Abstract

Blood pressure measurement is a significant part of preventive healthcare and has been widely used in clinical risk and disease management. However, conventional measurement does not provide continuous monitoring and sometimes is inconvenient with a cuff. In addition to the traditional cuff-based blood pressure measurement methods, some researchers have developed various cuff-less and noninvasive blood pressure monitoring methods based on Pulse Transit Time (PTT). Some emerging methods have employed features of either photoplethysmogram (PPG) or electrocardiogram (ECG) signals, although no studies to our knowledge have employed the combined features from both PPG and ECG signals. Therefore this study aims to investigate the performance of a predictive, machine learning blood pressure monitoring system using both PPG and ECG signals. It validates that the employment of the combination of PPG and ECG signals has improved the accuracy of the blood pressure estimation, compared with previously reported results based on PPG signal only. © 2018 Institution of Engineering and Technology. All rights reserved.

Highlights

  • Blood pressure is an important indicator in healthcare

  • This study aims to explore whether the combination of Pulse Transit Time (PTT) with both PPG and ECG features can improve the accuracy of the prediction of blood pressure, compared with a study based on the combination of PTT with PPG features only [16]

  • Comparing the Lasso regression with the support vector machine (SVM), it can be seen that the SVM has smaller mean absolute error (MAE) and standard deviation (STD) values

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Summary

Introduction

Blood pressure is an important indicator in healthcare. It has been widely recognized as a useful metric for the improvement of survival rates of patients and the prevention of cardiovascular diseases. Modern science and innovative technologies have brought about a revolution in many interdisciplinary research areas related to healthcare, such as miniaturized wearable devices, artificial intelligence, and smart mobile phones for medical care purposes [4]. These factors have promoted the development and adoption of ubiquitous blood pressure measurement and monitoring

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