Epilepsy is a type of brain disorder triggered by an abrupt electrical imbalance of neuronal networks. An electroencephalogram (EEG) is a diagnostic tool to capture the underlying brain mechanisms and detect seizure onset in epileptic patients. To detect seizures, neurologists need to manually monitor EEG recordings for long periods, which is challenging and susceptible to errors depending on expertise and experience. Therefore, automatic identification of seizure and seizure-free EEG signals becomes essential. This study introduces a method based on the features extracted from the phase space reconstruction for classifying seizure and seizure-free EEG signals. The computed features are derived from the elliptical area and interquartile range of the Euclidean distance by varying percentage values of data points ranging from 50 to 100%. We consider two public datasets and evaluate these features in each EEG epoch that includes the healthy, interictal, preictal, and ictal stages of epileptic subjects, utilizing the K-nearest neighbor classifier for classification. Results show that the features have higher values during the seizure than the seizure-free EEG signals and healthy subjects. Furthermore, the proposed features can effectively discriminate seizure EEG signals from the seizure-free and normal subjects with 100% accuracy, sensitivity, and specificity in both datasets. Likewise, the classification between the preictal stage and seizure EEG signals attains 98% accuracy. Overall, the reconstructed phase space features significantly enhance the accuracy of detecting epileptic EEG signals compared with existing methods. This advancement holds great potential in assisting neurologists in swiftly and accurately diagnosing epileptic seizures from EEG signals.

癫痫是一种由神经元网络突然电失衡引发的脑部疾病。脑电图 (EEG) 是一种诊断工具，可捕获潜在的大脑机制并检测癫痫患者的癫痫发作。为了检测癫痫发作，神经科医生需要长时间手动监测脑电图记录，这具有挑战性，并且根据专业知识和经验容易出错。因此，自动识别癫痫发作和无癫痫发作的脑电图信号变得至关重要。本研究介绍了一种基于相空间重建提取的特征的方法，用于对癫痫发作和无癫痫发作的脑电图信号进行分类。计算的特征是通过改变数据点的百分比值（范围从 50% 到 100%）从椭圆面积和欧几里得距离的四分位数范围导出的。我们考虑两个公共数据集，并利用 K 最近邻分类器进行分类，评估每个脑电图时期的这些特征，包括癫痫受试者的健康、发作间期、发作前和发作期。结果表明，癫痫发作期间这些特征比无癫痫发作的脑电图信号和健康受试者具有更高的值。此外，所提出的特征可以有效地区分癫痫发作脑电图信号与无癫痫发作和正常受试者，在两个数据集中具有 100% 的准确性、敏感性和特异性。同样，发作前阶段和癫痫发作脑电图信号之间的分类准确率达到 98%。总体而言，与现有方法相比，重建的相空间特征显着提高了检测癫痫脑电图信号的准确性。这一进展在协助神经科医生根据脑电图信号快速准确地诊断癫痫发作方面具有巨大潜力。