[HTML][HTML] A review of EEG signal features and their application in driver drowsiness detection systems

I Stancin, M Cifrek, A Jovic - Sensors, 2021 - mdpi.com
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …

Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts

PM Rossini, R Di Iorio, F Vecchio, M Anfossi… - Clinical …, 2020 - Elsevier
Alzheimer's disease (AD) is the most common neurodegenerative disease among the
elderly with a progressive decline in cognitive function significantly affecting quality of life …

[HTML][HTML] Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals

SK Khare, UR Acharya - Knowledge-Based Systems, 2023 - Elsevier
Background: Alzheimer's disease (AZD) is a degenerative neurological condition that
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …

Refined composite multiscale dispersion entropy and its application to biomedical signals

H Azami, M Rostaghi, D Abásolo… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Objective: We propose a novel complexity measure to overcome the deficiencies of the
widespread and powerful multiscale entropy (MSE), including, MSE values may be …

Complexity analysis of EEG, MEG, and fMRI in mild cognitive impairment and Alzheimer's disease: a review

J Sun, B Wang, Y Niu, Y Tan, C Fan, N Zhang, J Xue… - Entropy, 2020 - mdpi.com
Alzheimer's disease (AD) is a degenerative brain disease with a high and irreversible
incidence. In recent years, because brain signals have complex nonlinear dynamics, there …

Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis

M Bachmann, L Päeske, K Kalev, K Aarma… - Computer methods and …, 2018 - Elsevier
Abstract Background and Objective Depressive disorder is one of the leading causes of
burden of disease today and it is presumed to take the first place in the world in 2030. Early …

Diagnosis of Alzheimer's disease from EEG signals: where are we standing?

J Dauwels, F Vialatte, A Cichocki - Current Alzheimer Research, 2010 - benthamdirect.com
This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from
electroencephalograms (EEG). Three major effects of AD on EEG have been observed …

Automatic detection of Alzheimer's disease from EEG signals using low-complexity orthogonal wavelet filter banks

DV Puri, SL Nalbalwar, AB Nandgaonkar… - … Signal Processing and …, 2023 - Elsevier
Background: Alzheimer's disease (AD) is one of the most common neurodegenerative
disorder. As the incidence of AD is rapidly increasing worldwide, detecting it at an early …

Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy

C Pappalettera, F Miraglia, M Cotelli, PM Rossini… - GeroScience, 2022 - Springer
The objective of the present study is to explore the brain resting state differences between
Parkinson's disease (PD) patients and age-and gender-matched healthy controls (elderly) in …

[HTML][HTML] Automated multiclass classification of spontaneous EEG activity in Alzheimer's disease and mild cognitive impairment

SJ Ruiz-Gómez, C Gómez, J Poza, GC Gutiérrez-Tobal… - Entropy, 2018 - mdpi.com
The discrimination of early Alzheimer's disease (AD) and its prodromal form (ie, mild
cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the …