Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals

This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors’ previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an ext...

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Main Authors:	Guven, Onur, Eftekhar, Amir, Kindt, Wilko, Constandinou, Timothy G.
Format:	Online
Language:	English
Published:	The Institution of Engineering and Technology 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4916476/

id	pubmed-4916476
recordtype	oai_dc
spelling	pubmed-49164762016-07-05 Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals Guven, Onur Eftekhar, Amir Kindt, Wilko Constandinou, Timothy G. Article This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors’ previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 μV (mean), 0.63 μV (median) and 0.6 μV (standard deviation) per heartbeat on a 1 mVp–p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 μV (mean), 8.5 μV (median) and 9.0 μV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 μV, 11.6 μV (mean), 7.8 μV, 8.9 μV (median) and 9.8 μV, 9.3 μV (standard deviation) per heartbeat. The Institution of Engineering and Technology 2016-05-11 /pmc/articles/PMC4916476/ /pubmed/27382478 http://dx.doi.org/10.1049/htl.2015.0031 Text en http://creativecommons.org/licenses/by/3.0/ This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Guven, Onur Eftekhar, Amir Kindt, Wilko Constandinou, Timothy G.
spellingShingle	Guven, Onur Eftekhar, Amir Kindt, Wilko Constandinou, Timothy G. Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals
author_facet	Guven, Onur Eftekhar, Amir Kindt, Wilko Constandinou, Timothy G.
author_sort	Guven, Onur
title	Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals
title_short	Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals
title_full	Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals
title_fullStr	Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals
title_full_unstemmed	Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals
title_sort	computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals
description	This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors’ previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 μV (mean), 0.63 μV (median) and 0.6 μV (standard deviation) per heartbeat on a 1 mVp–p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 μV (mean), 8.5 μV (median) and 9.0 μV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 μV, 11.6 μV (mean), 7.8 μV, 8.9 μV (median) and 9.8 μV, 9.3 μV (standard deviation) per heartbeat.
publisher	The Institution of Engineering and Technology
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4916476/
_version_	1613598005317861376

Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals

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