Systematic review on resting‐state EEG for Alzheimer's disease diagnosis and progression assessment
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of
the more than 46 million dementia cases estimated worldwide. Although there is no cure for …
the more than 46 million dementia cases estimated worldwide. Although there is no cure for …
Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
Neurodegenerative diseases have shown an increasing incidence in the older population in
recent years. A significant amount of research has been conducted to characterize these …
recent years. A significant amount of research has been conducted to characterize these …
A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
Electroencephalographic (EEG) recordings generate an electrical map of the human brain
that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things …
that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things …
Early detection of Alzheimer's disease from EEG signals using Hjorth parameters
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 …
brain that ultimately results in the death of neurons and dementia. The prevalence of the …
EEG signal processing for Alzheimer's disorders using discrete wavelet transform and machine learning approaches
The most common neurological brain issue is Alzheimer's disease, which can be diagnosed
using a variety of clinical methods. However, the electroencephalogram (EEG) is shown to …
using a variety of clinical methods. However, the electroencephalogram (EEG) is shown to …
Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity
VA Maksimenko, SA Kurkin, EN Pitsik, VY Musatov… - …, 2018 - Wiley Online Library
We apply artificial neural network (ANN) for recognition and classification of
electroencephalographic (EEG) patterns associated with motor imagery in untrained …
electroencephalographic (EEG) patterns associated with motor imagery in untrained …
Identification of Alzheimer's disease from central lobe EEG signals utilizing machine learning and residual neural network
IA Fouad, FEZM Labib - Biomedical Signal Processing and Control, 2023 - Elsevier
Cognitive and behavioral deficits are some of the symptoms of Alzheimer's disease, a
neurological disease caused by brain deterioration. Early diagnosis of the disease …
neurological disease caused by brain deterioration. Early diagnosis of the disease …
A dementia classification framework using frequency and time-frequency features based on EEG signals
Alzheimer's disease (AD) accounts for 60%–70% of all dementia cases, and clinical
diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify …
diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify …
Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram
Functional connectivity of the human brain, representing statistical dependence of
information flow between cortical regions, significantly contributes to the study of the intrinsic …
information flow between cortical regions, significantly contributes to the study of the intrinsic …
A physiological signal-based method for early mental-stress detection
The early detection of mental stress is critical for efficient clinical treatment. Compared with
traditional approaches, the automatic methods presented in literature have shown …
traditional approaches, the automatic methods presented in literature have shown …