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A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-4841-2488
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6640-1868
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-8704-402X
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
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2023 (English)In: Electronics, E-ISSN 2079-9292, Vol. 12, no 5, article id 1174Article in journal (Refereed) Published
Abstract [en]

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

Place, publisher, year, edition, pages
2023. Vol. 12, no 5, article id 1174
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Medical Engineering
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URN: urn:nbn:se:mdh:diva-62004DOI: 10.3390/electronics12051174ISI: 000947098400001Scopus ID: 2-s2.0-85149747017OAI: oai:DiVA.org:mdh-62004DiVA, id: diva2:1741084
Available from: 2023-03-03 Created: 2023-03-03 Last updated: 2023-04-12Bibliographically approved

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Abdullah, SaadFolke, MiaLindén, MariaKristoffersson, Annica

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