Abdulhussien Amal S, Abdulsaddaa Ahmad T, Iqbal Kamran
Communication Technical Engineering College, Al-Furat Al-Awsat Technical University, Al-Najaf 54001, Iraq.
Department of System Engineering, University of Arkansas at Little Rock, Little Rock, AR 72204, USA.
J Biomed Res. 2022 Jan 10;36(1):48-57. doi: 10.7555/JBR.36.20210124.
Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram (EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform (SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response (IIR) structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.
自动癫痫发作检测对于快速检测癫痫发作非常重要，因为专家在长信号中标记和搜索癫痫发作的方式需要时间。自动检测癫痫发作最常用的方法是使用脑电图（EEG）。许多研究使用了需要时间进行计算的特征提取方法。在本研究中，滑动离散傅里叶变换（SDFT）被应用于无需加窗即可转换到频域，并与使用窗口进行特征选择进行了比较。通过使用简单的无限脉冲响应（IIR）结构，直接对每个时间序列样本计算SDFT，没有任何延迟。使用波恩大学的EEG数据库来测试所提出的方法，并定义了两种情况来检验一个双分类器前馈神经网络和一个基于自适应网络的模糊推理系统。结果表明，无延迟时的最大准确率为93%，延迟一秒时的最大准确率为99.8%。这种延迟是因为对一秒窗口的结果进行了平均。
