Cuffless blood pressure estimation by error-correcting output coding method based on an aggregation of AdaBoost with a photoplethysmograph sensor
Abstract
This paper presented a novel cuffless and non-invasive technique of Blood Pressure (BP) estimation with a pattern recognition method by using a Photoplethysmograph (PPG) sensor instead of a cuff. Error-Correcting Output Coding (ECOC) method was adopted as a multi-classifier machine based on an aggregation of general binary classifiers. AdaBoost was applied as binary classifier machine. 368 volunteers participated in the experiment. The estimated Systolic Blood Pressure (SBP) was calculated from their individual information and several features of their Pulse Wave (PW). As a result of the comparison between measured SBP, estimated SBP, MD = -1.2 [mmHg] and SD = 11.7 [mmHg] were obtained. Hence, this technique would be helpful to the advance the development of continuous BP monitoring system, since the only one device to monitor BP is smaller than traditional measurements.
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Artificial intelligence and machine learning have been used to determine blood pressure from PPG and mitigate the effects age have on the estimate.
“Artificial intelligence and machine learning have been used to determine blood pressure from PPG and mitigate the effects age have on the estimate.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [96]
Suzuki and Oguri used artificial intelligence to determine blood pressure from a cuffless monitor and incorporated age into their algorithm.
“Suzuki and Oguri similarly used artificial intelligence to determine blood pressure from a cuffless monitor and incorporated age into their algorithm [96].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [96]
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References 10 · Cited by 31
- Authors
- Suzuki, S., & Oguri, K.
- Affiliations
- Aichi Prefectural University (JP)
- Venue
- 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Publisher
- IEEE
- Published
- 2009-09
- Volume
- 2009
- Pages
- 6765-6768
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2108213815
- Cited by
- 31
- References
- 10
- Citations by year
- 2026: 1 · 2025: 3 · 2023: 3 · 2022: 7 · 2021: 5 · 2020: 2 · 2017: 1 · 2016: 2 · 2014: 1 · 2013: 3
- FWCI
- 2.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- cuffless blood pressure measurement, non-invasive blood pressure monitoring, photoplethysmography, error-correcting output coding, AdaBoost, classifier aggregation, systolic blood pressure, continuous blood pressure monitoring
- MeSH terms
- Adult, Aged, Algorithms, Blood Pressure, Blood Pressure Determination, Female, Humans, Male, Middle Aged, Monitoring, Physiologic, Pulse, Systole, Photoplethysmography, Age Distribution, Young Adult
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