The automated detection technique becomes the inexorable trend of medical development of the world. The objective of the work is to explore a feasible approach for patient-specific seizure detection in long-term electroencephalogram (EEG) recordings. For this purpose, a novel method based on nonlinear mode decomposition (NMD) has been proposed in this study. A sliding window is used on the multi-channel EEG, where four selected channels have been segmented into a series of successive short epochs with a 2-s duration. Then, the EEG is decomposed into a set of nonlinear modes (NMs) by the NMD algorithm and one type of statistical parameter named fractional central moment (FCM) is calculated over the first two NMs constituting the input feature vector to be fed to three common classifiers. The proposed features, when using K nearest neighbor (KNN), are able to detect seizures with high sensitivity values across all patients consistently. We have explored the ability of the FCM in NMD domain for classification of seizure and non-seizure EEG signals. Our approach has achieved the average sensitivity, specificity, and accuracy values as 98.40%, 99.10%, and 98.61%, respectively, over all the data groups on CHB-MIT database. The experimental results have indicated that the proposed method is not only quite reliable in diagnosing seizure with single type of feature yielding satisfied performance but also robust to variations of seizure types among patients. In this regard, it can be expected that our proposed method is endowed with promising prospects for the use of an expert software application in real-time automated seizure detection.

自动化检测技术成为世界医学发展的必然趋势。这项工作的目的是探索一种可行的方法，用于在长期脑电图 (EEG) 记录中检测患者特异性癫痫发作。为此，本研究提出了一种基于非线性模式分解（NMD）的新方法。在多通道脑电图中使用了一个滑动窗口，其中四个选定的通道被分割成一系列持续时间为 2 秒的连续短历元。然后，通过 NMD 算法将 EEG 分解为一组非线性模式 (NM)，并在构成输入特征向量的前两个 NM 上计算一种称为分数中心矩 (FCM) 的统计参数，并将其馈送到三个公共分类器。建议的特征，当使用K最近邻 (KNN) 能够始终如一地检测所有患者的高灵敏度癫痫发作。我们已经探索了 FCM 在 NMD 域中对癫痫发作和非癫痫发作 EEG 信号进行分类的能力。我们的方法在 CHB-MIT 数据库的所有数据组中分别实现了平均灵敏度、特异性和准确度值分别为 98.40%、99.10% 和 98.61%。实验结果表明，所提出的方法不仅在诊断癫痫发作方面非常可靠，具有单一类型的特征产生令人满意的性能，而且对患者癫痫发作类型的变化也具有鲁棒性。在这方面，可以预期我们提出的方法在实时自动癫痫检测中使用专家软件应用程序具有广阔的前景。