论文标题
使用脑电图实时癫痫发作检测：在现实环境下对最新方法的全面比较
Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting
论文作者
论文摘要
脑电图（EEG）是一项重要的诊断测试，医生用来通过监测信号来记录大脑活动并检测癫痫发作。已经有几次尝试检测具有现代深度学习模型的EEG信号中的癫痫发作和异常，以减轻临床负担。但是，由于在不同的实验环境中进行了测试，因此不能相互比较。另外，其中一些没有接受实时癫痫发作检测任务的培训，因此很难进行设备应用程序。因此，在这项工作中，我们首次使用各种评估指标，包括新的评估指标，包括我们建议评估癫痫发作检测模型的更实际方面的新评估指标，在实时癫痫发作检测框架中进行了广泛比较多个最先进的模型和信号提取器。我们的代码可从https://github.com/aitrics/eeg_real_time_seizure_detection获得。
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for on-device applications. Therefore in this work, for the first time, we extensively compare multiple state-of-the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models. Our code is available at https://github.com/AITRICS/EEG_real_time_seizure_detection.
