Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Sensors, Journal Year: 2023, Volume and Issue: 23(14), P. 6434 - 6434

Published: July 16, 2023

The electroencephalography (EEG) signal is a noninvasive and complex that has numerous applications in biomedical fields, including sleep the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing feature extraction methods to analyze EEG signals. In this study, we comprehensive review of articles related processing. We searched major scientific engineering databases summarized results our findings. Our survey encompassed entire process processing, from acquisition pretreatment (denoising) extraction, classification, application. present detailed discussion comparison various techniques used for Additionally, identify current limitations these their future development trends. conclude by offering some suggestions research field

Language: Английский

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A novel hybrid model in the diagnosis and classification of Alzheimer's disease using EEG signals: Deep ensemble learning (DEL) approach

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 89, P. 105751 - 105751

Published: Nov. 17, 2023

Language: Английский

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Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia

Molecular Psychiatry, Journal Year: 2024, Volume and Issue: 29(4), P. 1088 - 1098

Published: Jan. 24, 2024

Language: Английский

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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

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ML-Powered Handwriting Analysis for Early Detection of Alzheimer’s Disease

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 69031 - 69050

Published: Jan. 1, 2024

Alzheimer's disease (AD) is a progressive, incurable condition leading to decline of nerve cells and cognitive functions over time. Early detection essential for improving quality life, as treatment strategies aim decelerate its progression. AD also impacts fine motor control, including handwriting. Utilizing machine learning (ML) with efficient data analysis methods early through handwriting holds considerable promise clinical diagnosis, albeit challenging undertaking. In this study, we address complexity by employing ensemble learning, which amalgamates diverse ML algorithms enhance predictive performance. Our approach involves developing an model kinetics, utilizing the stacking technique integrate multiple base-level classifiers. The study encompasses 174 individuals, 89 diagnosed 85 healthy participants, drawn from DARWIN dataset (Diagnosis AlzheimeR WIth haNdwriting). To discern most effective base classifiers, employ both Repeated-k-fold Monte-Carlo Cross Validation techniques. Subsequently, top k features are selected each best-performing classifier using variance (ANOVA) recursive feature elimination (RFE). final step consolidating predictions classifiers ensemble, resulting in impressive achieves 97.14% accuracy, 95% sensitivity, 100% specificity, precision, 97.44% F1-score, 94.37% Matthews Correlation Coefficient (MCC), 94.21% Cohen Kappa, 97.5% Area Under Receiver Operating Characteristic Curve (AUC-ROC). Comparative performance demonstrates that our proposed surpasses all state-of-the-art models based on prediction. These findings underscore potential offer highly accurate affordable non-invasive manner, emphasizing significant utility, particularly analysis.

Language: Английский

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LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection

Physical and Engineering Sciences in Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: June 11, 2024

Language: Английский

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LEADNet: Detection of Alzheimer’s Disease Using Spatiotemporal EEG Analysis and Low-Complexity CNN

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 113888 - 113897

Published: Jan. 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.

Language: Английский

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A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(4), P. 335 - 335

Published: March 29, 2024

Early-stage Alzheimer’s disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, extraction, supervised to distinguish between patients, as well healthy controls (HCs). model, with a 90% overlap SVD entropy K-Nearest Neighbors (KNN) learning, achieved mean F1-score accuracy 93% 91%, 92.5% 93%, 91.5% 91% discriminating HC, FTD, respectively. The importance array, explainable AI feature, highlighted brain lobes contributed distinguishing biomarkers. This research introduces novel framework detecting using EEG signals, addressing need accurate early-stage diagnostics. Furthermore, comparative evaluation methods on AD/FTD detection discrimination is documented.

Language: Английский

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Introduction to Large Language Models (LLMs) for dementia care and research

Frontiers in Dementia, Journal Year: 2024, Volume and Issue: 3

Published: May 14, 2024

Dementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities social engagement. In light of the recent advent Large Language Models (LLMs) such as ChatGPT, this paper aims thoroughly analyse their potential applications usefulness dementia care research.

Language: Английский

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Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review

Diagnostics, Journal Year: 2024, Volume and Issue: 14(12), P. 1281 - 1281

Published: June 17, 2024

Alzheimer’s disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild impairment (MCI) (middle stage), dementia. Early diagnosis of crucial can improve survival rates among patients. Traditional methods for diagnosing regular checkups manual examinations are challenging. Advances in computer-aided systems (CADs) have led the development various artificial intelligence deep learning-based rapid detection. This survey aims explore different modalities, feature extraction methods, datasets, machine learning techniques, validation used We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific Frontiers PeerJ (2), Hindawi IO Press (1), other multiple sources (2). The review presented tables ease reference, allowing readers quickly grasp key findings each study. Additionally, this addresses challenges current literature emphasizes importance interpretability explainability understanding model predictions. primary goal assess existing techniques identification highlight obstacles guide future research.

Language: Английский

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