A comparative analysis of signal processing and classification methods for different applications based on EEG signals

A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2020 - Elsevier
Electroencephalogram (EEG) measures the neuronal activities in the form of electric
currents that are generated due to the synchronized activity by a group of specialized …

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 …

Early detection of Alzheimer's disease from EEG signals using Hjorth parameters

MS Safi, SMM Safi - Biomedical Signal Processing and Control, 2021 - Elsevier
Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the
brain that ultimately results in the death of neurons and dementia. The prevalence of the …

Particle swarm optimization fractional slope entropy: A new time series complexity indicator for bearing fault diagnosis

Y Li, L Mu, P Gao - Fractal and Fractional, 2022 - mdpi.com
Slope entropy (SlEn) is a time series complexity indicator proposed in recent years, which
has shown excellent performance in the fields of medical and hydroacoustics. In order to …

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

X Shan, J Cao, S Huo, L Chen… - Human Brain …, 2022 - Wiley Online Library
Functional connectivity of the human brain, representing statistical dependence of
information flow between cortical regions, significantly contributes to the study of the intrinsic …

Machine learning algorithms and statistical approaches for Alzheimer's disease analysis based on resting-state EEG recordings: A systematic review

KD Tzimourta, V Christou, AT Tzallas… - … journal of neural …, 2021 - World Scientific
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of
dementia with a great prevalence in western countries. The diagnosis of AD and its …

Detection of alzheimer's dementia by using signal decomposition and machine learning methods

OK Cura, A Akan, GC Yilmaz, HS Ture - International journal of …, 2022 - World Scientific
Dementia is one of the most common neurological disorders causing defection of cognitive
functions, and seriously affects the quality of life. In this study, various methods have been …

A new denoising method based on decomposition mixing of hydro-acoustic signal

G Li, H Yan, H Yang - Ocean Engineering, 2024 - Elsevier
Hydro-acoustic signal (HAS) contains abundant information. HAS denoising is of great
significance for accurate identification of underwater targets, research of military detection …

EEG window length evaluation for the detection of Alzheimer's disease over different brain regions

KD Tzimourta, N Giannakeas, AT Tzallas, LG Astrakas… - Brain sciences, 2019 - mdpi.com
Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of
dementia with a rapidly increasing world prevalence. In this paper, the ability of several …

A new approach for automatic detection of focal EEG signals using wavelet packet decomposition and quad binary pattern method

NJ Sairamya, MSP Subathra… - … Signal Processing and …, 2021 - Elsevier
A comprehensive feature representation for electroencephalogram (EEG) signal to achieve
effective epileptic focus localization using a one-dimensional quad binary pattern (QBP) is …