Premium
Effects of deep controlled breathing on heart rate variability in young adults
Author(s) -
Grigorieva Dina,
Dimitriev Dmitry,
Saperova Elena
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.724.2
Subject(s) - sample entropy , heart rate variability , detrended fluctuation analysis , poincaré plot , heart rate , respiratory rate , cardiorespiratory fitness , approximate entropy , biofeedback , breathing , cardiology , medicine , anesthesia , mathematics , audiology , statistics , blood pressure , physical medicine and rehabilitation , time series , geometry , scaling
Integrated physiological systems, such as the cardiac and the respiratory system, exhibit complex changes that are further influenced by intrinsic feedback mechanisms controlling their interaction. But the nature and mechanism underlying this effect is not well understood yet. The purpose of the present study was to investigate whether and how heart rate variability (HRV) responds to changes in cardiorespiratory synchronization. Material and methods The sample consisted of 87 healthy students with a mean age of 20.17±0.19 years. The students were examining in the following sequence: recording of HRV during spontaneous (control) breathing at rest (5 minutes) and during paced breathing at five frequencies (6.5 breaths/min, 6 breaths/min, 5.5 breaths/min, 5 breaths/min, 4.5 breaths/min). All studies were performed in a quiet room at approximately the same time of day. Poincaré plot indexes as well as traditional time and frequency, the sample entropy (SampEn) measure and detrended fluctuation analysis (alpha1, alpha1) were used for analyzing variability and complexity of HRV respectively. Statistical analysis was performed using Sign test (Z). Results and discussion The resonance frequency was determined manually on the basis of power spectral analysis of HRV. Most subjects showed a local peak at frequencies between 0.083 Hz and 0.1 Hz. The resonant characteristic of HRV is usually generated using biofeedback and the external pacing of breathing, which is typically around 6 breaths/min (0.1 Hz), although the exact frequency varies between individuals. Power spectral analysis of HRV showed that the HF component of HRV does not differ in control compared to resonance breathing. LF component of HRV rises from 1736.91±559.41 during spontaneous breathing to 8391.41±777.48 during resonance breathing (Z=8.791; p<0.1). The LF/HF ratio during paced breathing was significantly higher than that during control breathing (1.51±0.21 vs 8.02±0.66; Z=7.93; p<0.1). VLF parameter, was significantly higher during the rest (1506.51±374.85) than during resonance breathing (866.67±111.49; Z=2.57; p=0.01). SDNN was lower during control breathing (62.41±3.01), than that during resonance (100.20±4.21; Z=7.51; p<0.1). SD1 and SD2 during spontaneous breathing were significantly smaller (respectively, 39.49±2.80 and 78.11±3.43) than that during resonance breathing (respectively, 51.19±2.94 and 131.60±5.33; p<0.00001). SampEn (the index of system complexity) was significantly lower during resonance breathing than that control breathing (1.195±0.022 vs 1.646±0.030; p<0.1). DFA alpha1 was greater during paced breathing (1.506±0.020), than that during control breathing (1.021±0.020; Z=7.72; p<0.1). DFA alpha2 was lower during resonance breathing (0.396±0.019), than that during spontaneous breathing (0.841±0.019; Z=8.36; p<0.1). Conclusions The main findings of this study suggest that resonance breathing alter cardiovascular autonomic regulation compared with spontaneous breathing. Nonlinear HRV analysis could be effective in automatically detecting functional status during paced breathing.