癫痫是一种由过度放电引起的慢性疾病。目前,临床专家通过基于长时间颅内脑电图(iEEG)的视觉判断来识别癫痫发作区(SOZ)通道,这是非常耗时的,困难和基于经验的任务。因此,需要高精度的诊断辅助工具来减少临床专家的工作量。在这篇文章中,我们提出了一种方法,iEEG被分成20秒段,对于每个病人,我们要求临床专家标记一部分数据,用于训练模型并对剩余的iEEG数据进行分类。近年来,机器学习方法已成功应用于解决一些医学问题。过滤,熵和短时傅里叶变换(STFT)用于提取特征。我们将它们与小波变换(WT)进行比较,经验模态分解(EMD)和其他传统方法,旨在获得最佳的判别特征。最后,我们寻找他们的医学解释,这对临床专家来说很重要。我们通过使用标记的iEEG数据和支持向量机(SVM)来实现SOZ和非SOZ数据分类的高性能结果,全连接神经网络(FCNN)和卷积神经网络(CNN)作为分类模型。此外,我们引入了积极的无标签(PU)学习,以进一步减少临床专家的工作量。通过使用PU学习,我们可以学习一个有少量标记数据和大量无标记数据的二进制分类器。这可以大大减少临床专家注释工作的数量和难度。一起,我们显示,使用105分钟的标记数据,我们获得了多个患者平均91.46%的分类结果.
Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (
iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the
iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining
iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled
iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients.