Department of Electrical Engineering, Foundation University, Islamabad, Pakistan.
Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan.
Comput Biol Med. 2018 Sep 1;100:10-16. doi: 10.1016/j.compbiomed.2018.06.018. Epub 2018 Jun 21.
The detection of seizure activity in electroencephalogram (EEG) segments is very important for the classification and localization of epileptic seizures. The evolution of a seizure in an EEG usually appears as a train of non-uniformly spaced spikes and/or as piecewise linear frequency modulated signals. If a seizure is present, then the energy of the EEG is concentrated along the time axis and the frequency axis in the time-frequency plane. However, in the absence of a seizure, the energy of the EEG signal is uniformly distributed along all directions in the time-frequency plane. Based on this observation, we propose a new approach for the detection of a seizure. In this paper, we develop a new feature that exploits the direction of the energy of the signal in the time-frequency domain to distinguish between seizures and non-seizures in an EEG. Our experimental results indicate the superiority of the proposed approach over other conventional time-frequency approaches; for example, the proposed feature set achieves a classification accuracy of 98.25% by only using five features.
脑电图 (EEG) 中癫痫发作的检测对于癫痫发作的分类和定位非常重要。脑电图中癫痫发作的演变通常表现为非均匀间隔的尖峰和/或分段线性调频信号。如果存在癫痫发作，则 EEG 的能量集中在时频平面的时间轴和频率轴上。然而，在没有癫痫发作的情况下，EEG 信号的能量沿时频平面的所有方向均匀分布。基于这一观察，我们提出了一种新的癫痫发作检测方法。在本文中，我们开发了一种新的特征，利用信号在时频域中的能量方向来区分 EEG 中的癫痫发作和非癫痫发作。我们的实验结果表明，与其他传统时频方法相比，所提出的方法具有优越性；例如，仅使用五个特征，所提出的特征集即可实现 98.25%的分类准确率。
