Bhadra Rajdeep, Singh Pawan Kumar, Mahmud Mufti
Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, 700 106, Kolkata, West Bengal, India.
Metharath University, 99, Moo 10, Bang Toei, Sam Khok, 12160, Pathum Thani, Thailand.
Brain Inform. 2024 Aug 21;11(1):21. doi: 10.1186/s40708-024-00234-x.
Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain.
癫痫发作（ES）检测是一个活跃的研究领域，其目标是从脑电图（EEG）信号中高精度地进行针对特定患者的ES检测。癫痫发作的早期检测对于及时进行医疗干预和预防患者进一步受伤至关重要。这项工作提出了一个名为HyEpiSeiD的强大深度学习框架，该框架使用卷积神经网络与两个门控循环单元层的混合组合从预处理后的EEG信号中提取自训练特征，并基于这些提取的特征进行预测。所提出的HyEpiSeiD框架在两个公共数据集UCI癫痫数据集和Mendeley数据集上进行了评估。所提出的HyEpiSeiD模型分别实现了99.01%和97.50%的分类准确率，优于癫痫检测领域的大多数现有方法。
