计算机科学
发作性
人工智能
支持向量机
癫痫
模式识别(心理学)
深度学习
机器学习
语音识别
神经科学
生物
作者
David Geng,Ayham Alkhachroum,Manuel Melo Bicchi,Jonathan Jagid,Iahn Cajigas,Zhe Chen
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2021-03-26
卷期号:18 (5): 056015-056015
被引量:45
标识
DOI:10.1088/1741-2552/abf28e
摘要
Abstract Objective. Automatic detection of interictal epileptiform discharges (IEDs, short as ‘spikes’) from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs based on scalp or intracranial electroencephalogram (iEEG) may facilitate online seizure monitoring and closed-loop neurostimulation. Approach. We developed a new deep learning approach, which employs a long short-term memory network architecture (‘IEDnet’) and an auxiliary classifier generative adversarial network (AC-GAN), to train on both expert-annotated and augmented spike events from iEEG recordings of epilepsy patients. We validated our IEDnet with two real-world iEEG datasets, and compared IEDnet with the support vector machine (SVM) and random forest (RF) classifiers on their detection performances. Main results. IEDnet achieved excellent cross-validated detection performances in terms of both sensitivity and specificity, and outperformed SVM and RF. Synthetic spike samples augmented by AC-GAN further improved the detection performance. In addition, the performance of IEDnet was robust with respect to the sampling frequency and noise. Furthermore, we demonstrated the cross-institutional generalization ability of IEDnet while testing between two datasets. Significance. IEDnet achieves excellent detection performances in identifying interictal spikes. AC-GAN can produce augmented iEEG samples to improve supervised deep learning.
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