利用传统方法和基于深度学习的方法研究脑电图信号的多维(1D、2D、3D)分析

IF 1.3 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Darshil Shah, G. Gopan K, N. Sinha
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引用次数: 5

摘要

脑电图(EEG)信号是由于认知活动在大脑中产生的电信号。它们是非侵入性的,被广泛用于评估神经退行性疾病、精神负荷和睡眠模式。在这项工作中,我们探索了在不同维度(如1d特征向量、2d特征地图和3d视频)中表示固有单维时间序列的效用。该方法应用于四种不同的数据集:1)EEG基线,2)心算,3)帕金森病和4)情绪数据集。对于一维分析,从时间序列手工制作的流行一维特征被用于分类。将这种性能与使用原始时间序列作为深度学习框架输入的数据驱动方法进行比较。为了评估2D表示的有效性,提出了结合特征金字塔网络(FPN)和空间金字塔池(ASPP)的2D特征映射。这与利用由2D特征图和1D特征组成的复合特征集的方法进行了比较。然而,这些方法不能同时利用空间、光谱和时间特征。为了解决这个问题,3D脑电图视频是通过叠加在时域内每个时间帧从每个子带获得的频谱特征图来创建的。脑电图视频是卷积神经网络(CNN)和长短期记忆(LSTM)组合的输入,用于分类。使用所提出的方法获得的性能在本工作中考虑的三个分类场景中超过了最先进的技术,即脑电图基线、心算和帕金森病。视频分析结果表明,脑电基线和脑电图心算的峰值平均准确率分别为92.5%和98.81%。另一方面,对于帕金森病与对照组的区分,使用传统方法在一维特征向量上的峰值平均准确率为88.51%。这说明3D和2D特征表示对于观察到脑激活区域地形变化的脑电图数据是有效的。然而,在地形变化在同一类科目之间不一致的情况下,这些方法失败了。另一方面,对于由于不同程度的恶化而导致大脑整体激活发生变化的情况,1D分析被证明是非常有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods
Electroencephalographic (EEG) signals are electrical signals generated in the brain due to cognitive activities. They are non-invasive and are widely used to assess neurodegenerative conditions, mental load, and sleep patterns. In this work, we explore the utility of representing the inherently single dimensional time-series in different dimensions such as 1D-feature vector, 2D-feature maps, and 3D-videos. The proposed methodology is applied to four diverse datasets: 1) EEG baseline, 2) mental arithmetic, 3) Parkinson’s disease, and 4) emotion dataset. For a 1D analysis, popular 1D features hand-crafted from the time-series are utilized for classification. This performance is compared against the data-driven approach of using raw time-series as the input to the deep learning framework. To assess the efficacy of 2D representation, 2D feature maps that utilize a combination of the Feature Pyramid Network (FPN) and Atrous Spatial Pyramid Pooling (ASPP) is proposed. This is compared against an approach utilizing a composite feature set consisting of 2D feature maps and 1D features. However, these approaches do not exploit spatial, spectral, and temporal characteristics simultaneously. To address this, 3D EEG videos are created by stacking spectral feature maps obtained from each sub-band per time frame in a temporal domain. The EEG videos are the input to a combination of the Convolution Neural Network (CNN) and Long–Short Term Memory (LSTM) for classification. Performances obtained using the proposed methodologies have surpassed the state-of-the-art for three of the classification scenarios considered in this work, namely, EEG baselines, mental arithmetic, and Parkinson’s disease. The video analysis resulted in 92.5% and 98.81% peak mean accuracies for the EEG baseline and EEG mental arithmetic, respectively. On the other hand, for distinguishing Parkinson’s disease from controls, a peak mean accuracy of 88.51% is achieved using traditional methods on 1D feature vectors. This illustrates that 3D and 2D feature representations are effective for those EEG data where topographical changes in brain activation regions are observed. However, in scenarios where topographical changes are not consistent across subjects of the same class, these methodologies fail. On the other hand, the 1D analysis proves to be significantly effective in the case involving changes in the overall activation of the brain due to varying degrees of deterioration.
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