Tiwari Ashwani Kumar, Pachori Ram Bilas, Kanhangad Vivek, Panigrahi Bijaya Ketan
IEEE J Biomed Health Inform. 2017 Jul;21(4):888-896. doi: 10.1109/JBHI.2016.2589971. Epub 2016 Jul 11.
The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.
脑电图（EEG）信号常用于癫痫的诊断。在本文中，我们提出了一种基于脑电图的癫痫自动诊断新方法。我们的方法包括使用高斯滤波信号差分金字塔在脑电图信号的多个尺度上检测关键点。在这些关键点处计算局部二值模式（LBP），并将这些模式的直方图视为特征集，将其输入支持向量机（SVM）以对脑电图信号进行分类。针对四个著名的分类问题对所提出的方法进行了研究，即：1）正常与癫痫发作，2）癫痫发作与无发作，3）正常、癫痫发作与无发作，以及4）使用公开可用的波恩大学脑电图数据库对癫痫发作与非癫痫发作脑电图信号进行分类。我们在分类准确率方面的实验结果已与针对上述问题分类的现有方法进行了比较。此外，在另一个脑电图数据集上的性能评估表明，我们的方法对于癫痫发作和无癫痫发作脑电图信号的分类是有效的。基于在关键点处计算的LBP所提出的方法简单且易于实现，可用于实时癫痫发作检测。
