Pengukuran Tekanan Darah Non-Invasive Tanpa Manset Menggunakan Metode Pulse Transit Time Berbasis Machine Learning Multivariat Regresi

Authors

  • Ernia Susana Poltekkes Jakarta II

DOI:

https://doi.org/10.26630/jk.v10i1.1085

Keywords:

Machine learning, NIBP, Oscillometric, PTT, PPG

Abstract

Currently used non-invasive blood pressure (NIBP) measurements (oscillometric method) has disadvantages related to pumping cuffs which can cause discomfort for patients due to pressure from pumping cuffs.  The aim of this study was to measure blood pressure in a non-invasive manner without cuffs with the Pulse Transit Time (PTT) methodbase on machine learning technology.The blood pressure measurement by the PTT method is obtained from the calculation of the distance of the R-ECG wave with the peak signal photoplethysmogram (PPG). The main problem of the PTT method in some previous studies is that the estimation of systolic (SBP) and diastolic (DBP) values is still inaccurate. The blood pressure measurement method in this study used a combination of PTT calculations with machine learning multivariate regression. Therefore expected to obtain a more accurate estimate of systolic (SBP) and diastolic blood pressure (DBP). This study is a laboratory experiment research on 30 healthy volunteers aged 20 ± 1 years. The measurement of the blood pressure value of the PTT-to-oscillometric method is 5 ± 5 mmHg. The blood pressure values generated by this PTT method have a p-value for the sequential estimation of SBP and DBP of 0.7374 and 0.0262.

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Published

11-05-2019

Issue

Vol. 10 No. 1 (2019): Jurnal Kesehatan

Section

Articles

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