Sharma Aarti, Rai J K, Tewari R P
a Department of ECE , Inderprastha Engineering College , Ghaziabad , India.
b Department of ECE , ASET, Amity University , Noida , India.
J Med Eng Technol. 2018 Apr;42(3):203-216. doi: 10.1080/03091902.2018.1464074. Epub 2018 May 25.
Electric activity of brain gets disturbed prior to epileptic seizure onset. Early prediction of an upcoming seizure can help to increase effectiveness of antiepileptic drugs. The scalp electroencephalogram signals contain information about the dynamics of brain and have been used to predict an upcoming seizure and localise its zone. The objective of this paper is to localise the epileptogenic region and predict an upcoming seizure at the earliest. To localise epileptogenic region, Electroencephalogram signals are categorised into four regions of brain (Frontal, Temporal, Parietal and Central). For each signal seventy-two (72) parameters in frequency domain have been extracted by using ten minute non overlapping window. Four prominent ratio parameters, γ1/γ5, γ3/γ1, θ/γ2 and γ4/θ have been identified as best parameters based on relative fisher score. Zone 2 shows the highest change in all the parameters as compared to the other zones. So, temporal region is identified as the epileptogenic region in this work. For prediction of the epileptic seizure machine learning algorithm artificial neural network (ANN) is proposed. The proposed machine learning algorithm has an accuracy of 92.3%, sensitivity of 100% and specificity of 83.3%.
在癫痫发作开始之前，大脑的电活动会受到干扰。对即将到来的癫痫发作进行早期预测有助于提高抗癫痫药物的疗效。头皮脑电图信号包含有关大脑动态的信息，并已被用于预测即将到来的癫痫发作及其发作区域。本文的目的是最早定位癫痫源区并预测即将到来的癫痫发作。为了定位癫痫源区，脑电图信号被分为大脑的四个区域（额叶、颞叶、顶叶和中央区）。对于每个信号，通过使用十分钟的非重叠窗口在频域中提取了七十二（72）个参数。基于相对费舍尔分数，四个突出的比率参数γ1/γ5、γ3/γ1、θ/γ2和γ4/θ被确定为最佳参数。与其他区域相比，区域2在所有参数中显示出最大的变化。因此，在这项工作中，颞叶区域被确定为癫痫源区。为了预测癫痫发作，提出了机器学习算法人工神经网络（ANN）。所提出的机器学习算法的准确率为92.3%，灵敏度为100%，特异性为83.3%。
