Detection of Cognitive Performance Deterioration Due to Cold-Air Exposure in Females Using Wearable Electrodermal Activity and Electrocardiogram

Biosensors, Journal Year: 2025, Volume and Issue: 15(2), P. 78 - 78

Published: Jan. 29, 2025

Prolonged exposure to cold air can impair reaction time and cognitive function, which lead serious consequences. One mitigation strategy is develop models that predict performance by tracking physiological metrics associated with stress. As females are evidenced be more sensitive exposure, this study investigated the relationship between deterioration of female subjects under Wearable electrodermal activity (EDA) electrocardiogram (ECG) were collected from nineteen who underwent five sessions a task battery—assessing time, memory, attention—in (10 °C) environment. Machine learning classifiers showed higher classification accuracies heart rate variability (HRV) features than EDA features. Particularly in detecting assessing short-term our support vector machine classifier HRV an 82.4% accuracy, sensitivity 84.2% specificity 80.6%, whereas 55.4% accuracy 44.7% 66.7% was obtained Our results demonstrate feasibility using wearable ECG, allowing for preventive measures reduce risk environments, especially military personnel.

Language: Английский

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Stress Detection Based on EEG Values: A Systematic Literature Review

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 319 - 331

Published: Jan. 1, 2025

Language: Английский

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A neural approach to the Turing Test: The role of emotions

Neural Networks, Journal Year: 2025, Volume and Issue: unknown, P. 107362 - 107362

Published: March 1, 2025

Language: Английский

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HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals

Brain Informatics, Journal Year: 2024, Volume and Issue: 11(1)

Published: Aug. 21, 2024

Abstract Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES with high accuracy from electroencephalogram (EEG) signals. The early of crucial for timely medical intervention and prevention further injuries the patients. This work proposes a robust deep learning framework called HyEpiSeiD extracts self-trained features pre-processed EEG signals using hybrid combination convolutional neural network followed by two gated recurrent unit layers performs prediction based on those extracted features. proposed evaluated public datasets, UCI Epilepsy Mendeley datasets. model achieved 99.01% 97.50% classification accuracy, respectively, outperforming most state-of-the-art methods in epilepsy domain.

Language: Английский

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Ensemble Learning Techniques for Classifying Stressed and Unstressed Textual Data

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS), Journal Year: 2024, Volume and Issue: 14, P. 1 - 7

Published: Feb. 24, 2024

In today's digital age, occasional mild stress is commonplace, but excessive can significantly affect mental health. Early prediction of levels vital for preventing adverse effects. Automated systems are crucial accurate predictions, and sentiment analysis, which decodes online conversations, plays a key role. This research focuses on classifying textual data from conversations into unstressed categories using datasets X Reddit. The study aimed to improve analysis detection in by comparing machine learning approaches. utilized NLP techniques algorithms classify non-stress, achieving high accuracy precision. Employing machine-learning classifiers Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Ensemble Voting Classifier (EVC), the EVC stood out, 87% Twitter dataset surpassing 92% Reddit dataset, demonstrating its effectiveness classification. It found that ensemble methods, particularly method, show promise addressing complexities detection.

Language: Английский

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Stressor Type Matters! --- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection

Published: Oct. 30, 2024

Language: Английский

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Sustainability-Driven Hourly Energy Demand Forecasting in Bangladesh Using Bi-LSTMs

Procedia Computer Science, Journal Year: 2024, Volume and Issue: 236, P. 41 - 50

Published: Jan. 1, 2024

This research presents a comprehensive study on developing and evaluating deep learning-based forecasting model for hourly energy demand prediction in Bangladesh. Leveraging novel dataset obtained from the Power Grid Company of Bangladesh (PGCB), proposed utilizes bi-directional long short-term memory networks (Bi-LSTMs), implemented through Tensor-Flow Keras libraries. The meticulously preprocesses data, handling missing values ensuring compatibility with selected models. models are trained evaluated using Mean Absolute Error (MAE) Squared (MSE) metrics, revealing promising results 376.72 MAE. experimental findings demonstrate effectiveness developed model, showcasing its capability to predict accurately. insights derived this pave way enhanced management strategies, fostering sustainable efficient utilization practices.

Language: Английский

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Performance Analysis of a Single-Input Thermal Image Classifier with Patient Information for the Detection of Breast Cancer

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 148 - 168

Published: Jan. 1, 2024

Language: Английский

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Classifying Depressed and Healthy Individuals Using Wearable Sensor Data: A Comparative Analysis of Classical Machine Learning Approaches

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 126 - 147

Published: Jan. 1, 2024

Language: Английский

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Ultrasonic stress detection and regulation in the whole machining process of thin-walled part

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: 134(5-6), P. 2459 - 2477

Published: Aug. 19, 2024

Language: Английский

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