A Mutual Information-Based Many-Objective Optimization Method for EEG Channel Selection in the Epileptic Seizure Prediction Task

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Najwa Kouka, Rahma Fourati, Asma Baghdadi, Patrick Siarry, M. Adel
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Abstract

Epileptic seizure prediction using multi-channel electroencephalogram (EEG) signals is very important in clinical therapy. A large number of channels lead to high computational complexity with low model performance. To improve the performance and reduce the overfitting that arises due to the use of unrelevant channels, the present paper proposed a channel selection method to study the brain region activation related to epileptic seizure. Our method is bio-inspired and cognitive since it integrates the novel binary many-objective particle swarm optimization with a ConvLSTM model. The proposed method has two advantages. First, it performed a new initialization strategy based on channel weighting with mutual information, thereby promoting the fast convergence of the optimization algorithm. Second, it captures spatio-temporal information from raw EEG segments thanks to the ConvLSTM model. The selected sub-channels are optimized as many-objective optimization problem that includes maximizing F1-score, sensitivity, specificity, and minimizing the ratio rate of selected channels. Our results have shown a performance of up to \(97.94\%\) with only one EEG channel. Interestingly, when using all the EEG channels available, lower performance was achieved compared to the case when EEG channels were selected by our approach. This study revealed that it is possible to predict epileptic seizures using a few channels, which provides evidence for the future development of portable EEG seizure prediction devices.

Abstract Image

基于互信息的多目标优化方法,用于癫痫发作预测任务中的脑电图信道选择
利用多通道脑电图(EEG)信号预测癫痫发作在临床治疗中非常重要。大量通道导致计算复杂度高,模型性能低。为了提高模型性能,减少因使用无关通道而导致的过拟合,本文提出了一种通道选择方法,用于研究与癫痫发作相关的脑区激活。我们的方法融合了新颖的二元多目标粒子群优化和 ConvLSTM 模型,因此具有生物启发和认知的特点。所提出的方法有两个优点。首先,它采用了基于信道加权与互信息的新初始化策略,从而促进了优化算法的快速收敛。其次,由于采用了 ConvLSTM 模型,它能从原始脑电图片段中捕捉时空信息。所选子通道的优化是一个多目标优化问题,包括最大化 F1 分数、灵敏度、特异性和最小化所选通道的比率。我们的研究结果表明,仅使用一个脑电图通道,性能可达(97.94%/)。有趣的是,当使用所有可用的脑电图通道时,与通过我们的方法选择脑电图通道的情况相比,取得的性能较低。这项研究揭示了使用几个通道预测癫痫发作是可能的,这为未来开发便携式脑电图癫痫发作预测设备提供了证据。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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