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Classification of Non-focal and Focal EEG signals using Local Binary Pattern

机译：非局灶性和局灶性脑电信号的分类使用本地二进制模式

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Electroencephalogram is a clinical diagnoses tool that monitors the electrical impulses of the cerebrum. It is used to sense the glitch in the brain due to the recurrent existence of the seizures known as epilepsy. The detection of epileptic seizures by human examination is time consuming and it results in misconception. Therefore in this paper an effective feature extraction method of local binary pattern (LBP) is introduced for the automatic identification of epilepsy to reduce the complexity of the human examination. The extracted features are classified by employing artificial neural network (ANN) classifier to discriminate non-focal and focal EEG signals. The epilepsy EEG dataset furnished by Bern- Barcelona contains 3750 pairs of EEG signals from non-focal and focal class used in this study. 10-fold cross validation is performed to evaluate the discrimination performance. The proposed method LBP with ANN classifier achieved a 93.21% of accuracy, 93.63% of specificity and sensitivity of 92.80%.
机译：脑电图是监视大脑电脉冲的临床诊断工具。它被用于感知由于癫痫发作的反复发作而导致的大脑毛刺。通过人体检查来检测癫痫发作非常耗时，并且会导致误解。因此，本文提出了一种有效的局部二值模式特征提取方法，用于癫痫的自动识别，以降低人体检查的复杂性。通过使用人工神经网络（ANN）分类器对提取的特征进行分类，以区分非聚焦和聚焦EEG信号。伯尔尼-巴塞罗那提供的癫痫性脑电数据集包含3750对本研究中使用的非焦点和焦点类脑电信号。进行10倍交叉验证以评估判别性能。所提出的带有ANN分类器的LBP方法实现了93.21％的准确度，93.63％的特异性和92.80％的灵敏度。

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