Refined matrix completion for spectrum estimation of heart rate variability

Lei Lu; Tingting Zhu; Ying Tan; Jiandong Zhou; Jenny Yang; Lei Clifton; Yuan-Ting Zhang; David A. Clifton; Lei Lu; Tingting Zhu; Ying Tan; Jiandong Zhou; Jenny Yang; Lei Clifton; Yuan-Ting Zhang; David A. Clifton

doi:10.3934/mbe.2024296

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 8: 6758-6782. doi: 10.3934/mbe.2024296

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Refined matrix completion for spectrum estimation of heart rate variability

1.
School of Life Course & Population Sciences, King's College London, London WC2R 2LS, UK
2.
Department of Engineering Science, University of Oxford, Oxford OX1 2JD, UK
3.
Department of Mechanical Engineering, The University of Melbourne, Parkville 3010, Australia
4.
Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
5.
School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
6.
Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China
7.
Nuffield Department of Clinical Medicine, Experimental Medicine Division, University of Oxford, Oxford, UK
8.
Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong, China
9.
Oxford Suzhou Centre for Advanced Research, Suzhou, China

Academic Editor: Elena G. Tolkacheva

Received: 01 March 2024 Revised: 17 June 2024 Accepted: 28 June 2024 Published: 02 August 2024

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.
- heart rate variability,
- spectrum estimation,
- matrix completion,
- uncertainty,
- HRV modelling
Citation: 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[J]. Mathematical Biosciences and Engineering, 2024, 21(8): 6758-6782. doi: 10.3934/mbe.2024296

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Abstract

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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