Towards Semi-Automatic Artifact Rejection for the Improvement of Alzheimer’s Disease Screening from EEG Signals

Sensors

Jordi Solé-Casals¹  François-Benoît Vialatte² 

[1] Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, 08500 Barcelona, Spain; E-Mail:;BCI Team, Brain Plasticity Laboratory, UMR 8249, CNRS, Paris 75005, France

关键词: EEG;    artifacts;    blind source separation;    Alzheimer’s disease;    screening;   

DOI  :  10.3390/s150817963

来源: mdpi

【 摘 要 】

A large number of studies have analyzed measurable changes that Alzheimer’s disease causes on electroencephalography (EEG). Despite being easily reproducible, those markers have limited sensitivity, which reduces the interest of EEG as a screening tool for this pathology. This is for a large part due to the poor signal-to-noise ratio of EEG signals: EEG recordings are indeed usually corrupted by spurious extra-cerebral artifacts. These artifacts are responsible for a consequent degradation of the signal quality. We investigate the possibility to automatically clean a database of EEG recordings taken from patients suffering from Alzheimer’s disease and healthy age-matched controls. We present here an investigation of commonly used markers of EEG artifacts: kurtosis, sample entropy, zero-crossing rate and fractal dimension. We investigate the reliability of the markers, by comparison with human labeling of sources. Our results show significant differences with the sample entropy marker. We present a strategy for semi-automatic cleaning based on blind source separation, which may improve the specificity of Alzheimer screening using EEG signals.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.