Hypertension

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection.

OBJECTIVE: Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time...

Photoplethysmogram based vascular aging assessment using the deep convolutional neural network.

Arterial stiffness due to vascular aging is a major indicator during the assessment of cardiovascular risk. In this study, we propose a method for age estimation by applying deep learning to a photoplethysmogram (PPG) for the non-invasive assessment of the vascular age. The proposed deep learning-based age estimation model consists of three convolutional layers and two fully connected layers, and ...

XGBoost Regression of the Most Significant Photoplethysmogram Features for Assessing Vascular Aging.

The purpose of this study was to confirm the potential of XGBoost as a vascular aging assessment model based on the photoplethysmogram (PPG) features ...

Prediction of blood pressure changes associated with abdominal pressure changes during robotic laparoscopic low abdominal surgery using deep learning.

BACKGROUND: Intraoperative hypertension and blood pressure (BP) fluctuation are known to be associated with negative patient outcomes. During robotic ...

End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions.

The correct assessment and characterization of heart anatomy and functionality is usually done through inspection of magnetic resonance image cine seq...

Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure.

Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses ...

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients.

To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a p...

Arterial Hypertension and the Hidden Disease of the Eye: Diagnostic Tools and Therapeutic Strategies.

Hypertension is a major cardiovascular risk factor that is responsible for a heavy burden of morbidity and mortality worldwide. A critical aspect of c...

Follow-Up of Robotic Mitral Valve Repair: A Single Tertiary Institution Experience in China.

Surgical treatment of mitral valve diseases has become minimally invasive. This study analyzed the follow-up results of patients after mitral valve re...

Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings.

Several artificial intelligence algorithms have been proposed to help diagnose glaucoma by analyzing the functional and/or structural changes in the e...

Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients.

Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitorin...

A Detailed Systematic Review on Retinal Image Segmentation Methods.

The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from ...

A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation.

Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases...

An Artificial Intelligence-Enhanced Blood Pressure Monitor Wristband Based on Piezoelectric Nanogenerator.

Hypertensive patients account for about 16% to 37% of the global population, and about 9.4 million people die each year from hypertension and its comp...

Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements.

BACKGROUND: Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiolog...

Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to ...

Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.

OBJECTIVE: We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approach...

Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks.

Identification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic...

Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia.

This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) in the efficacy evaluation of radiofrequency ablat...

An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension.

The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternat...