A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

N Musa, AY Gital, N Aljojo, H Chiroma… - Journal of ambient …, 2023‏ - Springer
The success of deep learning over the traditional machine learning techniques in handling
artificial intelligence application tasks such as image processing, computer vision, object …

Cross-subject EEG emotion recognition combined with connectivity features and meta-transfer learning

J Li, H Hua, Z Xu, L Shu, X Xu, F Kuang… - Computers in biology and …, 2022‏ - Elsevier
In recent years, with the rapid development of machine learning, automatic emotion
recognition based on electroencephalogram (EEG) signals has received increasing …

Sensing physiological and environmental quantities to measure human thermal comfort through machine learning techniques

N Morresi, S Casaccia, M Sorcinelli… - IEEE Sensors …, 2021‏ - ieeexplore.ieee.org
This paper presents the results from the experimental application of smartwatch sensors to
predict occupants' thermal comfort under varying environmental conditions. The goal is to …

IM-ECG: An interpretable framework for arrhythmia detection using multi-lead ECG

R Tao, L Wang, Y ** measurable affect
manifestations from multiple modalities of user input to affect labels. That map** is usually …

[HTML][HTML] ECG-based stress detection and productivity factors monitoring: the real-time production factory system

M Donati, M Olivelli, R Giovannini, L Fanucci - Sensors, 2023‏ - mdpi.com
Productivity and production quality have become primary goals for the success of
companies in all industrial and manufacturing sectors. Performance in terms of productivity …

Deep multimodal fusion for subject-independent stress detection

K Radhika, VRM Oruganti - … on cloud computing, data science & …, 2021‏ - ieeexplore.ieee.org
This paper explores the influence of convolutional layer in deep multimodal fusion
(intermediate fusion) for the detection of subject-independent stress using physiological …