论文标题
基于双光谱的跨频功能连通性：阿尔茨海默氏病的分类
Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease
论文作者
论文摘要
阿尔茨海默氏病（AD）是一种神经退行性疾病，已知会影响脑功能连通性（FC）。线性FC测量已应用于通过分裂神经生理信号（例如脑电图（EEG）记录）中的神经生理信号来研究AD的差异，并分别分析它们。除传统的内部方法外，我们还通过量化跨频FC来解决这一限制。跨双光谱是一种高阶光谱分析，用于测量非线性FC，并将其与跨频谱进行比较，后光谱仅测量频段内的线性FC。然后，每个频率耦合都用于构建FC网络，该网络又是矢量化并用于训练分类器。我们表明，网络的融合功能提高了分类精度。尽管频率内部和跨频网络都可以用来以高准确性来预测AD，但我们的结果表明，基于双光谱的FC的表现优于跨频谱表明跨频FC的重要作用。
Alzheimer's disease (AD) is a neurodegenerative disease known to affect brain functional connectivity (FC). Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. Each frequency coupling is then used to construct an FC network, which is in turn vectorised and used to train a classifier. We show that fusing features from networks improves classification accuracy. Although both within-frequency and cross-frequency networks can be used to predict AD with high accuracy, our results show that bispectrum-based FC outperforms cross-spectrum suggesting an important role of cross-frequency FC.
