Abstract :
This study analyzes seasonal features of pulse rate chaos in both healthy and unhealthy subjects. Analytical methods, such as numerical titration, sample entropy and spectral analysis, were used to detect and estimate the seasonal fluctuations in chaotic attributes, complexity and nonlinearity of pulse rate. Chaotic feature analyses are based on pulse rate data collected over one year from a healthy male and a male patient after coronary stenting. The results show that the mean level of pulse rate chaos in the healthy subject is lowest in winter (values of NL, DR, and SampEn are 8.1 ± 0.3%, 41.0 ± 1.2%, and 1.98 ± 0.02, separately) and highest in summer (corresponding values are 9.9 ± 0.6%, 46.8 ± 2.3%, and 2.06 ± 0.03, separately) (P <; 0.05), whereas the postoperative individual has a relatively lower mean chaotic dynamics that is least active in autumn (7.1 ± 0.5%, 14.8 ± 1.5%, and 0.80 ± 0.01) and more active in winter (7.7 ± 0.4%, 35.9 ± 1.9%, and 0.93 ± 0.01) and spring (9.1 ± 0.7%, 28.6 ± 2.1%, and 0.87 ± 0.01) (P <; 0.05). The study reveals distinct seasonal autonomic and cardiac activities in both good health and disease. These findings may also pave the way for developing new approaches to monitoring long-term HRV and interpreting HRV chaotic features.
Keywords :
blood vessels; cardiovascular system; chaos; entropy; medical disorders; medical signal processing; patient monitoring; spectral analysis; HRV; chaotic attributes; chaotic feature analysis; complexity; coronary stenting; heart rate variability; nonlinearity; numerical titration; pulse rate; sample entropy; seasonal fluctuations; spectral analysis; Chaos; Entropy; Fluctuations; Hafnium; Heart rate variability; Noise; Springs; Adult; Coronary Stenosis; Coronary Vessels; Heart; Heart Rate; Humans; Male; Nonlinear Dynamics; Periodicity; Reproducibility of Results; Seasons; Signal Processing, Computer-Assisted; Sleep; Stents;