EEG complexity analysis has recently been shown to help to diagnose Alzheimer’s Disease (AD) in the early stages. The complexity study is based on the processing of continuous artifact-free Electroencephalography (EEG). Therefore, artifact rejection is normally required because artifacts might mimic cognitive or pathologic activity and therefore bias the neurologist visual interpretation of the EEG. Furthermore, the EEG complexity analysis is strongly altered by artifacts. In this paper, we evaluate the effects of artifacts rejection by a promising technique, Automatic Wavelet-Independent Component Analysis (AWICA), on the EEG Complexity in AD patients. We also investigate the EEG complexity before and after artifact rejection through some measures based on Shannon’s Entropy, Renyi’s Entropy and Tsallis’s Entropy.

Effects of artifacts rejection on EEG complexity in Alzheimer's disease

F. La Foresta;N. Mammone;F. C. Morabito
2015-01-01

Abstract

EEG complexity analysis has recently been shown to help to diagnose Alzheimer’s Disease (AD) in the early stages. The complexity study is based on the processing of continuous artifact-free Electroencephalography (EEG). Therefore, artifact rejection is normally required because artifacts might mimic cognitive or pathologic activity and therefore bias the neurologist visual interpretation of the EEG. Furthermore, the EEG complexity analysis is strongly altered by artifacts. In this paper, we evaluate the effects of artifacts rejection by a promising technique, Automatic Wavelet-Independent Component Analysis (AWICA), on the EEG Complexity in AD patients. We also investigate the EEG complexity before and after artifact rejection through some measures based on Shannon’s Entropy, Renyi’s Entropy and Tsallis’s Entropy.
2015
978-3-319-18163-9
ICA; EEG; Alzheimer Disease
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/11075
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