Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, 626005, India.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Sci Rep. 2023 Oct 18;13(1):17710. doi: 10.1038/s41598-023-44318-w.
Electroencephalogram (EEG) is one of the most common methods used for seizure detection as it records the electrical activity of the brain. Symmetry and asymmetry of EEG signals can be used as indicators of epileptic seizures. Normally, EEG signals are symmetrical in nature, with similar patterns on both sides of the brain. However, during a seizure, there may be a sudden increase in the electrical activity in one hemisphere of the brain, causing asymmetry in the EEG signal. In patients with epilepsy, interictal EEG may show asymmetric spikes or sharp waves, indicating the presence of epileptic activity. Therefore, the detection of symmetry/asymmetry in EEG signals can be used as a useful tool in the diagnosis and management of epilepsy. However, it should be noted that EEG findings should always be interpreted in conjunction with the patient's clinical history and other diagnostic tests. In this paper, we propose an EEG-based improved automatic seizure detection system using a Deep neural network (DNN) and Binary dragonfly algorithm (BDFA). The DNN model learns the characteristics of the EEG signals through nine different statistical and Hjorth parameters extracted from various levels of decomposed signals obtained by using the Stationary Wavelet Transform. Next, the extracted features were reduced using the BDFA which helps to train DNN faster and improve its performance. The results show that the extracted features help to differentiate the normal, interictal, and ictal signals effectively with 100% accuracy, sensitivity, specificity, and F1 score with a 13% selected feature subset when compared to the existing approaches.
脑电图（EEG）是最常用于检测癫痫发作的方法之一，因为它记录了大脑的电活动。EEG 信号的对称性和非对称性可以作为癫痫发作的指标。通常，EEG 信号在性质上是对称的，大脑两侧的模式相似。然而，在癫痫发作期间，大脑一侧的电活动可能会突然增加，导致 EEG 信号的不对称。在癫痫患者中，发作间期 EEG 可能会显示不对称的棘波或尖波，表明存在癫痫活动。因此，检测 EEG 信号的对称性/非对称性可以作为癫痫诊断和管理的有用工具。但是，应该注意的是，脑电图结果应始终结合患者的临床病史和其他诊断测试进行解释。在本文中，我们提出了一种基于 EEG 的改进的自动癫痫发作检测系统，该系统使用深度神经网络（DNN）和二进制蜻蜓算法（BDFA）。DNN 模型通过使用平稳小波变换获得的不同分解信号水平提取的九个不同的统计和 Hjorth 参数来学习 EEG 信号的特征。然后，使用 BDFA 减少提取的特征，这有助于更快地训练 DNN 并提高其性能。结果表明，与现有方法相比，当使用 13%的选定特征子集时，提取的特征可有效区分正常、发作间期和发作期信号，准确率、灵敏度、特异性和 F1 评分均为 100%。
