Trambaiolli Lucas R, Falk Tiago H, Fraga Francisco J, Anghinah Renato, Lorena Ana C
Mathematics, Computation and Cognition Center, Universidade Federal do ABC, Brazil.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3828-31. doi: 10.1109/IEMBS.2011.6090951.
There is recent indication that Alzheimer's disease (AD) can be characterized by atypical modulation of electrophysiological brain activity caused by fibrillar amyloid deposition in specific regions of the brain, such as those related to cognition and memory. In this paper, we propose to objectively characterize EEG sub-band modulation in an attempt to develop an automated noninvasive AD diagnostics tool. First, multi-channel full-band EEG signals are decomposed into five well-known frequency sub-bands: delta, theta, alpha, beta, and gamma. The temporal amplitude envelope of each sub-band is then computed via a Hilbert transformation. The proposed 'spectro-temporal modulation energy' feature measures the rate with which each sub-band is modulated. Modulation energy features are computed for 19 referential EEG signals and seven bipolar signals. Salient features are then selected and used to train four different classifiers, namely, support vector machines, logistic regression, classification and regression trees, and neural networks. Experiments with a database of 34 participants, 22 of which have been clinically diagnosed with probable-AD, show a neural network classifier achieving over 91% accuracy, thus significantly outperforming a classifier trained with conventional spectral-based features.
最近有迹象表明，阿尔茨海默病（AD）的特征可能是大脑特定区域（如与认知和记忆相关的区域）中纤维状淀粉样蛋白沉积引起的脑电生理活动的非典型调制。在本文中，我们建议客观地表征脑电图子带调制，以开发一种自动化的非侵入性AD诊断工具。首先，将多通道全频带脑电信号分解为五个著名的频率子带：δ、θ、α、β和γ。然后通过希尔伯特变换计算每个子带的时间幅度包络。所提出的“频谱-时间调制能量”特征测量每个子带被调制的速率。针对19个参考脑电信号和7个双极信号计算调制能量特征。然后选择显著特征并用于训练四种不同的分类器，即支持向量机、逻辑回归、分类和回归树以及神经网络。对一个包含34名参与者的数据库进行的实验表明，其中22人已被临床诊断为可能患有AD，结果显示神经网络分类器的准确率超过91%，因此明显优于使用传统基于频谱的特征训练的分类器。
