Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG
Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG
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基于不同频带的卷积神经网络使用脑电图评估强直性冷痛
DOI：
10.1016/j.neucom.2019.10.023
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发表时间：
2020-02
期刊：
影响因子：
6
通讯作者：
Mingxin Yu;Yichen Sun;Bofei Zhu;Lianqing Zhu;Yingzi Lin;Xiaoying Tang;Yikang Guo;Guangkai Sun;M. Dong
中科院分区：
文献类型：
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作者：
Mingxin Yu;Yichen Sun;Bofei Zhu;Lianqing Zhu;Yingzi Lin;Xiaoying Tang;Yikang Guo;Guangkai Sun;M. Dong
本研究的目的是提出一种新颖的分类框架，称为基于不同频段的卷积神经网络（DFB-based ConvNets），它可以客观地识别强直性冷痛状态。为了实现这一目标，记录了 32 名受试者在冷刺激条件下的头皮脑电图数据。所提出的基于 DFB 的 ConvNets 模型能够对三类强直性疼痛进行分类：无痛、中度疼痛和重度疼痛。首先，所提出的方法利用基于不同频段的输入来学习脑电图（EEG）不同频段的时间表示，预计这些表示将具有更强的辨别力。然后将导出的特征连接起来形成特征向量，将其输入到全连接网络中以执行分类任务。实验结果表明，该方法成功区分了强直性冷痛状态。为了展示基于 DFB 的 ConvNets 分类器的优越性，我们将我们的结果与最先进的分类器进行了比较，并表明它具有有竞争力的分类精度 (97.37%)。此外，这些有希望的结果可能为在临床疼痛研究中使用基于 DFB 的 ConvNet 铺平道路。
The purpose of this study is to present a novel classification framework, called diverse frequency band-based Convolutional Neural Networks (DFB-based ConvNets), which can objectively identify tonic cold pain states. To achieve this goal, scalp EEG data were recorded from 32 subjects under cold stimuli conditions. The proposed DFB-based ConvNets model is capable of classifying three classes of tonic pain: No pain, Moderate Pain, and Severe Pain. Firstly, the proposed method utilizes diverse frequency band-based inputs to learn temporal representations from different frequency bands of Electroencephalogram (EEG) which are expected to have more discriminative power. Then the derived features are concatenated to form a feature vector, which is fed into a fully-connected network for performing the classification task. Experimental results demonstrate that the proposed method successfully discriminates the tonic cold pain states. To show the superiority of the DFB-based ConvNets classifier, we compare our results with the state-of-the-art classifiers and show it has a competitive classification accuracy (97.37%). Moreover, these promising results may pave the way to use DFB-based ConvNets in clinical pain research.