An Application of The Lomb-Scargle Periodogram To Investigate Heart Rate
Variability During Haemodialysis
Abstract
Objective: Short-term cardiovascular compensatory responses to
perturbations in the circulatory system caused by haemodialysis can be
investigated by spectral analysis of heart rate variability. This could
provide an important variable for categorising individual patients
response to haemodialysis leading to a more personalised treatment.
However, data obtained over a four-hour haemodialysis treatment is
significant in volume and subject to artefacts that can compromise its
analysis.
Methods: The Lomb-Scargle Periodogram can provide a robust method of
generating power spectral density estimates for large, irregularly
sampled and noisy data sets obtained in clinical settings, provided that
careful attention is given to frequency limits. The effect of different
pre-processing methods on the resulting power spectrum is explored with
simulated and real heart rate variability data.
Results: Common pre-processing methods for correcting individual
artefacts in heart rate records, such as interpolation, are unreliable
as they act as non-linear low-pass filters and distort the resulting
spectral analysis. These distortions are present, but less apparent
within patient data and can mislead clinical interpretations.
Conclusion: It is more appropriate to exclude suspect data points than
to edit them prior to spectral analysis via the Lomb- Scargle
periodogram, and where required, de-noise the entire heart rate signal
by empirical mode decomposition. The use of a False Alarm Probability
metric can help establish whether spectral estimates are valid
Significance: Methods established to pre-process time-invariant data
prior to power spectral density estimation fail when used in conjunction
with the Lomb-Scargle method.