的风格进行调整，并且能够产生与脑电图技师相似的灵敏度和选择性。
