STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network

Cognitive Neurodynamics, Journal Year: 2024, Volume and Issue: 18(5), P. 3195 - 3208

Published: July 19, 2024

Language: Английский

---

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

Brain Research Bulletin, Journal Year: 2025, Volume and Issue: unknown, P. 111281 - 111281

Published: March 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.

Language: Английский

---

Different oscillatory mechanisms of dementia-related diseases with cognitive impairment in closed-eye state

NeuroImage, Journal Year: 2024, Volume and Issue: unknown, P. 120945 - 120945

Published: Nov. 1, 2024

Language: Английский

---

The application of brain–computer interface in Alzheimer’s disease studies based on machine learning algorithms

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 121 - 143

Published: Jan. 1, 2025

Language: Английский

---

Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer’s disease and frontotemporal dementia

Journal of Neuroscience Methods, Journal Year: 2025, Volume and Issue: 416, P. 110377 - 110377

Published: Jan. 31, 2025

Language: Английский

---

WEFormer: Classification for physiological time series with small sample sizes based on wavelet decomposition and time series foundation models

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107842 - 107842

Published: March 22, 2025

Language: Английский

---

EEG Channel and Feature Selection for Classification of Patients with Alzheimer’s Disease

IFMBE proceedings, Journal Year: 2025, Volume and Issue: unknown, P. 253 - 270

Published: Jan. 1, 2025

Language: Английский

---

Ensemble Learning-Based Alzheimer’s Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2881 - 2881

Published: May 2, 2025

Ensemble learning (EL), a machine technique that combines the results of multiple algorithms to obtain predicted values, aims achieve better predictive performance than single algorithm alone. Machine techniques, including EL, have been applied in field medicine assist clinical interpretation specific diseases. Although neurodegenerative diseases, especially Alzheimer’s disease (AD), are interest clinicians and researchers due their rapid increase cases, application EL AD diagnosis has relatively less attempted. In this research, we demonstrate three algorithms, trained on an ensemble electroencephalogram (EEG) clock drawing test (CDT) feature data for classification task, show improved detection accuracy compared when either EEG or CDT dataset is used independently. We also explore which contributes most decision-making healthy control (HC) classification. conclusion, current study suggests can be novel (ML) automated screening process.

Language: Английский

---

Exploring Functional Brain Networks in Alzheimer’s Disease Using Resting State EEG Signals

Journal of dementia and Alzheimer's disease, Journal Year: 2025, Volume and Issue: 2(2), P. 12 - 12

Published: May 2, 2025

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that disrupts functional brain connectivity, leading to cognitive and decline. Electroencephalography (EEG), noninvasive cost-effective technique, has gained attention as promising tool for studying network alterations in AD. This study aims leverage EEG-derived connectivity metrics differentiate between healthy controls (HC), subjective decline (SCD), mild impairment (MCI), AD, offering insights into progression. Methods: Using graph theory-based analysis, we extracted key from resting-state EEG signals, focusing on the betweenness centrality clustering coefficient. Statistical analysis was conducted across multiple frequency bands, discriminant applied evaluate classification performance of metrics. Results: Our findings revealed increase theta-band concurrent decrease alpha- beta-band centrality, reflecting AD-related reorganization. Among examined metrics, exhibited highest discriminative power distinguishing AD stages. Additionally, using comparable advanced deep learning models, highlighting their potential predictive biomarkers. Conclusions: demonstrate strong biomarkers early detection monitoring Their effectiveness capturing underscores value clinical diagnostic workflows, scalable interpretable alternative learning-based models classification.

Language: Английский

---

Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 487 - 487

Published: May 11, 2024

Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal EEG can be described through microstate analysis, which provides a discrete approximation continuous electric field generated by brain cortex. Here, we propose novel indicator, termed sequence non-randomness index (MSNRI). The essence method lies in initially generating transition state space compression data using analysis. Following this, assess these information-based similarity results suggest that this MSNRI metric potential marker distinguishing between health control (HC) frontotemporal (FTD) (HC vs. FTD: 6.958 5.756, p < 0.01), as well HC populations with Alzheimer’s disease (AD) AD: 5.462, 0.001). Healthy individuals exhibit more complex macroscopic structures non-random microstates, whereas disorders lead to random patterns. Additionally, extend proposed integrating Complementary Ensemble Empirical Mode Decomposition (CEEMD) explore microstates at specific frequency scales. Moreover, assessed effectiveness innovative predicting scores. demonstrate incorporation CEEMD-enhanced indicators significantly improved prediction accuracy Mini-Mental State Examination (MMSE) scores (R2 = 0.940). not only aids exploration large-scale neural changes but also offers robust tool characterizing dynamics transitions their impact on function.

Language: Английский

---