Bao Forrest Sheng, Gao Jue-Ming, Hu Jing, Lie Donald Y C, Zhang Yuanlin, Oommen K J
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas 79409, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6603-7. doi: 10.1109/IEMBS.2009.5332550.
Over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy.
全球超过5000万人患有癫痫症。癫痫症的传统诊断依赖于训练有素的临床医生对冗长脑电图记录进行繁琐的视觉筛查，这些记录包含癫痫发作（发作期）活动。如今，有许多自动系统可以识别与癫痫发作相关的脑电图信号以辅助诊断。然而，获取带有癫痫发作活动的长期脑电图数据成本很高且不方便，尤其是在医疗资源匮乏的地区。我们在本文中证明，我们可以使用发作间期头皮脑电图数据来自动诊断一个人是否患有癫痫，这种数据比发作期数据更容易收集。在我们的自动脑电图识别系统中，我们从脑电图数据中提取三类特征，并构建由这些特征输入的概率神经网络（PNN）。我们优化特征提取参数，并通过投票机制将这些PNN组合起来。结果，我们的系统实现了令人印象深刻的94.07%的准确率。
