Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification. Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which are then classified by a 1-D convolutional neural network. Comparing with conventional machine learning and deep learning methods, we demonstrate the superiority of using VAE for feature extraction, as reflected by the significantly improved classification accuracies, better visualizations and reduced impurity measures in the feature representations. Future work can be directed to gaining an in-depth understanding regarding the spatial patterns that have been learned by the proposed model from a neurological view, as well as improving the interpretability of the proposed model by allowing it to uncover any temporal-related information.
翻译:肥胖是当今现代社会常见的一个问题,它可能导致各种疾病,并显著降低生活质量。目前,已经开展了研究,以调查休眠状态EEG(电子脑图)信号,目的是查明与肥胖相关的可能神经特征。在这项研究中,我们提出了一个深层次的学习框架,以提取休眠状态EEG特征,用于肥胖和瘦质的科目分类。具体地说,采用了新的变异自动coder框架,从原始EEEG信号中提取主题异质特征,然后通过1-进化神经网络加以分类。与传统的机器学习和深层学习方法相匹配,我们展示了利用VAE进行特征提取的优越性,具体表现在特征描述中显著改进的分类精度、更好的可视化和减少不纯度措施中。未来工作的方向是深入了解拟议模型从神经学角度所学的空间模式,并通过允许其发现任何与时间有关的信息来改进拟议模型的可解释性。