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Evaluation of techniques for estimating the power spectral density of RR-intervals under paced respiration conditions

 
: Schaffer, T.; Hensel, B.; Weigand, C.; Schüttler, J.; Jeleazcov, C.

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Journal of clinical monitoring and computing 28 (2014), No.5, pp.481-486
ISSN: 1387-1307
ISSN: 0748-1977
ISSN: 1573-2614
English
Journal Article
Fraunhofer IIS ()

Abstract
Heart rate variability (HRV) analysis is increasingly used in anaesthesia and intensive care monitoring of spontaneous breathing and mechanical ventilated patients. In the frequency domain, different estimation methods of the power spectral density (PSD) of RR-intervals lead to different results. Therefore, we investigated the PSD estimates of fast Fourier transform (FFT), autoregressive modeling (AR) and Lomb-Scargle periodogram (LSP) for 25 young healthy subjects subjected to metronomic breathing. The optimum method for determination of HRV spectral parameters under paced respiration was identified by evaluating the relative error (RE) and the root mean square relative error (RMSRE) for each breathing frequency (BF) and spectral estimation method. Additionally, the sympathovagal balance was investigated by calculating the low frequency/high frequency (LF/HF) ratio. Above 7 breaths per minute, all methods showed a significant increase in LF/HF ratio with increasing BF. On average, the RMSRE of FFT was lower than for LSP and AR. Therefore, under paced respiration conditions, estimating RR-interval PSD using FFT is recommend.

: http://publica.fraunhofer.de/documents/N-264715.html