Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis DOI Creative Commons
Ziyang Li, Hong Wang, Jia-Ning Song

и другие.

Sensors, Год журнала: 2024, Номер 25(1), С. 52 - 52

Опубликована: Дек. 25, 2024

The early prediction of Alzheimer’s disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from Multi-Source Interference Task (MSIT) and Sternberg Memory (STMT). Time–frequency features extracted using Multitaper method, followed by multidimensional reduction techniques. Subspace (F24 F216) selected via t-tests False Discovery Rate (FDR) multiple comparisons correction, subsequently analyzed Time–Frequency Area Average Test (TFAAT) Prefrontal Beta Time Series (PBTST). experimental results reveal that MSIT task achieves optimal cross-subject classification performance Support Vector Machine (SVM) approach with TFAAT feature set, yielding Receiver Operating Characteristic Under Curve (ROC AUC) 58%. Similarly, demonstrates ability logistic regression model applied to PBTST emphasizing beta band power spectrum prefrontal cortex as potential marker AD risk. These findings confirm provides stronger compared resting-state EEG, offering valuable insights advancing research.

Язык: Английский

STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network DOI
Pramod Kachare, Sandeep B. Sangle, Digambar Puri

и другие.

Cognitive Neurodynamics, Год журнала: 2024, Номер 18(5), С. 3195 - 3208

Опубликована: Июль 19, 2024

Язык: Английский

Процитировано

13
LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection DOI
Pramod Kachare, Digambar Puri, Sandeep B. Sangle

и другие.

Physical and Engineering Sciences in Medicine, Год журнала: 2024, Номер unknown

Опубликована: Июнь 11, 2024

Язык: Английский

Процитировано

11
LEADNet: Detection of Alzheimer’s Disease Using Spatiotemporal EEG Analysis and Low-Complexity CNN DOI Creative Commons
Digambar Puri, Pramod Kachare, Sandeep B. Sangle

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 113888 - 113897

Опубликована: Янв. 1, 2024

Clinical methods for dementia detection are expensive and prone to human errors. Despite various computer-aided using electroencephalography (EEG) signals artificial intelligence, a consistent separation of Alzheimer's disease (AD) normal-control (NC) subjects remains elusive. This paper proposes low-complexity EEG-based AD CNN called LEADNet generate disease-specific features. employs spatiotemporal EEG as input, two convolution layers feature generation, max-pooling layer asymmetric redundancy reduction, fully-connected nonlinear transformation selection, softmax probability prediction. Different quantitative measures calculated an open-source dataset compare four pre-trained models. The results show that the lightweight architecture has at least 150-fold reduction in network parameters highest testing accuracy 98.75% compared investigation individual showed successive improvements selection separating NC subjects. A comparison with state-of-the-art models accuracy, sensitivity, specificity were achieved by model.

Язык: Английский

Процитировано

10
Unlocking the Potential of EEG in Alzheimer's Disease Research: Current Status and Pathways to Precision Detection DOI Creative Commons

Faisal Akbar,

Imran Taj,

Syed Muhammad Usman

и другие.

Brain Research Bulletin, Год журнала: 2025, Номер unknown, С. 111281 - 111281

Опубликована: Март 1, 2025

Alzheimer's disease (AD) affects millions of individuals worldwide and is considered a serious global health issue due to its gradual neuro-degenerative effects on cognitive abilities such as memory, thinking, behavior. There no cure for this but early detection along with supportive care plan may aid in improving the quality life patients. Automated AD challenging because symptoms vary patients genetic, environmental, or other co-existing conditions. In recent years, multiple researchers have proposed automated methods using MRI fMRI. These approaches are expensive, poor temporal resolution, do not offer real-time insights, proven be very accurate. contrast, only limited number studies explored potential Electroencephalogram (EEG) signals detection. present cost-effective, non-invasive, high-temporal-resolution alternative Despite their potential, application EEG research remains under-explored. This study reviews publicly available datasets, variety machine learning models developed detection, performance metrics achieved by these methods. It provides critical analysis existing approaches, highlights challenges, identifies key areas requiring further investigation. Key findings include detailed evaluation current methodologies, prevailing trends, gaps field. What sets work apart in-depth Disease providing stronger more reliable foundation understanding role area.

Язык: Английский

Процитировано

1
EEG complexity-based algorithm using Multiscale Fuzzy Entropy: Towards a detection of Alzheimer’s disease DOI Creative Commons
Andrea Cataldo, Sabatina Criscuolo, Egidio De Benedetto

и другие.

Measurement, Год журнала: 2023, Номер 225, С. 114040 - 114040

Опубликована: Дек. 15, 2023

Alzheimer's Disease (AD) is a progressive neurodegenerative condition causing memory, attention, and language decline. Current AD diagnostic methods lack objectivity non-invasiveness. While electroencephalography (EEG) holds promise for research, conventional EEG analysis have proven unsatisfactory. Non-linear dynamical approaches are considered more effective in assessing the brain's complex nature. Starting from these considerations, this study presents an entropy-based algorithm utilizing Multiscale Fuzzy Entropy (MFE) as promising, method. Computed across 20 different time scales public dataset, MFE showed significant discriminative power. Notably, trend inversion was observed results: subjects displayed higher complexity values slow frequency bands compared to healthy controls, while opposite found fast bands. These findings underscore potential of effectively distinguishing patients individuals, marking advance toward objective reliable diagnosis strategies.

