Epilepsy is a neurological disorder that has severely affected many people's lives across the world. Electroencephalogram (EEG) signals are used to characterize the brain's state and detect various disorders. The EEG signals are non-stationary and non-linear in nature. Therefore, it is challenging to accurately process and learn from the recorded EEG signals in order to detect disorders like epilepsy. This paper proposed an automated learning framework using the Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) method for detecting epileptic seizures from EEG signals. The scale-space boundary detection method was adopted to segment the Fourier-Bessel series expansion (FBSE) spectrum of multiple frame-size time-segmented EEG signals. Multiple frame-size time-segmented EEG signal's analysis was done using four different frame sizes: full, half, quarter, and half-quarter length of recorded EEG signals. Two different time-segmentation approaches were investigated on EEG signals: 1) segmenting signals based on multiple frame-size and 2) segmenting signals based on multiple frame-size with zero-padding the remaining signal. The FBSE-EWT method was applied to decompose the EEG signals into narrow sub-band signals. Features such as line-length (LL), log-energy-entropy (LEnt), and norm-entropy (NEnt) were computed from various frequency range sub-band signals. The relief-F feature ranking method was employed to select the most significant features; this reduces the computational burden of the models. The top-ranked accumulated features were used for classification using least square-support machine learning (LS-SVM), support vector machine (SVM), k-nearest neighbor (k-NN), and ensemble bagged tree classifiers. The proposed framework for epileptic seizure detection was evaluated on two publicly available benchmark EEG datasets: the Bonn EEG dataset and Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), well known as the CHB-MIT scalp EEG dataset. Training and testing of the models were performed using the 10-fold cross-validation technique. The FBSE-EWT based learning framework was compared with other state-of-the-art methods using both datasets. Experimental results showed that the proposed framework achieved 100 % classification accuracy on the Bonn EEG dataset, whereas 99.84 % classification accuracy on the CHB-MIT scalp EEG dataset.

癫痫是一种神经系统疾病，严重影响了世界各地许多人的生活。脑电图 (EEG) 信号用于表征大脑状态并检测各种疾病。EEG 信号本质上是非平稳和非线性的。因此，准确处理和学习记录的 EEG 信号以检测癫痫等疾病具有挑战性。本文提出了一种使用基于傅立叶-贝塞尔级数展开的经验小波变换 (FBSE-EWT) 方法从 EEG 信号检测癫痫发作的自动学习框架。采用尺度空间边界检测方法对多帧时间分段脑电信号的傅里叶-贝塞尔级数展开(FBSE)谱进行分段。多帧大小的时间分段脑电信号' s 分析是使用四种不同的帧大小完成的：记录的 EEG 信号的全长、半长、四分之一长和四分之一长。对 EEG 信号研究了两种不同的时间分割方法：1) 基于多个帧大小的分割信号和 2) 基于多个帧大小的分割信号，并对剩余信号进行零填充。应用 FBSE-EWT 方法将 EEG 信号分解为窄子带信号。诸如线长 (LL)、对数能量熵 (LEnt) 和范数熵 (NEnt) 等特征是从各种频率范围子带信号中计算出来的。采用relief-F特征排序法选取最显着的特征；这减少了模型的计算负担。使用最小二乘支持机器学习 (LS-SVM) 将排名靠前的累积特征用于分类，k -最近邻 ( k -NN) 和集成袋装树分类器。提出的癫痫发作检测框架在两个公开可用的基准 EEG 数据集上进行了评估：波恩 EEG 数据集和波士顿儿童医院 (CHB) 和麻省理工学院 (MIT)，即众所周知的 CHB-MIT 头皮 EEG 数据集。使用 10 倍交叉验证技术对模型进行训练和测试。使用两个数据集将基于 FBSE-EWT 的学习框架与其他最先进的方法进行比较。实验结果表明，所提出的框架在波恩脑电图数据集上实现了 100% 的分类准确率，而在 CHB-MIT 头皮脑电图数据集上实现了 99.84% 的分类准确率。