Refined matrix completion for spectrum estimation of heart rate variability

Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessme...

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Main Authors: Lei Lu, Tingting Zhu, Ying Tan, Jiandong Zhou, Jenny Yang, Lei Clifton, Yuan-Ting Zhang, David A. Clifton
Format: Article
Language:English
Published: AIMS Press 2024-08-01
Series:Mathematical Biosciences and Engineering
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Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2024296
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author Lei Lu
Tingting Zhu
Ying Tan
Jiandong Zhou
Jenny Yang
Lei Clifton
Yuan-Ting Zhang
David A. Clifton
author_facet Lei Lu
Tingting Zhu
Ying Tan
Jiandong Zhou
Jenny Yang
Lei Clifton
Yuan-Ting Zhang
David A. Clifton
author_sort Lei Lu
collection DOAJ
description Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessments of cardiac activities. In this study, we introduced a novel approach for estimating uncertainties in HRV spectrum based on matrix completion. The proposed method utilises the low-rank characteristic of HRV spectrum matrix to efficiently estimate data uncertainties. In addition, we developed a refined matrix completion technique to enhance the estimation accuracy and computational cost. Benchmarking on five public datasets, our model shows effectiveness and reliability in estimating uncertainties in HRV spectrum, and has superior performance against five deep learning models. The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.
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id doaj-art-dd5276b941ca43fcbdf3025211f59c66
institution Kabale University
issn 1551-0018
language English
publishDate 2024-08-01
publisher AIMS Press
record_format Article
series Mathematical Biosciences and Engineering
spelling doaj-art-dd5276b941ca43fcbdf3025211f59c662025-01-23T07:47:47ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-08-012186758678210.3934/mbe.2024296Refined matrix completion for spectrum estimation of heart rate variabilityLei Lu0Tingting Zhu1Ying Tan2Jiandong Zhou3Jenny Yang4Lei Clifton5Yuan-Ting Zhang6David A. Clifton7School of Life Course & Population Sciences, King's College London, London WC2R 2LS, UKDepartment of Engineering Science, University of Oxford, Oxford OX1 2JD, UKDepartment of Mechanical Engineering, The University of Melbourne, Parkville 3010, AustraliaDepartment of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, ChinaDepartment of Engineering Science, University of Oxford, Oxford OX1 2JD, UKNuffield Department of Clinical Medicine, Experimental Medicine Division, University of Oxford, Oxford, UKDepartment of Electronic Engineering, Chinese University of Hong Kong, Hong Kong, ChinaDepartment of Engineering Science, University of Oxford, Oxford OX1 2JD, UKHeart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessments of cardiac activities. In this study, we introduced a novel approach for estimating uncertainties in HRV spectrum based on matrix completion. The proposed method utilises the low-rank characteristic of HRV spectrum matrix to efficiently estimate data uncertainties. In addition, we developed a refined matrix completion technique to enhance the estimation accuracy and computational cost. Benchmarking on five public datasets, our model shows effectiveness and reliability in estimating uncertainties in HRV spectrum, and has superior performance against five deep learning models. The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.https://www.aimspress.com/article/doi/10.3934/mbe.2024296heart rate variabilityspectrum estimationmatrix completionuncertaintyhrv modelling
spellingShingle Lei Lu
Tingting Zhu
Ying Tan
Jiandong Zhou
Jenny Yang
Lei Clifton
Yuan-Ting Zhang
David A. Clifton
Refined matrix completion for spectrum estimation of heart rate variability
Mathematical Biosciences and Engineering
heart rate variability
spectrum estimation
matrix completion
uncertainty
hrv modelling
title Refined matrix completion for spectrum estimation of heart rate variability
title_full Refined matrix completion for spectrum estimation of heart rate variability
title_fullStr Refined matrix completion for spectrum estimation of heart rate variability
title_full_unstemmed Refined matrix completion for spectrum estimation of heart rate variability
title_short Refined matrix completion for spectrum estimation of heart rate variability
title_sort refined matrix completion for spectrum estimation of heart rate variability
topic heart rate variability
spectrum estimation
matrix completion
uncertainty
hrv modelling
url https://www.aimspress.com/article/doi/10.3934/mbe.2024296
work_keys_str_mv AT leilu refinedmatrixcompletionforspectrumestimationofheartratevariability
AT tingtingzhu refinedmatrixcompletionforspectrumestimationofheartratevariability
AT yingtan refinedmatrixcompletionforspectrumestimationofheartratevariability
AT jiandongzhou refinedmatrixcompletionforspectrumestimationofheartratevariability
AT jennyyang refinedmatrixcompletionforspectrumestimationofheartratevariability
AT leiclifton refinedmatrixcompletionforspectrumestimationofheartratevariability
AT yuantingzhang refinedmatrixcompletionforspectrumestimationofheartratevariability
AT davidaclifton refinedmatrixcompletionforspectrumestimationofheartratevariability