AIMC Topic: Blood Pressure Determination
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The Hemodynamic Parameters Values Prediction on the Non-Invasive Hydrocuff Technology Basis with a Neural Network Applying.
Sensors (Basel, Switzerland)
Jun 1, 2022
The task to develop a mechanism for predicting the hemodynamic parameters values based on non-invasive hydrocuff technology of a pulse wave signal fixation is described in this study. The advantages and disadvantages of existing methods of recording ...
A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation.
Neural networks : the official journal of the International Neural Network Society
Apr 22, 2022
Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases. Traditional techniques for measuring BP are either invasive or cuff-based and thus are not suitabl...
An Artificial Intelligence-Enhanced Blood Pressure Monitor Wristband Based on Piezoelectric Nanogenerator.
Biosensors
Apr 11, 2022
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 complications. Blood pressure is an important indicator for diagnosing hypertension. Currently, blood pr...
Intelligent Bio-Impedance System for Personalized Continuous Blood Pressure Measurement.
Biosensors
Feb 28, 2022
Continuous blood pressure (BP) measurement is crucial for long-term cardiovascular monitoring, especially for prompt hypertension detection. However, most of the continuous BP measurements rely on the pulse transit time (PTT) from multiple-channel ph...
Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review.
IEEE reviews in biomedical engineering
Jan 20, 2022
Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The us...
Artificial Intelligence (AI) approach to identifying factors that determine systolic blood pressure in type 2 diabetes (study from the LOOK AHEAD cohort).
Diabetes & metabolic syndrome
Sep 11, 2021
BACKGROUND AND AIMS: Artificial Intelligence (AI) methods have recently become critical for research in diabetes in the era of big-data science.
Discrimination of vascular aging using the arterial pulse spectrum and machine-learning analysis.
Microvascular research
Sep 8, 2021
Aging contributes to the progression of vascular dysfunction and is a major nonreversible risk factor for cardiovascular disease. The aim of this study was to determine the effectiveness of using arterial pulse-wave measurements, frequency-domain pul...
Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.
Sensors (Basel, Switzerland)
Sep 8, 2021
Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG ...
Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.
Scientific reports
Aug 3, 2021
In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of ...
Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features.
Scientific reports
Jun 29, 2021
The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. Howe...
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