Epileptic Seizure Prediction Using Attention Augmented Convolutional Network.

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Neural Systems Pub Date : 2023-11-01 Epub Date: 2023-09-07 DOI:10.1142/S0129065723500545
Dongsheng Liu, Xingchen Dong, Dong Bian, Weidong Zhou
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Abstract

Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network (CNN) to address the issue of CNN's limit of capturing global information of input signals. First, data enhancement is performed on original EEG recordings to balance the pre-ictal and inter-ictal EEG data, and the EEG recordings are sliced into 6-second-long EEG segments. Subsequently, EEG time-frequency distribution is obtained using Stockwell transform (ST), and the attention augmented convolutional network is employed for feature extraction and classification. Finally, post-processing is utilized to reduce the false prediction rate (FPR). The CHB-MIT EEG database was used to evaluate the system. The validation results showed a segment-based sensitivity of 98.24% and an event-based sensitivity of 94.78% with a FPR of 0.05/h were yielded, respectively. The satisfying results of the proposed method demonstrate its possible potential for clinical applications.

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使用注意力增强卷积网络预测癫痫发作。
早期癫痫发作预测对于癫痫患者减少意外伤害和提高生活质量至关重要。从发作间状态识别发作前脑电图特别具有挑战性,因为它们具有非发作性质和显著的相似性。本研究提出了一种新的基于多头注意力(MHA)增强卷积神经网络(CNN)的癫痫发作预测方法,以解决CNN在捕捉输入信号的全局信息方面的局限性问题。首先,对原始脑电图记录进行数据增强,以平衡发作前和发作间脑电图数据,并将脑电图记录切片为6秒长的脑电图片段。随后,使用Stockwell变换(ST)获得EEG的时频分布,并使用注意力增强卷积网络进行特征提取和分类。最后,利用后处理来降低错误预测率(FPR)。CHB-MIT脑电图数据库用于评估该系统。验证结果显示,FPR为0.05/h时,基于片段的灵敏度分别为98.24%和94.78%。所提出的方法的令人满意的结果证明了其可能的临床应用潜力。
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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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