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首页> 外文会议>Asia-Pacific Signal and Information Processing Association Annual Summit and Conference >Epileptic Focus Localization Based on iEEG by Using Positive Unlabeled (PU) Learning
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Epileptic Focus Localization Based on iEEG by Using Positive Unlabeled (PU) Learning

机译：基于正面无标记（PU）学习的基于iEEG的癫痫病灶定位

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摘要

Epilepsy is a chronic disorder of the brain. Intracranial electroencephalogram (iEEG) recorded from cortex is the most popular measurement for not only the diagnosis of epilepsy, but also the focus localization that is crucial for the surgery. In recent years, the machine learning methods have been rapidly developed and applied successfully to various real world problems. Given sufficient number of samples, the powerful deep learning methods can achieve high performance for epileptic focus localization. However, it is a challenging task to obtain large amount of labeled iEEG regarding focalon-focal channels, since the annotations must be performed by multiple clinical experts through visual judgment on the long term iEEG signals. In order to reduce the necessary number of labeled training samples, we introduce the positive unlabeled (PU) learning method for classification of focal and non-focal epileptic iEEG signals. The proposed method enables us to learn a binary classifier by using small amount of labeled data containing only one class (i.e., focal signals) and unlabeled data containing two classes (i.e., focal and non-focal signals), which greatly reduces the workload of clinical experts for annotations. Experimental results on Bern dataset and iEEG recorded from Juntendo University Hospital demonstrate the effectiveness of our method.
机译：癫痫病是大脑的一种慢性疾病。从皮层记录的颅内脑电图（iEEG）是最流行的测量方法，不仅用于诊断癫痫，而且对于手术至关重要。近年来，机器学习方法得到了快速发展，并成功地应用于各种现实问题。给定足够数量的样本，强大的深度学习方法可以实现癫痫病灶定位的高性能。但是，获取有关聚焦/非聚焦通道的大量标记的iEEG是一项艰巨的任务，因为注释必须由多个临床专家通过对长期iEEG信号的视觉判断来执行。为了减少标记的训练样本的必要数量，我们引入了阳性的未标记（PU）学习方法来对局灶性和非局灶性癫痫iEEG信号进行分类。提出的方法使我们能够通过使用少量仅包含一个类别（即聚焦信号）的标记数据和包含两个类别（即聚焦和非聚焦信号）的未标记数据来学习二元分类器，从而大大减少了分类器的工作量。临床专家的注释。从Juntendo大学医院记录的Bern数据集和iEEG的实验结果证明了我们方法的有效性。

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