School of Sciences, University of Southern Queensland, Australia; Thi-Qar University, College of Education for Pure Science, Iraq.
School of Sciences, University of Southern Queensland, Australia; School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China.
Brain Res. 2022 Mar 15;1779:147777. doi: 10.1016/j.brainres.2022.147777. Epub 2022 Jan 6.
The detection of epileptic seizures from electroencephalogram (EEG) signals is traditionally performed by clinical experts through visual inspection. It is a long process, is error prone, and requires a highly trained expert. In this research, a new method is presented for seizure classification for EEG signals using a dual-tree complex wavelet transform (DT-CWT) and fast Fourier transform (FFT) coupled with a least square support vector machine (LS-SVM) classifier. In this method, each EEG signal is divided into four segments. Each segment is further split into smaller sub-segments. The DT-CWT is applied to decompose each sub-segment into detailed and approximation coefficients (real and imaginary parts). The obtained coefficients by the DT-CWT at each decomposition level are passed through an FFT to identify the relevant frequency bands. Finally, a set of effective features are extracted from the sub-segments, and are then forwarded to the LS-SVM classifier to classify epileptic EEGs. In this paper, two epileptic EEG databases from Bonn and Bern Universities are used to evaluate the extracted features using the proposed method. The experimental results demonstrate that the method obtained an average accuracy of 97.7% and 96.8% for the Bonn and Bern databases, respectively. The results prove that the proposed DT-CWT and FFT based features extraction is an effective way to extract discriminative information from brain signals. The obtained results are also compared to those by k-means and Naïve Bayes classifiers as well as with the results from the previous methods reported for classifying epileptic seizures and identifying the focal and non-focal EEG signals. The obtained results show that the proposed method outperforms the others and it is effective in detecting epileptic seziures in EEG signals. The technique can be adopted to aid neurologists to better diagnose neurological disorders and for an early seizure warning system.
从脑电图 (EEG) 信号中检测癫痫发作传统上是由临床专家通过视觉检查来完成的。这是一个漫长的过程，容易出错，并且需要高度训练有素的专家。在这项研究中，提出了一种使用双树复小波变换 (DT-CWT) 和快速傅里叶变换 (FFT) 与最小二乘支持向量机 (LS-SVM) 分类器相结合的新方法，用于对 EEG 信号进行癫痫发作分类。在这种方法中，每个 EEG 信号被分为四个部分。每个部分进一步细分为较小的子部分。DT-CWT 用于将每个子部分分解为详细和近似系数 (实部和虚部)。在每个分解水平上由 DT-CWT 获得的系数通过 FFT 传递以识别相关频带。最后，从子部分中提取一组有效特征，并将其转发到 LS-SVM 分类器以对癫痫 EEG 进行分类。在本文中，使用来自波恩大学和伯尔尼大学的两个癫痫 EEG 数据库来评估使用所提出的方法提取的特征。实验结果表明，该方法分别获得了波恩和伯尔尼数据库的平均准确率为 97.7%和 96.8%。结果证明，基于 DT-CWT 和 FFT 的特征提取方法是从脑信号中提取鉴别信息的有效方法。获得的结果还与 k-均值和朴素贝叶斯分类器的结果以及之前用于分类癫痫发作和识别焦点和非焦点 EEG 信号的方法的结果进行了比较。获得的结果表明，该方法优于其他方法，并且在检测 EEG 信号中的癫痫发作方面非常有效。该技术可以被采用来帮助神经病学家更好地诊断神经障碍和进行早期癫痫预警系统。
