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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| Format: | Article |
| Language: | English |
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AIMS Press
2024-08-01
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| 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. |
| format | Article |
| 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 |
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