[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 …
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …
Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals
Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a
debilitating progressive neurodegenerative disease. We have developed an automated AD …
debilitating progressive neurodegenerative disease. We have developed an automated AD …
Lattice 123 pattern for automated Alzheimer's detection using EEG signal
This paper presents an innovative feature engineering framework based on lattice structures
for the automated identification of Alzheimer's disease (AD) using electroencephalogram …
for the automated identification of Alzheimer's disease (AD) using electroencephalogram …
Assessing the Potential of EEG in Early Detection of Alzheimer's Disease: A Systematic Comprehensive Review (2000–2023)
S Ehteshamzad - Journal of Alzheimer's Disease Reports, 2024 - journals.sagepub.com
Background: As the prevalence of Alzheimer's disease (AD) grows with an aging population,
the need for early diagnosis has led to increased focus on electroencephalography (EEG) …
the need for early diagnosis has led to increased focus on electroencephalography (EEG) …
EEG-Based Systematic Explainable Alzheimer's Disease and Mild Cognitive Impairment Identification Using Novel Rational Dyadic Biorthogonal Wavelet Filter Banks
DV Puri, SL Nalbalwar, PP Ingle - Circuits, Systems, and Signal …, 2024 - Springer
Alzheimer's disease (AD) is a frequently encountered chronic disorder. AD patients suffer
from various cognitive dysfunctions. The traditional methods fail to identify AD in the early …
from various cognitive dysfunctions. The traditional methods fail to identify AD in the early …
Automated alzheimer's disease diagnosis using norm features extracted from EEG signals
Alzheimer's disease (AD) is a neurodegenerative disorder that progresses over time and
affects cognitive abilities. It is marked by symptoms such as memory loss, language and …
affects cognitive abilities. It is marked by symptoms such as memory loss, language and …
Metaheuristic optimized time–frequency features for enhancing Alzheimer's disease identification
Background: Alzheimer's disease (AD) is a chronic disorder characterized by progressive
cognitive dysfunctions and memory loss. Electroencephalography (EEG) is a non-invasive …
cognitive dysfunctions and memory loss. Electroencephalography (EEG) is a non-invasive …
D‐Unet: A symmetric architecture of convolutional neural network with two auxiliary outputs for dementia recognition
Dementia‐associated disorders cause damage to the brains of patients and bring huge
burdens to individuals and families. Electroencephalogram (EEG) monitoring is friendly to …
burdens to individuals and families. Electroencephalogram (EEG) monitoring is friendly to …
Mild Cognitive Impairment detection based on EEG and HRV data
A Boudaya, S Chaabene, B Bouaziz… - Digital Signal …, 2024 - Elsevier
Brain volume decrease is usually connected to neurodegeneration and aging. In this
environment, an important percentage of elderly persons suffer from mild cognitive …
environment, an important percentage of elderly persons suffer from mild cognitive …
[HTML][HTML] Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals
Y Zhou, R Cao, A Zhang, P Li - Sensors, 2024 - mdpi.com
The detection performance of radar is significantly impaired by active jamming and mutual
interference from other radars. This paper proposes a radio signal modulation recognition …
interference from other radars. This paper proposes a radio signal modulation recognition …