A Linear Transformation Approach for Estimating Pulse Arrival Time

We propose a new mathematical framework for estimating pulse arrival time (PAT). Existing methods of estimating PAT rely on local characteristic points or global parametric models: local characteristic point methods detect points such as foot points, max points, or max slope points, while global par...

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Main Authors: Dohyun Kim, Jong-Hoon Ahn, Jongshill Lee, Hoon Ki Park, In Young Kim
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2012/643653
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spelling doaj-b4329e5981664c4abf27370ad25004fc2020-11-24T22:11:51ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422012-01-01201210.1155/2012/643653643653A Linear Transformation Approach for Estimating Pulse Arrival TimeDohyun Kim0Jong-Hoon Ahn1Jongshill Lee2Hoon Ki Park3In Young Kim4Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro Seongdong-gu, Seoul 133-791, Republic of KoreaDepartment of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro Seongdong-gu, Seoul 133-791, Republic of KoreaDepartment of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro Seongdong-gu, Seoul 133-791, Republic of KoreaFamily Medicine, College of Medicine, Hanyang University, 222 Wangsimni-ro Seongdong-gu, Seoul 133-791, Republic of KoreaDepartment of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro Seongdong-gu, Seoul 133-791, Republic of KoreaWe propose a new mathematical framework for estimating pulse arrival time (PAT). Existing methods of estimating PAT rely on local characteristic points or global parametric models: local characteristic point methods detect points such as foot points, max points, or max slope points, while global parametric methods fit a parametric form to the anacrotic phase of pulse signals. Each approach has its strengths and weaknesses; we take advantage of the favorable properties of both approaches in our method. To be more precise, we transform continuous pulse signals into scalar timing codes through three consecutive transformations, the last of which is a linear transformation. By training the linear transformation method on a subset of data, the proposed method yields results that are robust to noise. We apply this method to real photoplethysmography (PPG) signals and analyze the agreement between our results and those obtained using a conventional approach.http://dx.doi.org/10.1155/2012/643653
collection DOAJ
language English
format Article
sources DOAJ
author Dohyun Kim
Jong-Hoon Ahn
Jongshill Lee
Hoon Ki Park
In Young Kim
spellingShingle Dohyun Kim
Jong-Hoon Ahn
Jongshill Lee
Hoon Ki Park
In Young Kim
A Linear Transformation Approach for Estimating Pulse Arrival Time
Journal of Applied Mathematics
author_facet Dohyun Kim
Jong-Hoon Ahn
Jongshill Lee
Hoon Ki Park
In Young Kim
author_sort Dohyun Kim
title A Linear Transformation Approach for Estimating Pulse Arrival Time
title_short A Linear Transformation Approach for Estimating Pulse Arrival Time
title_full A Linear Transformation Approach for Estimating Pulse Arrival Time
title_fullStr A Linear Transformation Approach for Estimating Pulse Arrival Time
title_full_unstemmed A Linear Transformation Approach for Estimating Pulse Arrival Time
title_sort linear transformation approach for estimating pulse arrival time
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2012-01-01
description We propose a new mathematical framework for estimating pulse arrival time (PAT). Existing methods of estimating PAT rely on local characteristic points or global parametric models: local characteristic point methods detect points such as foot points, max points, or max slope points, while global parametric methods fit a parametric form to the anacrotic phase of pulse signals. Each approach has its strengths and weaknesses; we take advantage of the favorable properties of both approaches in our method. To be more precise, we transform continuous pulse signals into scalar timing codes through three consecutive transformations, the last of which is a linear transformation. By training the linear transformation method on a subset of data, the proposed method yields results that are robust to noise. We apply this method to real photoplethysmography (PPG) signals and analyze the agreement between our results and those obtained using a conventional approach.
url http://dx.doi.org/10.1155/2012/643653
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