title: Novel application of multi dynamic trend analysis as a sensitive tool for detecting the effects of aging and congestive heart failure on heart rate variability.
abstract: The complex fluctuations in heart rate variability (HRV) reflect cardiac autonomic modulation and are an indicator of congestive heart failure (CHF). This paper proposes a novel nonlinear approach to HRV investigation, the multi dynamic trend analysis (MDTA) method, based on the empirical mode decomposition algorithm of the Hilbert-Huang transform combined with a variable-sized sliding-window method. Electrocardiographic signal data obtained from the PhysioNet database were used. These data were from subjects with CHF (mean age = 59.4 ± 8.4), an age-matched elderly healthy control group (59.3 ± 10.6), and a healthy young group (30.3 ± 4.8); the HRVs of these subjects were processed using the MDTA method, time domain analysis, and frequency domain analysis. Among all HRV parameters, the MDTA absolute value slope (MDTS) and MDTA deviation (MDTD) exhibited the greatest area under the curve (AUC) of the receiver operating characteristics in distinguishing between the CHF group and the healthy controls (AUC = 1.000) and between the healthy elderly subject group and the young subject group (AUC = 0.834 ± 0.067 for MDTS; 0.837 ± 0.066 for MDTD). The CHF subjects presented with lower MDTA indices than those of the healthy elderly subject group. Furthermore, the healthy elderly subjects exhibited lower MDTA indices than those of the young controls. The MDTA method can adaptively and automatically identify the intrinsic fluctuation on variable temporal and spatial scales when investigating complex fluctuations in the cardiac autonomic regulation effects of aging and CHF.
authors.affiliation: Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Yu-Cheng
authors.initials: YC
authors.lastname: Lin
authors.affiliation: Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Yu-Hsuan
authors.initials: YH
authors.lastname: Lin
authors.affiliation: Center for Dynamical Biomarkers and Translational Medicine, National Central University, Jhongli, Taiwan.
authors.forename: Men-Tzung
authors.initials: MT
authors.lastname: Lo
authors.affiliation: Center for Dynamical Biomarkers and Translational Medicine, National Central University, Jhongli, Taiwan.
authors.forename: Chung-Kang
authors.initials: CK
authors.lastname: Peng
authors.affiliation: Research Center for Adaptive Data Analysis, National Central University, Taoyuan, Taiwan.
authors.forename: Norden E
authors.initials: NE
authors.lastname: Huang
authors.affiliation: Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Cheryl C H
authors.initials: CC
authors.lastname: Yang
authors.affiliation: Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Terry B J
authors.initials: TB
authors.lastname: Kuo
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000375:Aging / Q000502:physiology*; D019540:Area Under Curve; D005260:Female; D006333:Heart Failure / Q000503:physiopathology*; D006339:Heart Rate / Q000502:physiology*; D000070276:Heart Rate Determination; D006801:Humans; D008297:Male; D008875:Middle Aged; D013997:Time Factors