Seizures are the part of the epilepsy that occurs in central nervous system which leads to abnormal brain activity. Electroencephalogram (EEG) signal recordings are mostly used in epileptic seizure detection process. Detection of seizures is a crucial part for further treatment of patients. This paper proposes a multi-view SVM model for seizure detection using the single channel EEG signals. In this experiment, two views of the EEG data have been extracted, (1) the time domain features using Independent Component Analysis (ICA) and (2) power spectral densities are obtained in the frequency domain. Extracted features have been fed to multi-view SVM classification model. In this study, a single channel EEG dataset is used for seizure detection. Performance estimation parameters namely Accuracy, Sensitivity, Specificity, F1-score, and AUC value have been estimated for evaluating the proposed model. The model classified seizure and non-seizure over the sets A vs E and B vs E with an accuracy greater than 99% using k-fold cross validation. The classification accuracy obtained by multi-view SVM is better by 1–4% than single view SVM using the same features. Furthermore, the proposed model is also compared with existing single view SVM models. It is observed that the multi view SVM model performed significantly better, compare to a single view SVM model over the same features.

癫痫发作是发生在中枢神经系统的癫痫的一部分，会导致大脑活动异常。脑电图（EEG）信号记录主要用于癫痫发作检测过程。癫痫发作的检测是患者进一步治疗的关键部分。本文提出了一种使用单通道脑电图信号进行癫痫发作检测的多视图 SVM 模型。在本实验中，提取了 EEG 数据的两个视图，（1）使用独立分量分析（ICA）的时域特征和（2）在频域中获得功率谱密度。提取的特征已输入多视图 SVM 分类模型。在本研究中，单通道脑电图数据集用于癫痫发作检测。性能评估参数，即准确度、灵敏度、特异性、F1 分数、和 AUC 值已被估计用于评估所提出的模型。该模型使用 k 倍交叉验证对 A 组与 E 组和 B 组与 E 组中的癫痫发作和非癫痫发作进行分类，准确率超过 99%。使用相同特征，多视图 SVM 获得的分类精度比单视图 SVM 提高 1-4%。此外，所提出的模型还与现有的单视图 SVM 模型进行了比较。据观察，与相同特征的单视图 SVM 模型相比，多视图 SVM 模型的性能明显更好。使用相同特征，多视图 SVM 获得的分类精度比单视图 SVM 提高 1-4%。此外，所提出的模型还与现有的单视图 SVM 模型进行了比较。据观察，与相同特征的单视图 SVM 模型相比，多视图 SVM 模型的性能明显更好。使用相同特征，多视图 SVM 获得的分类精度比单视图 SVM 提高 1-4%。此外，所提出的模型还与现有的单视图 SVM 模型进行了比较。据观察，与相同特征的单视图 SVM 模型相比，多视图 SVM 模型的性能明显更好。