Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53715, USA.
IEEE Trans Biomed Eng. 2012 Apr;59(4):1125-34. doi: 10.1109/TBME.2012.2184796. Epub 2012 Jan 18.
Detection and analysis of epileptic seizures is of clinical and research interest. We propose a novel seizure detection and analysis scheme based on the phase-slope index (PSI) of directed influence applied to multichannel electrocorticogram data. The PSI metric identifies increases in the spatio-temporal interactions between channels that clearly distinguish seizure from interictal activity. We form a global metric of interaction between channels and compare this metric to a threshold to detect the presence of seizures. The threshold is chosen based on a moving average of recent activity to accommodate differences between patients and slow changes within each patient over time. We evaluate detection performance over a challenging population of five patients with different types of epilepsy using a total of 47 seizures in nearly 258 h of recorded data. Using a common threshold procedure, we show that our approach detects all of the seizures in four of the five patients with a false detection rate less than two per hour. A variation on the global metric is proposed to identify which channels are strong drivers of activity in each patient. These metrics are computationally efficient and suitable for real-time application.
癫痫发作的检测和分析具有临床和研究意义。我们提出了一种基于有向影响的相位斜率指数（PSI）的新型癫痫发作检测和分析方案，应用于多通道脑电数据。PSI 度量可以识别通道之间时空相互作用的增加，这些增加可以清楚地区分癫痫发作和发作间期活动。我们形成了一个通道之间相互作用的全局度量，并将该度量与一个阈值进行比较，以检测癫痫发作的存在。该阈值是基于最近活动的移动平均值选择的，以适应患者之间的差异和每个患者随时间的缓慢变化。我们使用 47 次癫痫发作和近 258 小时的记录数据，评估了 5 名具有不同类型癫痫的挑战性患者群体中的检测性能。使用常见的阈值处理程序，我们表明我们的方法可以检测到五名患者中的四名患者的所有癫痫发作，每小时的假阳性率小于两次。提出了一种全局度量的变体，以识别每个患者中哪些通道是活动的主要驱动因素。这些度量计算效率高，适合实时应用。
