用LDA和PCA检验正常和偏头痛近红外光谱数据的可分离性

I. Sen, Andreas Akun
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摘要

功能近红外光谱(fNIRS)是一种令人兴奋的、相对较新的测量大脑认知活动的方法。由于该方法测量血液氧合,因此可以用于检查偏头痛患者与健康人之间的差异,因为偏头痛是一种神经血管疾病。本研究的目的是检查健康受试者和偏头痛患者神经血管动力学的差异。为了实现这一目标,采用线性判别分析(LDA)和主成分分析(PCA)对采集的近红外光谱信号进行分析,并进行参数分类来量化可分性
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Inspection of Separability of Normal and Migraine fNIRS Data using LDA and PCA
Functional near infrared spectroscopy (fNIRS) is an exciting, relatively new method to measure cognitive activity in the brain. Since the method measures blood oxygenation, it can be used for examining the differences between migraineurs and healthy people since migraine is a neurovascular disease. The aim of this study is to inspect the differences in neurovascular dynamics of healthy subjects and migraineurs. To achieve this aim, linear discriminant analysis (LDA) and principal component analysis (PCA) have been applied to acquired fNIRS signals, and parametric classification has been performed to quantify the separability
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