In this study, techniques were proposed for the detection of epileptic seizures from electroencephalogram (EEG) signals using the wavelet transform (WT), peak extraction and phase–space reconstruction (PSR) based Euclidean distances. In the first step, the wavelet coefficients were extracted after eliminating the noise from the EEG signals using a WT, which is a widely used signal processing technique. In the second step, the peaks were extracted from the wavelet coefficients. In the third step, the continuous peaks that were extracted were mapped to 3D coordinates using PSR. In the fourth step, the Euclidean distances between the mapped 3D coordinates and the origin were obtained. The features of the Euclidean distances obtained were extracted using statistical techniques. The final features extracted were used as inputs to the neural network with weighted fuzzy membership (NEWFM). NEWFM contains the bounded sum of weighted fuzzy memberships (BSWFMs) that can reveal the differences in the graphic characteristics between normal EEG signals and epileptic-seizure EEG signals. The BSWFMs can easily be embedded in a portable device to detect epileptic seizures from EEG signals in real life.

中文翻译：

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使用小波变换、峰值提取和 EEG 信号的 PSR 检测癫痫发作

在这项研究中，提出了使用小波变换 (WT)、峰值提取和基于相空间重建 (PSR) 的欧几里德距离从脑电图 (EEG) 信号检测癫痫发作的技术。在第一步中，使用广泛使用的信号处理技术 WT 从 EEG 信号中去除噪声后提取小波系数。第二步，从小波系数中提取峰值。在第三步中，提取的连续峰使用 PSR 映射到 3D 坐标。在第四步中，获得映射的 3D 坐标和原点之间的欧几里德距离。使用统计技术提取获得的欧几里得距离的特征。提取的最终特征用作具有加权模糊隶属度（NEWFM）的神经网络的输入。NEWFM 包含加权模糊隶属度 (BSWFM) 的有界总和，可以揭示正常 EEG 信号和癫痫发作 EEG 信号之间图形特征的差异。BSWFM 可以轻松嵌入便携式设备中，以在现实生活中通过 EEG 信号检测癫痫发作。