43 research outputs found
Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy
The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer’s disease (AD) patients using the Multiscale Entropy (MSE). The MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant difference
The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia
Dementia being a syndrome caused by a brain disease of a chronic or
progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding
and adequate communication, of organizing daily life and of leading a family,
work and autonomous social life; leads to a state of total dependence; therefore,
its early detection and classification is of vital importance in order to serve as
clinical support for physicians in the personalization of treatment programs. The
use of the electroencephalogram as a tool for obtaining information on the
detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of
mental states based on electromagnetic oscillations, signal processing given by
the electroencephalogram, review of processing techniques, results obtained
where it is proposed the mathematical model about neural networks, discussion
and finally the conclusions