Unrestrained Diastolic Blood Pressure Estimation Method Using Heartbeat during Sleep

2018 ◽  
Author(s):  
Tomomasa Yamasaki ◽  
Hikaru Iwasaki ◽  
Takashi Kaburagi ◽  
Yosuke Kurihara
Keyword(s):  
Blood Pressure ◽  
Diastolic Blood Pressure ◽  
Estimation Method ◽  
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

scholarly journals Cuffless Blood Pressure Estimation Based on Data-Oriented Continuous Health Monitoring System

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kengo Atomi ◽  
Haruki Kawanaka ◽  
Md. Shoaib Bhuiyan ◽  
Koji Oguri
Keyword(s):  
Blood Pressure ◽  
Time Series Data ◽  
Estimation Method ◽  
Series Data ◽  
Healthy Life ◽  
Blood Pressure Data ◽  
Pressure Estimation ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure ◽  
Lifestyle Related Diseases

Measuring blood pressure continuously helps monitor health and also prevent lifestyle related diseases to extend the expectancy of healthy life. Blood pressure, which is nowadays used for monitoring patient, is one of the most useful indexes for prevention of lifestyle related diseases such as hypertension. However, continuously monitoring the blood pressure is unrealistic because of discomfort caused by the tightening of a cuff belt. We have earlier researched the data-oriented blood pressure estimation without using a cuff. Remarkably, our blood pressure estimation method only uses a photoplethysmograph sensor. Therefore, the application is flexible for sensor locations and measuring situations. In this paper, we describe the implementation of our estimation method, the launch of a cloud system which can collect and manage blood pressure data measured by a wristwatch-type photoplethysmograph sensor, and the construction of our applications to visualize life-log data including the time-series data of blood pressure.

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