Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures

• Published in: Annals of biomedical engineering Vol. 38; no. 3; pp. 854 - 864
• Main Authors: Hu, Jing, Gao, Jianbo, Tung, Wen-wen, Cao, Yinhe
• Format: Journal Article
• Language: English
• Published: Boston Boston : Springer US 01.03.2010; Springer US; Springer Nature B.V
• Subjects: Algorithms; Atrial Fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - physiopathology; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Cardiovascular system; Classical Mechanics; diagnosis; Diagnosis, Computer-Assisted; Diagnosis, Computer-Assisted - methods; Electrocardiography; Electrocardiography - methods; Entropy; Female; Heart Rate; Heart rate variability; Humans; Male; methods; Multiscale analysis; Pattern Recognition, Automated; Pattern Recognition, Automated - methods; physiopathology; Reproducibility of Results; Scale-dependent Lyapunov exponent; Sensitivity and Specificity; Cardiovascular system; Scale-dependent Lyapunov exponent; Heart rate variability; Multiscale analysis
• ISSN: 0090-6964; 1573-9686; 1573-9686
• DOI: 10.1007/s10439-009-9863-2

Heart rate variability (HRV) is an important dynamical variable of the cardiovascular function. There have been numerous efforts to determine whether HRV dynamics are chaotic or random, and whether certain complexity measures are capable of distinguishing healthy subjects from patients with certain cardiac disease. In this study, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure (CHF), and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups. Furthermore, we show that for the purpose of distinguishing healthy subjects from patients with CHF, features derived from SDLE are more effective than other complexity measures such as the Hurst parameter, the sample entropy, and the multiscale entropy.