Automatic seizure detection with different time delays using SDFT and time-domain feature extraction
Amal S.Abdulhussien1
Ahmad T.AbdulSaddaa1
Kamran Iqbal2
1.Communication Technical Engineering College,Al-Furat Al-Awsat Technical University,Al-Najaf 54001,Iraq2.Department of System Engineering,University of Arkansas at Little Rock,Little Rock,AR 72204,USA
摘要：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.
机标关键词：
分类号：R742.1(神经病学与精神病学)
论文发表日期：2022-01-30
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：10( 48-57 )
英文信息