Munia Munawara Saiyara, Hosseini MohammadSaleh, Nourani Mehrdad, Harvey Jay
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782120.
Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur in between seizures in Epilepsy patients. Automated detection of IEDs is crucial for assisting clinicians in epilepsy diagnosis as they can help identify the extent of cortical irritations and may indicate an upcoming seizure, thus helping in preventing seizure. It also minimizes visual inspection of very long EEG signals by physicians. This paper presents a transfer-learning-based approach for analyzing time-frequency representations of different types of IEDs from scalp EEG data using a fine-tuned deep residual network. The proposed method was evaluated using the publicly available Temple University Events EEG dataset. Experimental results show that our method demonstrates promising performance, by achieving an F1-score of 88.52% on this dataset for binary classification of IEDs.
发作间期癫痫样放电（IEDs）是癫痫患者发作间期间歇性出现的电生理事件。自动检测IEDs对于协助临床医生进行癫痫诊断至关重要，因为它们有助于识别皮质刺激的程度，并可能预示即将发作，从而有助于预防发作。它还最大限度地减少了医生对非常长的脑电图信号的目视检查。本文提出了一种基于迁移学习的方法，使用微调的深度残差网络分析头皮脑电图数据中不同类型IEDs的时频表示。使用公开可用的天普大学事件脑电图数据集对所提出的方法进行了评估。实验结果表明，我们的方法表现出了良好的性能，在该数据集上对IEDs进行二元分类时，F1分数达到了88.52%。
