Wang Jiahao, Wei Mengying, Zhang Li, Huang Gan, Liang Zhen, Li Linling, Zhang Zhiguo
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1507-1511. doi: 10.1109/EMBC44109.2020.9176644.
Pain is a subjective experience and clinicians need to treat patients with accurate pain levels. EEG has emerged as a useful tool for objective pain assessment, but due to the low signal-to-noise ratio of pain-related EEG signals, the prediction accuracy of EEG-based pain prediction models is still unsatisfactory. In this paper, we proposed an autoencoder model based on convolutional neural networks for feature extraction of pain-related EEG signals. More precisely, we used EEGNet to build an autoencoder model to extract a small set of features from high-density pain-evoked EEG potentials and then establish a machine learning models to predict pain levels (high pain vs. low pain) from extracted features. Experimental results show that the new autoencoder-based approach can effectively identify pain-related features and can achieve better classification results than conventional methods.
疼痛是一种主观体验，临床医生需要根据准确的疼痛程度来治疗患者。脑电图（EEG）已成为客观疼痛评估的一种有用工具，但由于与疼痛相关的EEG信号信噪比低，基于EEG的疼痛预测模型的预测准确性仍然不尽人意。在本文中，我们提出了一种基于卷积神经网络的自动编码器模型，用于与疼痛相关的EEG信号的特征提取。更确切地说，我们使用EEGNet构建一个自动编码器模型，从高密度疼痛诱发的EEG电位中提取一小部分特征，然后建立一个机器学习模型，根据提取的特征预测疼痛程度（高疼痛与低疼痛）。实验结果表明，新的基于自动编码器的方法可以有效地识别与疼痛相关的特征，并且比传统方法能取得更好的分类结果。
