Towards photoplethysmogram based non-invasive blood pressure classification

Rajdeep Kumar Nath, Himanshu Thapliyal, Allison Caban-Holt

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

6 Scopus citations

Abstract

A novel blood pressure classification model using Phototplethysmogram (PPG) is proposed in this work. The proposed model uses signal processing and machine learning algorithms to classify blood pressure in four stages: normal, elevated, stage 1 and stage 2. A total of 83 features were extracted from the PPG signal which includes 71 statistical features and 12 characteristic features. We have used random forest classifier to train and test our predictive model. The proposed method is evaluated on publicly available MIMIC database for 20 different individuals. The database contains raw PPG data for different users and Arterial Blood Pressure (ABP) to calculate the systolic and diastolic blood pressure to be used as the ground truth for training and validation purposes. We have achieved an overall accuracy of 90.8% over the four classes of blood pressure levels. The results indicate that the proposed model will be ideal for integration into a non-invasive blood pressure monitoring system with significant accuracy.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018
Pages37-39
Number of pages3
ISBN (Electronic)9781538691724
DOIs
StatePublished - Jul 2 2018
Event4th IEEE International Symposium on Smart Electronic Systems, iSES 2018 - Hyderabad, India
Duration: Dec 17 2018Dec 19 2018

Publication series

NameProceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018

Conference

Conference4th IEEE International Symposium on Smart Electronic Systems, iSES 2018
Country/TerritoryIndia
CityHyderabad
Period12/17/1812/19/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Arterial Blood Pressure
  • Blood Pressure (BP)
  • Diastolic Blood Pressure (DBP)
  • Frequency Spectrum
  • Photoplethysmogram (PPG)
  • Systolic Blood Pressure (SBP)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Instrumentation

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