Intrinsic Mode Analysis of Human Heartbeat Time Series |
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Authors: | Jia-Rong Yeh Wei-Zen Sun Jiann-Shing Shieh Norden E Huang |
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Institution: | (1) Department of Mechanical Engineering, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li, Taoyuan, 320, Taiwan;(2) Department of Anaesthesiology, College of Medicine, National Taiwan University, Taipei, Taiwan;(3) Research Center for Adaptive Data Analysis, National Central University, Taoyuan, Taiwan; |
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Abstract: | The human heartbeat interval is determined by complex nerve control and environmental inputs. As a result, the heartbeat interval
for a human is a complex time series, as shown by previous studies. Most of the analysis algorithms proposed for characterizing
the profile of heartbeat time series, such as detrended fluctuation analysis and multi-scale entropy, are based on various
characteristics of dynamics. In this study, we present an empirical mode decomposition-based intrinsic mode analysis, which
uses the appearance energy index (AEI) to quantify the property of long-term correlation, and structure index (SI) to characterize
the internal modulation of data. This presented algorithm was used to investigate the human heartbeat time series downloaded
from PhysioBank. We found the profiles of human heartbeat time series of subjects with congestive heart failure (CHF) or atrial
fibrillation (AF) are significantly different from those of healthy subjects in internal modulation as shown by SI. Moreover,
AEI is the critical characteristics for verifying subjects with CHF from subjects with AF in a degree of long-term correlation.
Both AEI and SI contribute to presenting the characteristic profiles of a human heartbeat time series. |
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