Boosting Algorithms based Cuff-less Blood Pressure Estimation from Clinically Relevant ECG and PPG Morphological Features

Aayushman Ghosh, Sayan Sarkar, Haipeng Liu, Subhamoy Mandal

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

1 Citation (Scopus)

Abstract

Blood Pressure (BP) is often coined as a critical physiological marker for cardiovascular health. Multiple studies have explored either Photoplethysmogram (PPG) or ECG-PPG derived features for continuous BP estimation using machine learning (ML); deep learning (DL) techniques. Majority of those derived features often lack a stringent biological explanation and are not significantly correlated with BP. In this paper, we identified several clinically relevant (bio-inspired) ECG and PPG features; and exploited them to estimate Systolic (SBP), and Diastolic Blood Pressure (DBP) values using CatBoost, and AdaBoost algorithms. The estimation performance was then compared against popular ML algorithms. SBP and DBP achieved a Pearson’s correlation coefficient of 0.90 and 0.83 between estimated and target BP values. The estimated mean absolute error (MAE) values are 3.81 and 2.22 mmHg with a Standard Deviation of 6.24 and 3.51 mmHg, respectively, for SBP and DBP using CatBoost. The results surpassed the Advancement of Medical Instrumentation (AAMI) standards. For the British Hypertension Society (BHS) protocol, the results achieved for all the BP categories resided in Grade A. Further investigation reveals that bio-inspired features along with tuned ML models can produce comparable results w.r.t parameter-intensive DL networks. ln(HR × mNPV), HR, BMI index, ageing index, and PPG-K point were identified as the top five key features for estimating BP. The group-based analysis further concludes that a trade-off lies between the number of features and MAE. Increasing the no. of features beyond a certain threshold saturates the reduction in MAE.
Original languageEnglish
Title of host publication2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9798350324471
ISBN (Print)9798350324488
DOIs
Publication statusE-pub ahead of print - 11 Dec 2023
Event45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society - Sydney, Australia
Duration: 24 Jul 202327 Jul 2023
https://embc.embs.org/2023/

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society
Abbreviated titleEMBC
Country/TerritoryAustralia
CitySydney
Period24/07/2327/07/23
Internet address

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