Epileptic Seizure Detection Based on EEG Signals and CNN
Epileptic Seizure Detection Based on EEG Signals and CNN
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DOI：
10.3389/fninf.2018.00095
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发表时间：
2018-12-10
影响因子：
3.5
通讯作者：
Xiang, Jie
中科院分区：
文献类型：
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作者：
Zhou, Mengni;Tian, Cheng;Xiang, Jie
癫痫是一种神经系统疾病，据世界卫生组织统计，大约有5000万人患有癫痫。虽然脑电图（EEG）在监测癫痫患者的大脑活动和诊断癫痫方面发挥着重要作用，但需要专家分析所有EEG记录以检测癫痫活动。这种方法显然是耗时和繁琐的，及时准确地诊断癫痫对于开始抗癫痫药物治疗并随后降低未来癫痫发作和癫痫相关并发症的风险至关重要。在这项研究中，一个卷积神经网络（CNN）的基础上，原始EEG信号，而不是手动特征提取被用来区分发作，发作前，发作间期段癫痫发作检测。我们比较了基于颅内弗赖堡和头皮CHB-MIT数据库的癫痫信号检测中时域和频域信号的性能，以探索这些参数的潜力。通过两个二分类问题（发作间期与发作前、发作间期与发作间期）和一个三分类问题（发作间期与发作前、发作间期与发作间期）的三组实验，探讨了该方法的可行性。使用弗赖堡数据库中的频域信号，三个实验的平均准确度分别为96.7%、95.4%和92.3%，而CHB-MIT数据库中检测的平均准确度在三个实验中分别为95.6%、97.5%和93%。使用弗赖堡数据库中的时域信号，三个实验的平均准确率分别为91.1%、83.8%和85.1%，而CHB-MIT数据库中的信号检测准确率在三个实验中仅为59.5%、62.3%和47.9%。基于这些结果，使用频域信号有效地检测到这三种情况。然而，使用时域信号作为输入样本的三种情况的有效识别仅针对部分患者实现。总的来说，频域信号的分类精度与时域信号相比显著提高。此外，对于CNN应用，频域信号比时域信号具有更大的潜力。
Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. While electroencephalography (EEG) plays important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, an expert is needed to analyze all EEG recordings to detect epileptic activity. This method is obviously time-consuming and tedious, and a timely and accurate diagnosis of epilepsy is essential to initiate antiepileptic drug therapy and subsequently reduce the risk of future seizures and seizure-related complications. In this study, a convolutional neural network (CNN) based on raw EEG signals instead of manual feature extraction was used to distinguish ictal, preictal, and interictal segments for epileptic seizure detection. We compared the performances of time and frequency domain signals in the detection of epileptic signals based on the intracranial Freiburg and scalp CHB-MIT databases to explore the potential of these parameters. Three types of experiments involving two binary classification problems (interictal vs. preictal and interictal vs. ictal) and one three-class problem (interictal vs. preictal vs. ictal) were conducted to explore the feasibility of this method. Using frequency domain signals in the Freiburg database, average accuracies of 96.7, 95.4, and 92.3% were obtained for the three experiments, while the average accuracies for detection in the CHB-MIT database were 95.6, 97.5, and 93% in the three experiments. Using time domain signals in the Freiburg database, the average accuracies were 91.1, 83.8, and 85.1% in the three experiments, while the signal detection accuracies in the CHB-MIT database were only 59.5, 62.3, and 47.9% in the three experiments. Based on these results, the three cases are effectively detected using frequency domain signals. However, the effective identification of the three cases using time domain signals as input samples is achieved for only some patients. Overall, the classification accuracies of frequency domain signals are significantly increased compared to time domain signals. In addition, frequency domain signals have greater potential than time domain signals for CNN applications.