scholarly journals A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data

2021 ◽  
Vol 68 ◽  
pp. 102813
Author(s):  
Erick Martinez-Ríos ◽  
Luis Montesinos ◽  
Mariel Alfaro-Ponce ◽  
Leandro Pecchia
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Physiological Data ◽  
Hypertension Detection ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

scholarly journals Enhancement of blood pressure estimation method via machine learning

2021 ◽  
Vol 60 (6) ◽  
pp. 5779-5796
Author(s):  
Nashat Maher ◽  
G.A. Elsheikh ◽  
W.R. Anis ◽  
Tamer Emara
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Estimation Method ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

Investigation on the effect of Womersley number, ECG and PPG features for cuff less blood pressure estimation using machine learning

2020 ◽  
Vol 60 ◽  
pp. 101942 ◽  
Author(s):  
Geerthy Thambiraj ◽  
Uma Gandhi ◽  
Umapathy Mangalanathan ◽  
V. Jeya Maria Jose ◽  
M. Anand
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Womersley Number ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

scholarly journals Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1160 ◽  
Author(s):  
Monika Simjanoska ◽  
Martin Gjoreski ◽  
Matjaž Gams ◽  
Ana Madevska Bogdanova
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Machine Learning Techniques ◽  
Invasive Blood Pressure ◽  
Non Invasive ◽  
Learning Techniques ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods

2020 ◽  
Vol 164 ◽  
pp. 107279 ◽  
Author(s):  
Ahmed S. Alghamdi ◽  
Kemal Polat ◽  
Abdullah Alghoson ◽  
Abdulrahman A. Alshdadi ◽  
Ahmed A. Abd El-Latif
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Estimation Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

scholarly journals Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis

Computation ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 46 ◽  
Author(s):  
Francesco Rundo ◽  
Alessandro Ortis ◽  
Sebastiano Battiato ◽  
Sabrina Conoci
Keyword(s):  
Blood Pressure ◽  
Ad Hoc ◽  
Diastolic Pressure ◽  
Skin Surface ◽  
Physiological Data ◽  
Measuring Device ◽  
Physiological Signal ◽  
Non Invasive ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

Blood Pressure (BP) is one of the most important physiological indicators that provides useful information in the field of health-care monitoring. Blood pressure may be measured by both invasive and non-invasive methods. A novel algorithmic approach is presented to estimate systolic and diastolic blood pressure accurately in a way that does not require any explicit user calibration, i.e., it is non-invasive and cuff-less. The approach herein described can be applied in a medical device, as well as in commercial mobile smartphones by an ad hoc developed software based on the proposed algorithm. The authors propose a system suitable for blood pressure estimation based on the PhotoPlethysmoGraphy (PPG) physiological signal sampling time-series. Photoplethysmography is a simple optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is non-invasive since it takes measurements at the skin surface. In this paper, the authors present an easy and smart method to measure BP through careful neural and mathematical analysis of the PPG signals. The PPG data are processed with an ad hoc bio-inspired mathematical model that estimates systolic and diastolic pressure values through an innovative analysis of the collected physiological data. We compared our results with those measured using a classical cuff-based blood pressure measuring device with encouraging results of about 97% accuracy.

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