Carvajal-Dossman Juan Pablo, Guio Laura, García-Orjuela Danilo, Guzmán-Porras Jennifer J, Garces Kelly, Naranjo Andres, Maradei-Anaya Silvia Juliana, Duitama Jorge
System and computing engineering department, Universidad de Los Andes, Bogota, Colombia.
HOMI, Fundación Hospital Pediátrico La Misericordia, Bogota, Colombia.
Sci Rep. 2025 May 2;15(1):15345. doi: 10.1038/s41598-025-98389-y.
Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models have been developed to perform automated detection of seizures from EEGs. However, a large gap is observed between initial accuracies and those observed in clinical practice. In this work, we reproduced and assessed the accuracy of a large number of models, including deep learning networks, for detection of seizures from EEGs. Benchmarking included three different datasets for training and initial testing, and a manually annotated EEG from a local patient for further testing. Random forest and a convolutional neural network achieved the best results on public data, but a large reduction of accuracy was observed testing with the local data, especially for the neural network. We expect that the retrained models and the data available in this work will contribute to the integration of machine learning techniques as tools to improve the accuracy of diagnosis in clinical settings.
脑电图（EEG）是癫痫诊断中最常用的技术之一。然而，EEG数据中癫痫发作的手动标注是EEG分析过程中一个主要的耗时步骤。已经开发了不同的机器学习模型来自动检测EEG中的癫痫发作。然而，初始准确率与临床实践中观察到的准确率之间存在很大差距。在这项工作中，我们重现并评估了大量模型（包括深度学习网络）从EEG中检测癫痫发作的准确率。基准测试包括三个不同的数据集用于训练和初始测试，以及一个来自当地患者的手动标注的EEG用于进一步测试。随机森林和卷积神经网络在公共数据上取得了最佳结果，但在用本地数据测试时观察到准确率大幅下降，尤其是对于神经网络。我们期望在这项工作中重新训练的模型和可用数据将有助于将机器学习技术作为工具进行整合，以提高临床环境中的诊断准确性。
