RESUMEN
Frequency domain analysis of heart rate variability (HRV) has been proposed as a semiquantitative method for assessing activities in the autonomic nervous system. We examined whether absolute powers, normalized powers, and the low frequency-to-high frequency ratio (LF/HF) derived from the HRV power spectrum could detect shifts in autonomic balance in a setting with low sympathetic nervous tone. Healthy subjects were examined for 3 h in the supine position during 1) control conditions (n = 12), 2) acute beta-blockade (n = 11), and 3) chronic beta-blockade (n = 10). Heart rate fell during the first 40 min of the control session (72 +/- 2 to 64 +/- 2 beats/min; P < 0. 005) and was even lower during acute and chronic beta-blockade (56 +/- 2 beats/min; P < 0.005). The powers of all spectral areas rose during the first 60 min in all three settings, more so with beta-blockade (P < 0.05). LF/HF was found to contain the same information as powers expressed in normalized units. LF/HF detected the shift in autonomic balance induced by beta-blockade but not the change induced by supine position. In conclusion, none of the investigated measures derived from power spectral analysis comprehensively and consistently described the changes in autonomic balance.
Asunto(s)
Antagonistas Adrenérgicos beta/farmacología , Sistema Nervioso Autónomo/fisiología , Electrocardiografía Ambulatoria , Corazón/fisiología , Metoprolol/farmacología , Antagonistas Adrenérgicos beta/administración & dosificación , Adulto , Sistema Nervioso Autónomo/efectos de los fármacos , Esquema de Medicación , Electrocardiografía Ambulatoria/efectos de los fármacos , Femenino , Humanos , Masculino , Metoprolol/administración & dosificación , Modelos Cardiovasculares , Posición Supina , Factores de TiempoRESUMEN
The contribution of nonlinear dynamics to heart rate variability in healthy humans was examined using surrogate data analysis. Several measures of heart rate variability were used and compared. Heart rates were recorded for three hours and original data sets of 8192 R-R intervals created. For each original data set (n = 34), three surrogate data sets were made by shuffling the order of the R-R intervals while retaining their linear correlations. The difference in heart rate variability between the original and surrogate data sets reflects the amount of nonlinear structure in the original data set. Heart rate variability was analyzed by two different nonlinear methods, point correlation dimension and approximate entropy. Nonlinearity, though under 10 percent, could be detected with both types of heart rate variability measures. More importantly, not only were the correlations between these measures and the standard deviation of the R-R intervals weak, the correlation among the nonlinear measures themselves was also weak (generally less than 0.6). This suggests that in addition to standard linear measures of heart rate variability, the use of multiple nonlinear measures of heart rate variability might be useful in monitoring heart rate dynamics.
Asunto(s)
Frecuencia Cardíaca/fisiología , Dinámicas no Lineales , Adulto , Algoritmos , Electrocardiografía , Entropía , HumanosRESUMEN
Although it is doubtful whether the normal sinus rhythm can be described as low-dimensional chaos, there is evidence for inherent nonlinear dynamics and determinism in time series of consecutive R-R intervals. However, the physiological origin for these nonlinearities is unknown. The aim of this study was to test whether the known nonlinear input from spontaneous respiration is a source for the nonlinearities in heart rate variability. Twelve healthy subjects were examined in supine position with 3-h electrocardiogram recordings during both spontaneous and forced respiration in accordance with a metronome set to 12 min(-1). Nonlinear dynamics were measured as the correlation dimension and the nonlinear prediction error. Complexity expressed as correlation dimension was unchanged from normal respiration, 9.1 +/- 0.5, compared with forced respiration, 9.3 +/- 0.6. Also, nonlinear determinism expressed as the nonlinear prediction error did not differ between spontaneous respiration, 32.3 +/- 3.4 ms, and forced respiration, 31.9 +/- 5.7. It is concluded that the origin of the nonlinear dynamics in heart rate variability is not a nonlinear input from the respiration into the cardiovascular oscillator. Additional studies are needed to elucidate the mechanisms behind the nonlinear dynamics in heart rate variability.
Asunto(s)
Frecuencia Cardíaca , Respiración , Adolescente , Adulto , Femenino , Humanos , Masculino , Persona de Mediana Edad , Modelos Biológicos , Dinámicas no Lineales , Mecánica Respiratoria , Posición Supina , Factores de TiempoRESUMEN
OBJECTIVES: The purpose of the study was to investigate the short- and long-term variations in the non-linear dynamics of heart rate variability, and to determine the relationships between conventional time and frequency domain methods and the newer non-linear methods of characterizing heart rate variability. METHODS: Twelve healthy subjects were investigated by 3-h ambulatory ECG recordings repeated on 3 separate days. Correlation dimension, non-linear predictability, mean heart rate, and heart rate variability in the time and frequency domains were measured and compared with the results from corresponding surrogate time series. RESULTS: A small significant amount of non-linear dynamics exists in heart rate variability. Correlation dimensions and non-linear predictability are relatively specific parameters for each individual examined. The correlation dimension is inversely correlated to the heart rate and describes mainly linear correlations. Non-linear predictability is correlated with heart rate variability measured as the standard deviation of the R-R intervals and the respiratory activity expressed as power of the high-frequency band. The dynamics of heart rate variability changes suddenly even during resting, supine conditions. The abrupt changes are highly reproducible within the individual subjects. CONCLUSIONS: The study confirms that the correlation dimension of the R-R intervals is mostly due to linear correlations in the R-R intervals. A small but significant part is due to non-linear correlations between the R-R intervals. The different measures of heart rate variability (correlation dimension, average prediction error, and the standard deviation of the R-R intervals) characterize different properties of the signal, and are therefore not redundant measures. Heart rate variability cannot be described as a single chaotic system. Instead heart rate variability consists of intertwined periods with different non-linear dynamics. It is hypothesized that the heart rate is governed by a system with multiple "strange" attractors.