Epilepsy is a neurological disease that is very common worldwide. Patient’s electroencephalography (EEG) signals are frequently used for the detection of epileptic seizure segments. In this paper, a high-resolution time-frequency (TF) representation called Synchrosqueezing Transform (SST) is used to detect epileptic seizures. Two different EEG data sets, the IKCU data set we collected, and the publicly available CHB-MIT data set are analyzed to test the performance of the proposed model in seizure detection. The SST representations of seizure and nonseizure (pre-seizure or inter-seizure) EEG segments of epilepsy patients are calculated. Various features like higher-order joint TF (HOJ-TF) moments and gray-level co-occurrence matrix (GLCM)-based features are calculated using the SST representation. By using single and ensemble machine learning methods such as k-Nearest Neighbor (kNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Boosted Trees (BT), and Subspace kNN (S-kNN), EEG features are classified. The proposed SST-based approach achieved 95.1% ACC, 96.87% PRE, 95.54% REC values for the IKCU data set, and 95.13% ACC, 93.37% PRE, 90.30% REC values for the CHB-MIT data set in seizure detection. Results show that the proposed SST-based method utilizing novel TF features outperforms the short-time Fourier transform (STFT)-based approach, providing over 95% accuracy for most cases, and compares well with the existing methods.

中文翻译：

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使用同步压缩变换和机器学习对癫痫脑电信号进行分类

癫痫是一种在世界范围内非常普遍的神经系统疾病。患者的脑电图 (EEG) 信号经常用于检测癫痫发作段。在本文中，使用称为同步压缩变换 (SST) 的高分辨率时频 (TF) 表示来检测癫痫发作。分析了两个不同的 EEG 数据集，我们收集的 IKCU 数据集和公开的 CHB-MIT 数据集，以测试所提出的模型在癫痫发作检测中的性能。计算癫痫患者的癫痫发作和非癫痫发作（癫痫发作前或癫痫发作间）EEG 段的 SST 表示。使用 SST 表示计算各种特征，如高阶联合 TF (HOJ-TF) 矩和基于灰度共生矩阵 (GLCM) 的特征。ķ-最近的邻居 （ķNN)、逻辑回归 (LR)、朴素贝叶斯 (NB)、支持向量机 (SVM)、增强树 (BT) 和子空间ķ神经网络 (S-ķNN)，EEG 特征被分类。所提出的基于 SST 的方法在癫痫检测中实现了 IKCU 数据集的 95.1% ACC、96.87% PRE、95.54% REC 值，以及 CHB-MIT 数据集的 95.13% ACC、93.37% PRE、90.30% REC 值。结果表明，所提出的基于 SST 的方法利用新的 TF 特征优于基于短时傅里叶变换 (STFT) 的方法，在大多数情况下提供超过 95% 的准确度，并且与现有方法相比具有很好的性能。