Язык: Английский

Процитировано

12
A novel optimal wavelet filter banks for automated diagnosis of Alzheimer’s disease and mild cognitive impairment using Electroencephalogram signals DOI Creative Commons
Digambar Puri, Jayanand P. Gawande, Jaswantsing L. Rajput

и другие.

Decision Analytics Journal, Год журнала: 2023, Номер 9, С. 100336 - 100336

Опубликована: Окт. 5, 2023

Electroencephalogram (EEG) of Alzheimer's disease (AD) patients show a slowing effect and less synchronization. EEG signal's transient abrupt nature is captured from various mother wavelets. However, better performance can be obtained by balancing time-frequency localization in wavelet filters. We propose new approach for designing filter banks based on optimal four-step lifting structure (FSLS). First, we design FSLS using Euler's Frobenius half-band polynomial (EFHBP). The perfect reconstruction condition vanishing moments are imposed EFHBP to achieve maximum flat filters (HBFs). HBFs optimized balanced uncertainty metric obtain spread balance. Afterward, these used the synthesis analysis banks. proposed biorthogonal (TFOBWFBs) achieved balance between localization. Further, TFBOBWFBs applied decompose signals AD patients. Twenty different features were extracted decomposed subbands, which twelve significant selected Kruskal Walli's test. machine learning models trained tested with 10-fold cross-validation leave-one-subject-out cross-validation. To validate this study, TFOBWFBs have been two publicly available datasets mild cognitive impairment (MCI), AD, healthy control (HC) subjects. 98.90% accuracy 2-way (AD vs. HC) 96.50% 3-way MCI classification support vector model method outperforms existing detection techniques. framework optimization more fast compared previous studies. Also, detect other neurodegenerative disorders.

Язык: Английский

Процитировано

11
A Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy DOI Creative Commons
Andrea Cataldo, Sabatina Criscuolo, Egidio De Benedetto

и другие.

Bioengineering, Год журнала: 2024, Номер 11(4), С. 324 - 324

Опубликована: Март 27, 2024

Alzheimer's disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques questionnaires, present limitations such as invasiveness, high costs, subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, temporal resolution. this regard, work introduces novel metric by multiscale fuzzy entropy (MFE) assess complexity, offering clinicians an objective, cost-effective tool aid early intervention patient care. To purpose, patterns different frequency bands 35 healthy subjects (HS) patients were investigated. Then, based on the resulting MFE values, specific algorithm, able complexity abnormalities are typical of AD, was developed further validated 24 EEG test recordings. This MFE-based method achieved accuracy 83% differentiating between HS with odds ratio 25, Matthews correlation coefficient 0.67, indicating viability diagnosis. Furthermore, algorithm showed potential identifying anomalies when tested subject mild impairment (MCI), warranting investigation future research.

Язык: Английский

Процитировано

4
Insights into computer-aided EEG signal processing for traumatic brain injury assessment: A review DOI

Farzaneh Manzari,

Peyvand Ghaderyan

Measurement, Год журнала: 2025, Номер unknown, С. 117279 - 117279

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection DOI Creative Commons
Wei Tong, Weiqi Yue, Fangni Chen

и другие.

Sensors, Год журнала: 2024, Номер 24(13), С. 4234 - 4234

Опубликована: Июнь 29, 2024

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels which leads to tissue ischemia and hypoxia ultimately results cell necrosis. Without timely effective treatment early time window, ischemic can lead long-term disability even death. Therefore, rapid detection crucial patients with stroke. In this study, we developed deep learning model based on fusion features extracted from electroencephalography (EEG) signals for fast Specifically, recruited 20 who underwent EEG examination during acute phase collected 19 adults no history as control group. Afterwards, constructed correlation-weighted Phase Lag Index (cwPLI), novel feature, explore synchronization information functional connectivity between channels. Moreover, spatio-temporal nonlinear complexity were fused combining cwPLI matrix Sample Entropy (SaEn) together further improve discriminative ability model. Finally, MSE-VGG network was employed classifier distinguish non-ischemic data. Five-fold cross-validation experiments demonstrated that proposed possesses excellent performance, accuracy, sensitivity, specificity reaching 90.17%, 89.86%, 90.44%, respectively. Experiments consumption verified method superior other state-of-the-art examinations. This study contributes advancement stroke, shedding light untapped potential demonstrating efficacy identification.

Язык: Английский

Процитировано

3
ComBat models for harmonization of resting-state EEG features in multisite studies DOI Creative Commons
Alberto Jaramillo‐Jimenez, Diego Tovar, Yorguin-José Mantilla-Ramos

и другие.

Clinical Neurophysiology, Год журнала: 2024, Номер 167, С. 241 - 253

Опубликована: Сен. 24, 2024

Язык: Английский

Процитировано

3