Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan.
Data and Signal Processing Research Group, Department of Engineering, University of Vic - Central University of Catalonia, Barcelona, Spain.
J Neural Eng. 2022 Nov 18;19(6). doi: 10.1088/1741-2552/aca04f.
. Identifying the seizure onset zone (SOZ) in patients with focal epilepsy is the critical information required for surgery. However, collecting this information is challenging, time-consuming, and subjective. Some machine learning methods reduce the workload of clinical experts in intracranial electroencephalogram (iEEG) visual diagnosis but face significant challenges because interictal iEEG clinical data often suffer from a significant class imbalance. We aim to generate synthetic data for the minority class.. To make the clinically imbalanced data suitable for machine learning, we introduce an EEG augmentation method (EEGAug). The EEGAug method randomly selects several samples from the minority class and transforms them into the frequency domain. Then, different frequency bands from different samples are used to compose new data. Finally, a synthetic sample is generated after converting the new data back to the time domain.. The imbalanced clinical iEEG data can be balanced and applied to machine learning models using the method. A one-dimensional convolutional neural network model is used to classify the SOZ and non-SOZ data. We compare the EEGAug method with other data augmentation methods and another method of class-balanced focal loss function, which is also used for solving the data imbalance problem by adjusting the weights between the minority and majority classes. The results show that the EEGAug method performs best in most data.. Data imbalance is a widespread clinical problem. The EEGAug method can flexibly generate synthetic data for the minority class, yielding synthetic and raw data with a high distribution similarity. By using the EEGAug method, clinical data can be used in machine learning models.
. 确定局灶性癫痫患者的癫痫发作起始区（SOZ）是手术所需的关键信息。然而，收集这些信息具有挑战性、耗时且主观。一些机器学习方法可以减轻临床专家在颅内脑电图（iEEG）视觉诊断中的工作量，但面临着重大挑战，因为发作间期 iEEG 临床数据通常存在严重的类别不平衡。我们旨在为少数类别生成合成数据。为了使临床不平衡数据适用于机器学习，我们引入了一种脑电图增强方法（EEGAug）。EEGAug 方法从少数类别中随机选择几个样本，并将其转换为频域。然后，从不同的样本中使用不同的频带来组成新的数据。最后，将新数据转换回时域后，生成一个合成样本。该方法可以平衡不平衡的临床 iEEG 数据，并将其应用于机器学习模型。使用一维卷积神经网络模型对 SOZ 和非 SOZ 数据进行分类。我们将 EEGAug 方法与其他数据增强方法和另一种类平衡焦点损失函数方法进行了比较，该方法也通过调整少数类和多数类之间的权重来解决数据不平衡问题。结果表明，EEGAug 方法在大多数数据中表现最好。数据不平衡是一个普遍存在的临床问题。EEGAug 方法可以灵活地为少数类别生成合成数据，生成的合成数据和原始数据具有高度的分布相似性。通过使用 EEGAug 方法，可以将临床数据用于机器学习模型。
