[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 …

A dataset of scalp EEG recordings of Alzheimer's disease, frontotemporal dementia and healthy subjects from routine EEG

A Miltiadous, KD Tzimourta, T Afrantou, P Ioannidis… - Data, 2023 - mdpi.com
Recently, there has been a growing research interest in utilizing the electroencephalogram
(EEG) as a non-invasive diagnostic tool for neurodegenerative diseases. This article …

Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text

SA Murad, N Rahimi - arXiv preprint arXiv:2405.00726, 2024 - arxiv.org
The conversion of brain activity into text using electroencephalography (EEG) has gained
significant traction in recent years. Many researchers are working to develop new models to …

EEG entropy insights in the context of physiological aging and Alzheimer's and Parkinson's diseases: a comprehensive review

A Cacciotti, C Pappalettera, F Miraglia, PM Rossini… - GeroScience, 2024 - Springer
In recent decades, entropy measures have gained prominence in neuroscience due to the
nonlinear behaviour exhibited by neural systems. This rationale justifies the application of …

GC‐CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network

M Amini, MM Pedram, AR Moradi… - Computational …, 2022 - Wiley Online Library
There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease
that can lead to cognitive decline, deterioration of daily life, and behavioral and …

Efficient identification of Alzheimer's brain dynamics with Spatial-Temporal Autoencoder: A deep learning approach for diagnosing brain disorders

L Wu, Q Zhao, J Liu, H Yu - Biomedical Signal Processing and Control, 2023 - Elsevier
Alzheimer's disease (AD) is a progressive neurological disorder seriously affecting cognitive
and behavior abilities of the older people. Accurate and early diagnosis of AD is critical for …

Identifying biomarkers for tDCS treatment response in Alzheimer's disease patients: a machine learning approach using resting-state EEG classification

SM Andrade, L da Silva-Sauer… - Frontiers in Human …, 2023 - frontiersin.org
Background Transcranial direct current stimulation (tDCS) is a promising treatment for
Alzheimer's Disease (AD). However, identifying objective biomarkers that can predict brain …

Enhanced Alzheimer's disease and Frontotemporal Dementia EEG Detection: Combining lightGBM Gradient Boosting with Complexity Features

A Miltiadous, KD Tzimourta, V Aspiotis… - 2023 IEEE 36th …, 2023 - ieeexplore.ieee.org
Alzheimer's disease and Frontotemporal dementia are the two most reported dementia
cases. They both are neurodegenerative disorders without cure while existing treatments …

[HTML][HTML] An empirical wavelet transform-based approach for motion artifact removal in electroencephalogram signals

AB Nayak, A Shah, S Maheshwari, V Anand… - Decision Analytics …, 2024 - Elsevier
Motion artifacts reduce the quality of information in the electroencephalogram (EEG) signals.
In this study, we have developed an effective approach to mitigate the motion artifacts in …

Neural markers of reduced arousal and consciousness in mild cognitive impairment

M Estarellas, J Huntley, D Bor - International Journal of Geriatric …, 2024 - Wiley Online Library
Abstract Objectives People with Alzheimer's Disease (AD) experience changes in their level
and content of consciousness, but there is little research on biomarkers of consciousness in …