Estimating Blood Pressure via Artificial Neural Networks Based on Measured Photoplethysmography Waveforms

K. N.G. Priyanka, Chang-Po Chao, Tse Yi Tu, Yung Hua Kao, Ming Hua Yeh, Rajeev Pandey, Fitrah P. Eka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


A new approach for estimating blood pressure from photoplethysmography (PPG) signals is developed using artificial neural networks (ANNs). Blood Pressure is one of the most important parameters that can provide valuable information of personal healthcare. A reflective photoplethysmography (PPG) sensor module is developed for the cuffless, non-invasive blood pressure (BP) measurement based on PPG at wrist on radial artery. Blood Pressure is in a relation with the pulse duration of the PPG. In this paper, we propose to estimate blood pressure from PPG signal by using artificial neural networks approach. This is the first reported study to consider varied temporal periods of PPG waveforms as features for application of artificial neural networks (ANNs) to estimate blood pressure. We compared our results with those measured using a commercial cuff-based digital blood pressure measuring device and obtained encouraging results of overall SBP and DBP regression (R) as 0.99115.

Original languageEnglish
Title of host publication2018 IEEE SENSORS, SENSORS 2018 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647073
StatePublished - 26 Dec 2018
Event17th IEEE SENSORS Conference, SENSORS 2018 - New Delhi, India
Duration: 28 Oct 201831 Oct 2018

Publication series

NameProceedings of IEEE Sensors
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229


Conference17th IEEE SENSORS Conference, SENSORS 2018
CityNew Delhi


  • Artificial Neural Networks (ANN)
  • Blood Pressure (BP) Measurement
  • PPG Sensor

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