D Ramaekers

Analysis of heart rate variability with correlation dimension method in a normal population and in heart transplant patients

INTRODUCTIONnThe correlation dimension (CD) method is related to chaos theory and is used to quantify heart rate variability (HRV). The CD is a measure for the amount of correlations present in the signal.nnnMETHODSnThe algorithm used to calculate the CD is based on the method of Grassberger and Proccacia. The method was first validated on signals with known CD and then applied to HRV-signals of heart transplants and an age-matched control group of healthy subjects. The CD of the corresponding surrogate time series was calculated to investigate non-linear correlations in the HRV-signal. Circadian variations of the CD were studied in 20 healthy subjects, including men and women.nnnRESULTSnThe value of the CD for healthy subjects ranged from 2.12 to 5.53 with a mean value of 4.32. For heart transplants, only a few time series showed a finite value of the CD that varied between 2.10 and 5.60. Also, a significant difference was found between the CD of the original and the surrogate time series in healthy subjects. The CD of women is higher than the CD of men, and this difference was more pronounced during the night than during the day.nnnCONCLUSIONSnThis limited study shows that the CD alone cannot be used to make a distinction between HRV-signals of healthy subjects and of heart transplants. However, there is evidence that there are non-linear correlations present in the HRV-signal and that there are significant gender and circadian differences in the CD.

Effect of long term physical training on heart rate variability

The purpose of this study was to investigate the influence of physical training on the autonomic nervous system in healthy subjects. The authors compared 28 trained athletes and 28 sedentary subjects and divided them into two age groups: 18 to 34 and 35 to 55 years of age. ECG was recorded during 2 minutes while the subjects were breathing periodically under metronome control. RR tachograms were obtained and heart rate variability (HRV) was calculated. In the younger subgroup (18-34 years of age) only the standard deviation of mean RR interval and the frequency content were significantly different. In the older subgroup (35-55 years of age), all measured parameters were significantly different. A clear peak around 0.083 Hz, in coherence with respiratory rate, was observed in athletes and was present to a much smaller amplitude in sedentary subjects. These results show that regular physical training enhances the synchronization between heart rate and respiratory frequency, with a more pronounced effect at a higher age. The fact that physical training has a distinct impact on HRV in healthy subjects, infers that exercising may be of value in the modification of cardiac autonomic activity in cardiac patients.

Nonlinear dynamics in heart rate variability

The aim of the present study was to examine the influence of data length on the values of fractal dimension (FD), Lyapunov exponent (LE) and approximate entropy (ApEn) and to apply these techniques in a healthy population. There was no significant difference between long- and short-term recordings in the methods used, except when using FD without a windowing function. There was a significant difference between day and night values of FD (p<0.005) and ApEn showed a difference between standing and supine position (p<0.01). These results indicate a possible relation with the autonomic regulation of heart rate for FD and ApEn, while the LE seems to be an inherent to the system. The results presented indicate that these methods can be used on both long and short recordings. This could be useful in the clinical situation.