[HTML][HTML] Advances in respiratory monitoring: a comprehensive review of wearable and remote technologies
D Vitazkova, E Foltan, H Kosnacova, M Micjan… - Biosensors, 2024 - mdpi.com
This article explores the importance of wearable and remote technologies in healthcare. The
focus highlights its potential in continuous monitoring, examines the specificity of the issue …
focus highlights its potential in continuous monitoring, examines the specificity of the issue …
A novel deep learning technique for morphology preserved fetal ECG extraction from mother ECG using 1D-CycleGAN
The non-invasive fetal electrocardiogram (fECG) enables easy detection of developing heart
abnormalities, leading to a significant reduction in infant mortality rate and post-natal …
abnormalities, leading to a significant reduction in infant mortality rate and post-natal …
[HTML][HTML] A shallow U-Net architecture for reliably predicting blood pressure (BP) from photoplethysmogram (PPG) and electrocardiogram (ECG) signals
Cardiovascular diseases are the most common causes of death around the world. To detect
and treat heart-related diseases, continuous blood pressure (BP) monitoring along with …
and treat heart-related diseases, continuous blood pressure (BP) monitoring along with …
[HTML][HTML] Machine learning-based respiration rate and blood oxygen saturation estimation using photoplethysmogram signals
The continuous monitoring of respiratory rate (RR) and oxygen saturation (SpO2) is crucial
for patients with cardiac, pulmonary, and surgical conditions. RR and SpO2 are used to …
for patients with cardiac, pulmonary, and surgical conditions. RR and SpO2 are used to …
Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs
Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has
become a potential field of research in recent years. Although several studies have been …
become a potential field of research in recent years. Although several studies have been …
Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals
Gait analysis is helpful for rehabilitation, clinical diagnoses, and sporting activities. Among
the gathered signals, ground reaction forces (GRF) may be used for assisting doctors in …
the gathered signals, ground reaction forces (GRF) may be used for assisting doctors in …
[HTML][HTML] Thermal change index-based diabetic foot thermogram image classification using machine learning techniques
Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot
thermogram images acquired using an infrared camera have been shown to detect changes …
thermogram images acquired using an infrared camera have been shown to detect changes …
Motion artifacts correction from EEG and fNIRS signals using novel multiresolution analysis
Physiological signal measurement and processing are increasingly becoming popular in the
ambulatory setting as the hospital-centric treatment is moving towards wearable and …
ambulatory setting as the hospital-centric treatment is moving towards wearable and …
[HTML][HTML] Lightweight end-to-end deep learning solution for estimating the respiration rate from photoplethysmogram signal
Respiratory ailments are a very serious health issue and can be life-threatening, especially
for patients with COVID. Respiration rate (RR) is a very important vital health indicator for …
for patients with COVID. Respiration rate (RR) is a very important vital health indicator for …
LGI-rPPG-Net: A shallow encoder-decoder model for rPPG signal estimation from facial video streams
A method to accurately estimate physiological signals from video streams at a minimal cost
is invaluable. The importance of such a technique in pre-clinical health monitoring cannot be …
is invaluable. The importance of such a technique in pre-clinical health monitoring cannot be …