Application of the Lomb-Scargle periodogram to investigate heart rate variability during haemodialysis

Stewart, Jill ORCID logo ORCID: , Stewart, Paul ORCID logo ORCID: , Walker, Tom, Gullapudi, Latha, Eldehni, Mohamed T., Selby, Nicholas M., Taal, Maarten W. and Yang, Albert C. (2020) Application of the Lomb-Scargle periodogram to investigate heart rate variability during haemodialysis. Journal of Healthcare Engineering, 2020 . p. 8862074.

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Short-term cardiovascular compensatory responses to perturbations in the circulatory system caused by haemodialysis can be investigated by the spectral analysis of heart rate variability, thus providing an important variable for categorising individual patients’ response, leading to a more personalised treatment. This is typically accomplished by resampling the irregular heart rate to generate an equidistant time series prior to spectral analysis, but resampling can further distort the data series whose interpretation can already be compromised by the presence of artefacts. The Lomb–Scargle periodogram provides a more direct method of spectral analysis as this method is specifically designed for large, irregularly sampled, and noisy datasets such as those obtained in clinical settings. However, guidelines for preprocessing patient data have been established in combination with equidistant time-series methods and their validity when used in combination with the Lomb–Scargle approach is missing from literature. This paper examines the effect of common preprocessing methods on the Lomb–Scargle power spectral density estimate using both real and synthetic heart rate data and will show that many common techniques for identifying and editing suspect data points, particularly interpolation and replacement, will distort the resulting power spectrum potentially misleading clinical interpretations of the results. Other methods are proposed and evaluated for use with the Lomb–Scargle approach leading to the main finding that suspicious data points should be excluded rather than edited, and where required, denoising of the heart rate signal can be reliably accomplished by empirical mode decomposition. Some additional methods were found to be particularly helpful when used in conjunction with the Lomb–Scargle periodogram, such as the use of a false alarm probability metric to establish whether spectral estimates are valid and help automate the assessment of valid heart rate records, potentially leading to greater use of this powerful technique in a clinical setting.

Item Type: Article
Journal / Publication Title: Journal of Healthcare Engineering
Publisher: Hindawi
ISSN: 2040-2309
Departments: Institute of Engineering, Computing and Advanced Manufacture
Additional Information: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Depositing User: Paul Stewart
Date Deposited: 04 May 2023 11:43
Last Modified: 13 Jan 2024 11:30


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