Electroencephalography (EEG) serves as a crucial neurodiagnostic tool by recording the electrical brain activity via attached electrodes on the patient’s head. While artificial intelligence (AI) exhibited considerable promise in medical diagnostics, its potential in the realm of neurodiagnostics remains underexplored. This research addresses this gap by proposing an innovative approach employing time-series classification of EEG data, leveraging long-short-term memory (LSTM) neural networks for the identification of abnormal brain activity, particularly seizures. To enhance the performance of the proposed model, metaheuristic algorithms were employed for optimizing hyperparameter collection. Additionally, a tailored modification of the variable neighborhood search (VNS) is introduced, specifically tailored for this neurodiagnostic application. The effectiveness of this methodology is evaluated using a carefully curated dataset comprising real-world EEG recordings from both healthy individuals and those affected by epilepsy. This software-based approach demonstrates noteworthy results, showcasing its efficacy in anomaly and seizure detection, even when working with relatively modest sample sizes. This research contributes to the field by illuminating the potential of AI in neurodiagnostics, presenting a methodology that enhances accuracy in identifying abnormal brain activities, with implications for improved patient care and diagnostic precision.

脑电图 (EEG) 通过连接在患者头部的电极记录脑电活动，成为重要的神经诊断工具。虽然人工智能 (AI) 在医疗诊断方面展现出巨大的前景，但其在神经诊断领域的潜力仍未得到充分开发。这项研究通过提出一种创新方法来解决这一差距，该方法采用脑电图数据的时间序列分类，利用长短期记忆 (LSTM) 神经网络来识别异常的大脑活动，特别是癫痫发作。为了提高所提出模型的性能，采用元启发式算法来优化超参数收集。此外，还引入了变量邻域搜索（VNS）的定制修改，专门针对这种神经诊断应用而定制。该方法的有效性是使用精心策划的数据集进行评估的，该数据集包括健康个体和癫痫患者的真实脑电图记录。这种基于软件的方法展示了值得注意的结果，展示了其在异常和癫痫检测方面的功效，即使在样本量相对较小的情况下也是如此。这项研究通过阐明人工智能在神经诊断方面的潜力，提出了一种提高识别异常大脑活动准确性的方法，对改善患者护理和诊断精度具有重要意义，从而为该领域做出了贡献。