Al-Adhaileh Mosleh Hmoud, Ahmad Sultan, Alharbi Alhasan A, Alarfaj Mohammed, Dhopeshwarkar Mukta, Aldhyani Theyazn H H
King Salman Center for Disability Research, Riyadh, Saudi Arabia.
Deanship of E-Learning and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
Front Med (Lausanne). 2025 May 20;12:1577474. doi: 10.3389/fmed.2025.1577474. eCollection 2025.
Affecting millions of individuals worldwide, epilepsy is a neurological condition marked by repeated convulsions. Monitoring brain activity and identifying seizures depends much on electroencephalography (EEG). An essential step that may help clinicians identify and treat epileptic seizures is the differentiation between epileptic and non-epileptic signals by use of epileptic seizure detection categorization.
In this work, we investigated Machine learning algorithms including Random Forest, Gradient Boosting, and K-Nearest Neighbors, alongside advanced DL architectures such as Long Short-Term Memory networks and Long-term Recurrent Convolutional Networks for detecting epileptic seizures in terms of difficulties and procedures evolved depending on EEG data. The EEG data classification by applying ML and DL framework to improve the accuracy of seizure detection. The EEG dataset consisted of 102 patients (55 seizure and 47 non-seizure cases), and the data underwent comprehensive preprocessing, including noise removal, frequency band extraction, and data balancing using SMOTE to address class imbalance. Key features, including delta, theta, alpha, beta, and gamma bands, as well as spectral entropy, were extracted to aid in the classification process.
A comparative analysis was conducted, resulting in high classification accuracy, with the Random Forest model achieving the best results at 99.9% accuracy.
The study demonstrates the potential of EEG data for reliable seizure detection while emphasizing the need for further development of more practical and non-invasive monitoring systems for real-world applications.
癫痫是一种影响全球数百万人的神经系统疾病，其特征为反复惊厥。监测大脑活动和识别癫痫发作很大程度上依赖于脑电图（EEG）。通过癫痫发作检测分类来区分癫痫信号和非癫痫信号是帮助临床医生识别和治疗癫痫发作的关键步骤。
在这项工作中，我们研究了包括随机森林、梯度提升和K近邻在内的机器学习算法，以及诸如长短期记忆网络和长期递归卷积网络等先进的深度学习架构，以根据脑电图数据的难度和流程来检测癫痫发作。通过应用机器学习和深度学习框架对脑电图数据进行分类，以提高癫痫发作检测的准确性。脑电图数据集由102名患者（55例癫痫发作和47例非癫痫发作病例）组成，数据经过了全面的预处理，包括去除噪声、提取频段以及使用合成少数过采样技术（SMOTE）进行数据平衡以解决类别不平衡问题。提取了包括δ、θ、α、β和γ频段以及谱熵在内的关键特征，以辅助分类过程。
进行了对比分析，分类准确率较高，随机森林模型取得了最佳结果，准确率达到99.9%。
该研究证明了脑电图数据在可靠的癫痫发作检测方面的潜力，同时强调了需要进一步开发更实用且无创的监测系统以用于实际应用。
