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 …

Electrochemical immunosensors developed for amyloid-beta and tau proteins, leading biomarkers of Alzheimer's disease

A Sharma, L Angnes, N Sattarahmady, M Negahdary… - Biosensors, 2023 - mdpi.com
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 …

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 …

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 …

A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals

S Siuly, ÖF Alçin, E Kabir, A Şengür… - … on Neural Systems …, 2020 - ieeexplore.ieee.org
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 …

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 …

Epileptic seizure detection using 1 D-convolutional long short-term memory neural networks

W Hussain, MT Sadiq, S Siuly, AU Rehman - Applied Acoustics, 2021 - Elsevier
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 …

Trustworthy artificial intelligence in Alzheimer's disease: state of the art, opportunities, and challenges

S El-Sappagh, JM Alonso-Moral, T Abuhmed… - Artificial Intelligence …, 2023 - Springer
Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
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

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 …

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 …