Correlation between brain activity and comfort at different illuminances based on electroencephalogram signals during reading

Building and Environment, Journal Year: 2024, Volume and Issue: 261, P. 111694 - 111694

Published: June 7, 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 Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy

Bioengineering, Journal Year: 2024, Volume and Issue: 11(4), P. 324 - 324

Published: March 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.

Language: Английский

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FFT Power Relationships Applied to EEG Signal Analysis: A Meeting between Visual Analysis of EEG and Its Quantification.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 15, 2025

Abstract Objective This study presents a novel computational approach for analyzing electroencephalogram (EEG) signals, focusing on the distribution and variability of energy in different frequency bands. The proposed method, FFT Weed Plot, systematically encodes EEG spectral information into structured metrics that facilitate quantitative analysis. Methods methodology employs Fast Fourier Transform (FFT) to compute Power Spectral Density (PSD) signals. A encoding technique transforms band distributions six-entry vectors, referred as “words,” which serve basis three key metrics: scalar value vector , matrix H . These are evaluated using dataset comprising recordings from 30 healthy individuals 15 patients with epilepsy. Machine learning classifiers then applied assess discriminatory power features. Results classification models achieved 95.55% accuracy, 93.33% sensitivity, 96.67% specificity, demonstrating robustness distinguishing between control epileptic EEGs. Conclusions Plot method provides signal quantification, improving systematization analysis neurophysiological studies. developed could descriptors automated interpretation, offering potential applications clinical research settings. Highlights From domain probability theory, new ways information. step towards automation medical reading. New global description an recording their machine learning. We present new, reproducible, robust clinically designed improve objectivity practice neurophysiology.

Language: Английский

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A fractional-order Chua’s system: System model, numerical simulations, hidden dynamics, DSP implementation and voice encryption application

AEU - International Journal of Electronics and Communications, Journal Year: 2025, Volume and Issue: unknown, P. 155691 - 155691

Published: Jan. 1, 2025

Language: Английский

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Development and Metrological Characterization of Low-Cost Wearable Pulse Oximeter

Bioengineering, Journal Year: 2025, Volume and Issue: 12(3), P. 314 - 314

Published: March 19, 2025

Pulse oximetry is essential for monitoring arterial oxygen saturation (SpO2) and heart rate (HR) in various medical scenarios. However, the traditional pulse oximeters face challenges related to high costs, motion artifacts, susceptibility ambient light interference. This work presents a low-cost experimental oximeter prototype designed address these limitations through design advancements. The device incorporates 3D-printed finger support minimize artifacts excessive capillary pressure, along with an elastic element enhance stability. Unlike conventional transmission-based oximetry, employs reflectance-based measurement approach, improving versatility enabling reliable readings even cases of poor peripheral perfusion. Additionally, integration light-shielding materials mitigates effects illumination, ensuring accurate operation challenging environments such as surgical settings. Metrological characterization demonstrates that achieves accuracy comparable commercial GIMA Oxy-50 while maintaining production cost at approximately one-tenth alternatives. study highlights potential deliver affordable different applications.

Language: Английский

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Complexity and Non-Predictability in Neurodynamic: Gender-Specific EEG Dynamics Uncovered via Hidden Markov Models

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 26, 2025

One area of interest in neuroscience is the study differences between male and female brains, encompassing structural, physiological, neural activity, as well their implications for behavioral traits functional capabilities. In this study, we investigate complexity EEG signals men women propose hidden Markov model (HMM) method measuring which significantly improves accuracy gender-based classification compared to conventional signal measures. Using measure signal, enhanced results by reaching 86% decoding accuracy. Additionally, demonstrated that observed effect particularly dominant parietal, frontal central regions brain. Through filtering, are present across most frequency bins with high rate enhancement. It also noteworthy level women's brain activity higher than men's. The HMM showed methods nonlinearity, such entropy, Lyapunov Hurst exponent. Importantly, performance improvement was other methods. our finding entirely consistent previous studies. Overall, using a Hidden Model can more effectively extract complexity, enhancing EEG-based gender classification.

Language: Английский

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Automatic detection of Alzheimer’s disease from EEG signals using an improved AFS–GA hybrid algorithm

Cognitive Neurodynamics, Journal Year: 2024, Volume and Issue: 18(5), P. 2993 - 3013

Published: June 10, 2024

Language: Английский

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Entropy and Coherence Features in EEG-Based Classification for Alzheimer's Disease Detection

2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Journal Year: 2024, Volume and Issue: unknown

Published: May 20, 2024

Language: Английский

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Refined time-shift multiscale slope entropy: a new nonlinear dynamic analysis tool for rotating machinery fault feature extraction

Nonlinear Dynamics, Journal Year: 2024, Volume and Issue: 112(22), P. 19887 - 19915

Published: Aug. 5, 2024

Language: Английский

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