Abstract
In this paper, we have proposed a computational framework for continuous blood pressure estimation using Photoplethysmogram (PPG) signal. The proposed framework is evaluated on the publicly available MIMIC Database. The database contains raw PPG data for different users and also the Arterial Blood Pressure (ABP) for calculating the systolic and diastolic blood pressure. Results showed that the Decision Tree Regressor boosted by Adaboost regressor could estimate systolic blood pressure with a mean average error of 2.07 and a standard deviation of 5.97 and the diastolic blood pressure with a mean average error of 1.15 and a standard deviation of 4.05. The results indicate that the proposed framework is an ideal candidate for integrating with the wearable devices for unobtrusive cuff less continuous blood pressure monitoring to provide real time update on the blood pressure of an individual with high degree of accuracy.
| Original language | English |
|---|---|
| Title of host publication | IEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings |
| ISBN (Electronic) | 9781728155036 |
| DOIs | |
| State | Published - Jun 2020 |
| Event | 6th IEEE World Forum on Internet of Things, WF-IoT 2020 - New Orleans, United States Duration: Jun 2 2020 → Jun 16 2020 |
Publication series
| Name | IEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings |
|---|
Conference
| Conference | 6th IEEE World Forum on Internet of Things, WF-IoT 2020 |
|---|---|
| Country/Territory | United States |
| City | New Orleans |
| Period | 6/2/20 → 6/16/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Photoplethysmogram (PPG)
- diastolic blood pressure
- frequency spectrum
- systolic blood pressure
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Information Systems and Management
- Statistics, Probability and Uncertainty
- Computational Mechanics
- Instrumentation