AIMC Topic: Blood Pressure

Clear Filters Showing 251 to 260 of 299 articles

Machine learning-based blood pressure estimation using impedance cardiography data.

Acta physiologica (Oxford, England)
OBJECTIVE: Accurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, treatment decision-making, and monitoring of cardiovascular diseases. Unfortunately, cuff-based BP measurements suffer from inaccuracies and discomfort...

Machine Learning Approaches for Blood Pressure Classification from Photoplethysmogram: A Comparative Analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The cuffless estimation of blood pressure (BP) has become a prominent area of research in recent years fueled by its potential clinical implications and the growing interest from the wearable device industry. It has been accelerated by the emergence ...

Minimally invasive monitor of cardiac output based on the machine-learning analysis of the pulse contour of the peripheral arterial pressure.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In the hemodynamic management of anesthetized patients during surgical operation, minimally invasive and accurate cardiac output (CO) monitoring is strongly required. We have developed a CO monitor based on the machine-learning analysis of the pulse ...

Machine Learning Algorithm to Estimate Cardiac Output Based On Less-Invasive Arterial Blood Pressure Measurements.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cardiac output (CO) is a vital hemodynamic parameter that reflects the blood volume pumped by the heart per minute. A less-invasive way to estimate CO is by analyzing arterial blood pressure (ABP) waveforms. However, the relationship between CO and b...

Advancing Cuffless Arterial Blood Pressure Waveform Estimation: Time-Series Deep Neural Network Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Existing deep learning models for arterial blood pressure (ABP) estimation are becoming increasingly complex. They mainly treat the estimation as a sequence-to-sequence (seq2seq) task, to establish the relationship between input physiological signals...

Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Blood pressure variability (BPV) plays a critical role in vascular diseases, particularly in acute ischemic stroke patients in intensive care units (ICUs), where higher BPV correlates with increased mortality rates. Current interventions lack effecti...

TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnor...

Prediction of intradialytic hypotension using pre-dialysis features-a deep learning-based artificial intelligence model.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
BACKGROUND: Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD) that is associated with increased risks of cardiovascular morbidity and mortality. However, its accurate prediction remains a clinical challenge. The aim of th...

Demographic Information Fusion Using Attentive Pooling In CNN-GRU Model For Systolic Blood Pressure Estimation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Fusing demographic information into deep learning models has become of interest in recent end-to-end cuff-less blood pressure (BP) estimation studies in order to achieve improved performance. Conventionally, the demographic feature vector is concaten...