Impact of eeg parameters detecting dementia diseases: A systematic review
LM Sánchez-Reyes, J Rodríguez-Reséndiz… - IEEE …, 2021 - ieeexplore.ieee.org
Dementia diseases are increasing rapidly, according to the World Health Organization
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …
(WHO), becoming an alarming problem for the health sector. The electroencephalogram …
Electrochemical immunosensors developed for amyloid-beta and tau proteins, leading biomarkers of Alzheimer's disease
Alzheimer's disease (AD) is the most common neurological disease and a serious cause of
dementia, which constitutes a threat to human health. The clinical evidence has found that …
dementia, which constitutes a threat to human health. The clinical evidence has found that …
Medical health big data classification based on KNN classification algorithm
W Xing, Y Bei - Ieee Access, 2019 - ieeexplore.ieee.org
The rapid development of information technology has led to the development of medical
informatization in the direction of intelligence. Medical health big data provides a basic data …
informatization in the direction of intelligence. Medical health big data provides a basic data …
A hybrid deep neural network for classification of schizophrenia using EEG Data
J Sun, R Cao, M Zhou, W Hussain, B Wang, J Xue… - Scientific Reports, 2021 - nature.com
Schizophrenia is a serious mental illness that causes great harm to patients, so timely and
accurate detection is essential. This study aimed to identify a better feature to represent …
accurate detection is essential. This study aimed to identify a better feature to represent …
A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals
Mild cognitive impairment (MCI) can be an indicator representing the early stage of
Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major …
Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major …
A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis
RA Movahed, GP Jahromi, S Shahyad… - Journal of Neuroscience …, 2021 - Elsevier
Background Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed
through questionnaire-based approaches; however, these methods may not lead to an …
through questionnaire-based approaches; however, these methods may not lead to an …
Epileptic seizure detection using 1 D-convolutional long short-term memory neural networks
Advances in deep learning methods present new opportunities for fixing complex problems
for an end to end learning. In terms of optimal design, seizure detection from EEG data has …
for an end to end learning. In terms of optimal design, seizure detection from EEG data has …
Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …
remarkable performance in providing medical professionals and patients with support for …
Automatic detection of Alzheimer's disease from EEG signals using low-complexity orthogonal wavelet filter banks
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 …
disorder. As the incidence of AD is rapidly increasing worldwide, detecting it at an early …
A survey on eeg signal processing techniques and machine learning: Applications to the neurofeedback of autobiographical memory deficits in schizophrenia
MÁ Luján, MV Jimeno, J Mateo Sotos, JJ Ricarte… - Electronics, 2021 - mdpi.com
In this paper, a general overview regarding neural recording, classical signal processing
techniques and machine learning classification algorithms applied to monitor brain activity is …
techniques and machine learning classification algorithms applied to monitor brain activity is …