Application of the Lomb-Scargle Periodogram to InvestigateHeart Rate Variability during Haemodialysis

Abstract

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.

Reliability of five-minute vs. one-hour heart rate variability metrics in individuals with spinal cord injury

“…In the frequency-domain analysis, power in the high frequency (HF, 0.15–0.4 Hz), low frequency (LF, 0.04–0.15 Hz), very low frequency (VLF, 0.0033–0.04 Hz), and ultra-low frequency (ULF, <0.0033 Hz) bands, together with total power (TP) was calculated. The Lomb-Scargle least squares spectral analysis method was used because it is more appropriate for spectral analysis when the data are irregularly sampled such as in RR time series ( Fonseca et al, 2013 ; Krafty et al, 2014 ; Stewart et al, 2020 ).…”

Test-retest reliability of short- and long-term heart rate variability in individuals with spinal cord injury

“…The Lomb-Scargle least squares spectral analysis method for spectral density estimation was used, as it is specifically designed and optimised for non-uniformly spaced data sets such as RR time series. A recent review provides a systematic analysis of the applicability of Lomb-Scargle to in the clinical HRV analysis setting [ 24 ].…”

An open-access simultaneous electrocardiogram and phonocardiogram database

“…The Lomb-Scargle normalized periodogram is a standard method for measuring the power spectrum of HRV time-series, such as R-R intervals (Estévez et al 2016). It is particularly useful for analyzing HRV in cases where the data is irregularly sampled, noisy, or non-stationary (Stewart et al 2020). Figures 8(a) and (b) present the spectral analysis of R-R, S1-S1, and S2-S2 intervals from the sample records from figure 6, under various workload scenarios.…”