Ubiquitous affective computing: A review
R Assabumrungrat, S Sangnark… - IEEE Sensors …, 2021 - ieeexplore.ieee.org
This review investigated research works on affective computing by using electrocardiogram
(ECG) and electrodermal activity (EDA). The 27 related research papers, including 23 from …
(ECG) and electrodermal activity (EDA). The 27 related research papers, including 23 from …
[HTML][HTML] Deep learning for ECG classification: A comparative study of 1D and 2D representations and multimodal fusion approaches
The improved diagnosis of cardiovascular diseases (CVD) from electrocardiograms (ECG)
may help prevent their severity. Since Deep Learning (DL) became popular, several DL …
may help prevent their severity. Since Deep Learning (DL) became popular, several DL …
Comparison of the representational ability in individual difference analysis using 2-D time-series image and time-series feature patterns
J Li, Q Wang - Expert Systems with Applications, 2023 - Elsevier
Physiological signals, as crucial indicators for measuring physical health, must be acquired
by model learning, but the modeling and analysis performance is strongly affected by …
by model learning, but the modeling and analysis performance is strongly affected by …
Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling
This research presents a machine learning modeling process for detecting mental fatigue
using three physiological signals: electrodermal activity, electrocardiogram, and respiration …
using three physiological signals: electrodermal activity, electrocardiogram, and respiration …
Stability and Phase Response Analysis of Optimum Reduced‐Order IIR Filter Designs for ECG R‐Peak Detection
H Amhia, AK Wadhwani - Journal of Healthcare Engineering, 2022 - Wiley Online Library
Cardiovascular health and training success can be assessed using electrocardiogram
(ECG) data. For over a quarter of a century, an individual's resting heart rate is varying more …
(ECG) data. For over a quarter of a century, an individual's resting heart rate is varying more …
[HTML][HTML] Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution
Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing
essential information on electrical hearth activity. However, inherent noise and limited …
essential information on electrical hearth activity. However, inherent noise and limited …
A deep learning framework for electrocardiogram (ecg) super resolution and arrhythmia classification
CP Kaniraja, D Mishra - Research on Biomedical Engineering, 2024 - Springer
Purpose The preferred sampling rate for recording the electrocardiogram (ECG) data as per
industry standards is roughly 500 Hz. For the remote monitoring of patients, the transmission …
industry standards is roughly 500 Hz. For the remote monitoring of patients, the transmission …
Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12‐Lead ECGs Based on Left Atrial Remodeling
JH Choi, SH Song, H Kim, J Kim, H Park… - Journal of the …, 2024 - ahajournals.org
Background We hypothesized that analysis of serial ECGs could predict new‐onset atrial
fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac …
fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac …
Deep Learning based classification of ECG signals using RNN and LSTM Mechanism
V Satheeswaran, GN Chandrika, A Mitra… - Journal of Electronics …, 2024 - jeeemi.org
The Electrocardiogram (ECG) stands as a pivotal tool in cardiovascular disease diagnosis,
widely embraced within clinical domains for its simplicity and effectiveness. This paper …
widely embraced within clinical domains for its simplicity and effectiveness. This paper …
Sampling rate requirement for accurate calculation of heart rate and its variability based on the electrocardiogram
Objective. To develop analytical formulas which can serve as quantitative guidelines for the
selection of the sampling rate for the electrocardiogram (ECG) required to calculate heart …
selection of the sampling rate for the electrocardiogram (ECG) required to calculate heart …