The University of Southampton
University of Southampton Institutional Repository

Spectral estimation of HRV in signals with gaps

Spectral estimation of HRV in signals with gaps
Spectral estimation of HRV in signals with gaps

Heart rate variability is commonly quantified following spectral estimation. However, it is often difficult to obtain continuous recordings of beat-to-beat intervals without interruptions due to artefacts, noise or sporadic arrhythmias. Such data loss may be seen as gaps in the recordings, and often results in such signals being discarded. While a number of methods has been proposed for spectral estimation in heart rate records with gaps, there are no comprehensive comparisons between them. This paper tries to fill this void, comparing methods and identifying the most versatile and reliable one. The mean (bias error)and standard deviation (random error)of estimates of power in the low frequency band (LF), from 0.04 to 0.15 Hz; in the high frequency band (HF), from 0.15 to 0.4 Hz; and their ratio (LF/HF), were calculated in RR-interval time-series with up to 50% of samples missing through large or small gaps introduced into recordings. ‘Correlogram (bridging)’ and ‘Burg for segments’ methods proved to be the most robust methods for dealing with gaps, but Burg for segments was found to be more robust, especially in the HF band. Our results clearly show that even large gaps (covering a total of 50% of the recording time)can still yield robust spectral estimates of HRV, provided appropriate methods are used.

Biomedical signal processing, Data loss, Electrocardiography, Heart rate variability, Spectral analysis
1746-8094
187-197
Rodríguez-Liñares, L.
6af7bd04-d30d-4c0f-bd6c-1e9057c3fcad
Simpson, D.M.
53674880-f381-4cc9-8505-6a97eeac3c2a
Rodríguez-Liñares, L.
6af7bd04-d30d-4c0f-bd6c-1e9057c3fcad
Simpson, D.M.
53674880-f381-4cc9-8505-6a97eeac3c2a

Rodríguez-Liñares, L. and Simpson, D.M. (2019) Spectral estimation of HRV in signals with gaps. Biomedical Signal Processing and Control, 52, 187-197. (doi:10.1016/j.bspc.2019.04.006).

Record type: Article

Abstract

Heart rate variability is commonly quantified following spectral estimation. However, it is often difficult to obtain continuous recordings of beat-to-beat intervals without interruptions due to artefacts, noise or sporadic arrhythmias. Such data loss may be seen as gaps in the recordings, and often results in such signals being discarded. While a number of methods has been proposed for spectral estimation in heart rate records with gaps, there are no comprehensive comparisons between them. This paper tries to fill this void, comparing methods and identifying the most versatile and reliable one. The mean (bias error)and standard deviation (random error)of estimates of power in the low frequency band (LF), from 0.04 to 0.15 Hz; in the high frequency band (HF), from 0.15 to 0.4 Hz; and their ratio (LF/HF), were calculated in RR-interval time-series with up to 50% of samples missing through large or small gaps introduced into recordings. ‘Correlogram (bridging)’ and ‘Burg for segments’ methods proved to be the most robust methods for dealing with gaps, but Burg for segments was found to be more robust, especially in the HF band. Our results clearly show that even large gaps (covering a total of 50% of the recording time)can still yield robust spectral estimates of HRV, provided appropriate methods are used.

Text
201903MainSubmitted - Accepted Manuscript
Restricted to Repository staff only until 28 April 2020.
Request a copy

More information

Accepted/In Press date: 6 April 2019
e-pub ahead of print date: 28 April 2019
Published date: 1 July 2019
Keywords: Biomedical signal processing, Data loss, Electrocardiography, Heart rate variability, Spectral analysis

Identifiers

Local EPrints ID: 432547
URI: https://eprints.soton.ac.uk/id/eprint/432547
ISSN: 1746-8094
PURE UUID: c591f292-9a0b-4412-8eff-cdc0e88fd3df

Catalogue record

Date deposited: 17 Jul 2019 16:35
Last modified: 19 Jul 2019 16:34

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×