How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
[HTML][HTML] A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics
Hypertension describes elevated blood pressure, which significantly impacts cardiovascular
diseases. Typically, a sphygmomanometer, a cuff-like device, is used to measure a patient's …
diseases. Typically, a sphygmomanometer, a cuff-like device, is used to measure a patient's …
Detecting beats in the photoplethysmogram: benchmarking open-source algorithms
The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches.
A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat …
A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat …
Tissue perfusion pressure enables continuous hemodynamic evaluation and risk prediction in the intensive care unit
Abstract Treatment of circulatory shock in critically ill patients requires management of blood
pressure using invasive monitoring, but uncertainty remains as to optimal individual blood …
pressure using invasive monitoring, but uncertainty remains as to optimal individual blood …
[HTML][HTML] A survey on data-driven approaches for reliability, robustness, and energy efficiency in wireless body area networks
Wireless Body Area Networks (WBANs) are pivotal in health care and wearable
technologies, enabling seamless communication between miniature sensors and devices …
technologies, enabling seamless communication between miniature sensors and devices …
A refined blood pressure estimation model based on single channel photoplethysmography
Y Zhang, X Ren, X Liang, X Ye… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
This study proposed a refined BP prediction strategy that using single-channel
photoplethysmography (PPG) signals to stratify populations by cardiovascular status before …
photoplethysmography (PPG) signals to stratify populations by cardiovascular status before …
Attention-based residual improved U-Net model for continuous blood pressure monitoring by using photoplethysmography signal
M Yu, Z Huang, Y Zhu, P Zhou, J Zhu - Biomedical Signal Processing and …, 2022 - Elsevier
Blood pressure (BP) is an important clinical indicator for cardiovascular health assessment,
and accurate monitoring of continuous BP is still a challenging task. In this paper, an …
and accurate monitoring of continuous BP is still a challenging task. In this paper, an …
Non-Invasive Blood Pressure Sensing via Machine Learning
In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using
photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML …
photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML …
Classifying sepsis from photoplethysmography
Purpose Sepsis is a life-threatening organ dysfunction. It is caused by a dysregulated
immune response to an infection and is one of the leading causes of death in the intensive …
immune response to an infection and is one of the leading causes of death in the intensive …
[HTML][HTML] Performance comparison of machine learning algorithms for the estimation of blood pressure using photoplethysmography
This paper deals with an in-depth performance analysis on the estimation of Systolic Blood
Pressure (SBP) and Diastolic Blood Pressure (DBP) by using features from the …
Pressure (SBP) and Diastolic Blood Pressure (DBP) by using features from the …