AIMC Topic: Blood Pressure

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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...

Non-invasive estimation of beat-by-beat aortic blood pressures from electrical impedance tomography data processed by machine learning.

Journal of clinical monitoring and computing
Hypotension in perioperative and intensive care settings is a significant risk factor associated with complications such as myocardial infarction and kidney injury thereby increasing perioperative complications and mortality. Continuous blood pressur...

Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management.

Hypertension (Dallas, Tex. : 1979)
Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and sub...

Machine learning analysis of integrated ABP and PPG signals towards early detection of coronary artery disease.

Scientific reports
Every year, Coronary Artery Disease (CAD) claims lives of over a million people. CAD occurs when the coronary arteries, responsible for supplying oxygenated blood to the heart, get occluded due to plaque deposits on their inner walls. The most critic...

Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study.

International journal of public health
OBJECTIVE: To develop a machine learning (ML) model utilizing transfer learning (TL) techniques to predict hypertension in children and adolescents across South America.

A spectral machine learning approach to derive central aortic pressure waveforms from a brachial cuff.

Proceedings of the National Academy of Sciences of the United States of America
Analyzing cardiac pulse waveforms offers valuable insights into heart health and cardiovascular disease risk, although obtaining the more informative measurements from the central aorta remains challenging due to their invasive nature and limited non...

Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.

Hypertension research : official journal of the Japanese Society of Hypertension
This study aims to investigate the association between visit-to-visit blood pressure variability (VVV) in early stage of continuous ambulatory peritoneal dialysis (CAPD) and long-term clinical outcomes, utilizing machine learning algorithms. Patients...

Diastolic Versus Systolic or Mean Intraoperative Hypotension as Predictive of Perioperative Myocardial Injury in a White-Box Machine-Learning Model.

Anesthesia and analgesia
BACKGROUND: Intraoperative hypotension (IOH) and tachycardia are associated with perioperative myocardial injury (PMI), and thereby increased postoperative mortality. Patients undergoing vascular surgery are specifically at risk of developing cardiac...

Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation.

Nature reviews. Cardiology
With advances in materials science and medical technology, wearable sensors have become crucial tools for the early diagnosis and continuous monitoring of numerous cardiovascular diseases, including arrhythmias, hypertension and coronary artery disea...