Oscillatory patterns in heart rate variability and complexity: A meta-analysis. (March 2017)

• Record Type: Journal Article
• Authors: Natali, José Eduardo Soubhia; Starzynski, Paulo Nogueira; Chaui-Berlinck, José Guilherme
• Is Part Of: Biomedical signal processing and control. Volume 33(2017)
• Page Start: 66
• Page End: 71
• Publication Date: 2017-03
• Subjects: HRV heart rate variability -- SNS sympathetic nervous system -- PNS parasympathetic nervous system -- HRC heart rate complexity -- ANS autonomic nervous system -- HR heart rate -- ECG electrocardiogram -- LF low frequencies components of HRV -- HF high frequencies components of HRV -- a1ApEn area of the ApEn for all tolerances and window size 1 -- V˙O2max maximal rate of oxygen consumption -- FFT fast fourier transform -- nuHF normalized HF, calculated by nuHF = HF/(LF + HF) -- Ratio the ratio between LF and HF -- VLF very low frequencies components of HRV; Heart rate variability -- Heart rate complexity -- Oscillatory pattern -- Physiological time-series analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.11.012
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Beat-to-beat variation may be analyzed through linear and non linear methods. Information obtained is often limited to changes in the autonomic nervous system. A meta -analysis to detect oscillatory patterns is proposed. A sine wave model explains the results in both methods, but better in non linear. It is suggested that the oscillatory pattern is associated with thermoregulation. Abstract: The study of instantaneous heart rate changes is a non invasive form to obtain indirect information about heart rate control. This beat-to-beat variation is denominated heart rate variability (HRV) and when estimated through frequency domain methods provides information about the sympathetic (SNS) or parasympathetic (PNS) nervous system. Beat-to-beat variation can also be estimated by nonlinear methods, then termed heart rate complexity (HRC). Even though HRC does not possess a straightforward relationship with the SNS or PNS, these estimators are also utilized to infer changes in the autonomic nervous system (ANS). In many situations, a low value of both indexes (HRV/HRC) is associated with several cardiovascular diseases. On the other hand, there are scenarios (such as, exercise and temperature challenges) in which those indexes appear to be less informative, mainly because the association between HRV/HRC and the ANS ceases to hold tight. Therefore, it is interesting to extract additional information from HRV/HRC analyses that could lead to a broader understanding of cardiac control. Previous experiments in our laboratory suggested the existence of an oscillatory component in HRV/HRC results along the time of experiment. The present study tested the existence of this pattern in HRV/HRC of 13 subjects running at constant speed. For this purpose, sine wave, linear and quadratic models were fitted to the results of these estimators. The sine wave model significantly, and more adequately than the other models, fitted the results obtained. Furthermore, the correlation obtained was significantly higher for the HRC data. This meta -analysis is a novel technique not found in the literature survey, moreover, it reveals a new way to approach cardiac control.