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 …
Decision variants for the automatic determination of optimal feature subset in RF-RFE
Q Chen, Z Meng, X Liu, Q **, R Su - Genes, 2018 - mdpi.com
Feature selection, which identifies a set of most informative features from the original feature
space, has been widely used to simplify the predictor. Recursive feature elimination (RFE) …
space, has been widely used to simplify the predictor. Recursive feature elimination (RFE) …
Diagnose Alzheimer's disease and mild cognitive impairment using deep CascadeNet and handcrafted features from EEG signals
K Rezaee, M Zhu - Biomedical Signal Processing and Control, 2025 - Elsevier
Alzheimer's disease (AD) is the most prevalent clinically diagnosed neurodegenerative
disorder. Early detection of mild cognitive impairment (MCI) is crucial for implementing …
disorder. Early detection of mild cognitive impairment (MCI) is crucial for implementing …
A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson's Disease
Background Assessment and rating of Parkinson's Disease (PD) are commonly based on
the medical observation of several clinical manifestations, including the analysis of motor …
the medical observation of several clinical manifestations, including the analysis of motor …
EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques
Introduction Despite the existence of numerous clinical techniques for identifying
neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows …
neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows …
Classification of healthy subjects and Alzheimer's disease patients with Dementia from cortical sources of resting state EEG rhythms: A study using artificial neural …
Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's
disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based …
disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based …
Object detection for industrial applications: Training strategies for ai-based depalletizer
In the last 10 years, the demand for robot-based depalletization systems has constantly
increased due to the growth of sectors such as logistics, storage, and supply chains. Since …
increased due to the growth of sectors such as logistics, storage, and supply chains. Since …
A survey on deep learning in electromyographic signal analysis
In the recent past Deep Learning (DL) has been used to develop intelligent systems that
perform surprisingly well in a large variety of tasks, eg image recognition, machine …
perform surprisingly well in a large variety of tasks, eg image recognition, machine …
Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer's diagnosis from EEG signals towards IoMT
Abstract The Internet of Medical Things (IoMT) is poised to play a pivotal role in future
medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer's …
medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer's …
Assessment and rating of movement impairment in parkinson's disease using a low-cost vision-based system
Assessment and rating of Parkinson's Disease (PD) are commonly based on the medical
observation of several clinical manifestations, including the analysis of motor activities. In …
observation of several clinical manifestations, including the analysis of motor activities. In …