Putting back respiration into respiratory sinus arrhythmia or high-frequency heart rate variability: Implications for interpretation, respiratory rhythmicity, and health

Ritz, Thomas

doi:10.1016/j.biopsycho.2023.108728

Cited by 6 publications

(1 citation statement)

References 130 publications

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“…RSA also depend on a number of control mechanisms due to interference from cardiovascular centers in the medulla oblongata, the degree of lung distension, and reflexive input from right atrial wall distension ( 38 ). Therefore, interpretations other than cardiac vagal activity must be considered, but this potential influence is rarely taken into account in linear analysis methods ( 39 ). Consequently, it was hypothesized that dogs with physiological respiratory arrhythmias might be affected by this phenomenon.…”

Section: Discussionmentioning

confidence: 99%

Exploring sleep heart rate variability: linear, nonlinear, and circadian rhythm perspectives

Hasegawa,

Sasaki,

Umemoto

et al. 2024

Front. Vet. Sci.

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BackgroundHeart rate variability (HRV) is believed to possess the potential for disease detection. However, early identification of heart disease remains challenging, as HRV analysis in dogs primarily reflects the advanced stages of the disease.Hypothesis/objectiveThe aim of this study is to compare 24-h HRV with sleep HRV to assess the potential utility of sleep HRV analysis.AnimalsThirty healthy dogs with no echocardiographic abnormalities were included in the study, comprising 23 females and 7 males ranging in age from 2 months to 8 years (mean [standard deviation], 1.4 [1.6]).MethodsThis study employed a cross-sectional study. 24-h HRV and sleep HRV were measured from 48-h Holter recordings. Both linear analysis, a traditional method of heart rate variability analysis, and nonlinear analysis, a novel approach, were conducted. Additionally, circadian rhythm parameters were assessed.ResultsIn frequency analysis of linear analysis, the parasympathetic index nHF was significantly higher during sleep compared to the mean 24-h period (mean sleep HRV [standard deviation] vs. mean 24 h [standard deviation], 95% confidence interval, p value, r-family: 0.24 [0.057] vs. 0.23 [0.045], 0.006–0.031, p = 0.005, r = 0.49). Regarding time domain analysis, the parasympathetic indices SDNN and RMSSD were also significantly higher during sleep (SDNN: 179.7 [66.9] vs. 156.6 [53.2], 14.5–31.7, p < 0.001, r = 0.71 RMSSD: 187.0 [74.0] vs. 165.4 [62.2], 13.2–30.0, p < 0.001, r = 0.70). In a geometric method of nonlinear analysis, the parasympathetic indices SD1 and SD2 showed significantly higher values during sleep (SD1: 132.4 [52.4] vs. 117.1 [44.0], 9.3–21.1, p < 0.001, r = 0.70 SD2: 215.0 [80.5] vs. 185.9 [62.0], 17.6–40.6, p < 0.001, r = 0.69). Furthermore, the circadian rhythm items of the parasympathetic indices SDNN, RMSSD, SD1, and SD2 exhibited positive peaks during sleep.ConclusionThe findings suggest that focusing on HRV during sleep can provide a more accurate representation of parasympathetic activity, as it captures the peak circadian rhythm items.

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Section: Discussionmentioning

confidence: 99%