AIMC Topic: Blood Pressure

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Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

PloS one
In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significant challenges due to the dynamic interaction between physiological and clinical markers. This study aims to uncover these subtle interconnections and i...

Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.

Annals of medicine
BACKGROUND: High intraoperative blood pressure variability (HIBPV) is significantly associated with postoperative adverse complications. However, practical tools to characterize perioperative factors associated with HIBPV remain limited. This study a...

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population.

Scientific reports
Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to cat...

Evolving Blood Pressure Estimation: From Feature Analysis to Image-Based Deep Learning Models.

Journal of medical systems
Traditional cuffless blood pressure (BP) estimation methods often require collecting physiological signals, such as electrocardiogram (ECG) and photoplethysmography (PPG), from two distinct body sites to compute metrics like pulse transit time (PTT) ...

Filter-type neural network-based counter-pulsation control in pulsatile ECMO: improving heartbeat-pulse discrimination and synchronization accuracy.

Biomedical engineering online
Implementing counter-pulsation (CP) control in pulsatile extracorporeal membrane oxygenator (p-ECMO) systems offers a refined approach to mitigate risks commonly associated with conventional ECMOs. To attain CP between the p-ECMO and heart, accurate ...

A supervised machine learning approach with feature selection for sex-specific biomarker prediction.

NPJ systems biology and applications
Biomarkers are crucial in aiding in disease diagnosis, prognosis, and treatment selection. Machine learning (ML) has emerged as an effective tool for identifying novel biomarkers and enhancing predictive modelling. However, sex-based bias in ML algor...

Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism.

Scientific reports
Blood pressure (BP) serves as a fundamental indicator of cardiovascular health, measuring the force exerted by circulating blood against arterial walls during each heartbeat. This paper introduces an advanced deep learning framework for precise, non-...

Circulating fibroblast growth factor 21 is associated with blood pressure in the Chinese population: a community-based study.

Annals of medicine
BACKGROUND: Our research team previously found that fibroblast growth factor (FGF) 21, a circulating hormone, was significantly associated with atherosclerosis in human and animal models. The relationship between FGF21 and blood pressure (BP) is rare...

Cuffless Blood Pressure Measurement: Where Do We Actually Stand?

Hypertension (Dallas, Tex. : 1979)
Cuffless blood pressure (BP) measurement offers considerable potential for clinical practice but is a challenging technological field. Many are investigating pulse wave analysis with or without pulse arrival time in which machine learning is applied ...

Transfer Learning Enhanced Blood Pressure Monitoring Based on Flexible Optical Pulse Sensing Patch.

ACS sensors
Blood pressure (BP), a crucial health biomarker, is essential for detecting early indications of cardiovascular disease in routine monitoring and clinical surveillance of inpatients. However, conventional cuff-based BP measurements are limited in pro...