Geva A B, Kerem D H
Electrical and Computer Engineering Department, Ben Gurion University of the Negev, Beer Sheva, Israel.
IEEE Trans Biomed Eng. 1998 Oct;45(10):1205-16. doi: 10.1109/10.720198.
Dynamic state recognition and event-prediction are fundamental tasks in biomedical signal processing. We present a new, electroencephalogram (EEG)-based, brain-state identification method which could form the basis for forecasting a generalized epileptic seizure. The method relies on the existence in the EEG of a preseizure state, with extractable unique features, a priori undefined. We exposed 25 rats to hyperbaric oxygen until the appearance of a generalized EEG seizure. EEG segments from the preexposure, early exposure, and the period up to and including the seizure were processed by the fast wavelet transform. Features extracted from the wavelet coefficients were imputed to the unsupervised optimal fuzzy clustering (UOFC) algorithm. The UOFC is useful for classifying similar discontinuous temporal patterns in the semistationary EEG to a set of clusters which may represent brain-states. The unsupervised selection of the number of cluster overcomes the a priori unknown and variable number of states. The usually vague brain state transitions are naturally treated by assigning each temporal pattern to one or more fuzzy clusters. The classification succeeded in identifying several, behavior-backed, EEG states such as sleep, resting, alert and active wakefulness, as well as the seizure. In 16 instances a preseizure state, lasting between 0.7 and 4 min was defined. Considerable individual variability in the number and characteristics of the clusters may postpone the realization of an early universal epilepsy warning. University may not be crucial if using a dynamic version of the UOFC which has been taught the individual's normal vocabulary of EEG states and can be expected to detect unspecified new states.
动态状态识别和事件预测是生物医学信号处理中的基本任务。我们提出了一种基于脑电图（EEG）的新型脑状态识别方法，该方法可为预测全身性癫痫发作奠定基础。该方法依赖于脑电图中存在的发作前状态，其具有可提取的独特特征，且事先未定义。我们将25只大鼠暴露于高压氧环境中，直至出现全身性脑电图癫痫发作。对暴露前、早期暴露以及直至癫痫发作（包括癫痫发作期）的脑电图片段进行快速小波变换处理。从小波系数中提取的特征被输入到无监督最优模糊聚类（UOFC）算法中。UOFC可用于将半平稳脑电图中类似的不连续时间模式分类到一组可能代表脑状态的聚类中。对聚类数量的无监督选择克服了状态数量事先未知且可变的问题。通过将每个时间模式分配到一个或多个模糊聚类中，自然地处理了通常模糊的脑状态转换。该分类成功识别了几种有行为支持的脑电图状态，如睡眠、静息、警觉和主动觉醒，以及癫痫发作。在16个实例中，定义了一个持续0.7至4分钟的发作前状态。聚类数量和特征存在相当大的个体差异，这可能会推迟早期通用癫痫预警的实现。如果使用已学习个体正常脑电图状态词汇表且有望检测未指定新状态的UOFC动态版本，通用性可能并不关键。
