Our goal was to determine the contributions of sympathetic and parasympathetic activity to entropy measures of heart rate variability (HRV). We compared our results with two commonly used methods to analyze HRV: standard deviation (SDNN) and power spectral analysis (HF norm). Beat-by-beat analysis of R-R intervals was performed in conscious dogs. The R-R intervals were analyzed with approximate entropy (ApEn) and entropy of symbolic dynamics (SymDyn) to assess the effects of reducing system complexity. This was achieved by pharmacologically inhibiting sympathetic, parasympathetic, and total autonomic nervous system regulation of heart rate. Three conditions were examined: rest, standing, and systemic hypotension. At rest or standing, sympathetic inhibition (propranolol) had no effect on ApEn or SymDyn, whereas parasympathetic (atropine) and combined (propranolol + atropine) inhibition reduced both entropy measures to near zero. Systemic hypotension reduced both entropy measures in intact dogs. When hypotension was induced after sympathetic inhibition, ApEn was increased compared with hypotension alone, whereas parasympathetic inhibition with hypotension resulted in near-zero ApEn. Changes in the entropy measures of HRV were directionally similar to changes in SDNN and HF norm. These results indicate that the entropy of R-R intervals reflects parasympathetic modulation of heart rate.
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http://dx.doi.org/10.1152/ajpheart.1998.274.4.H1099 | DOI Listing |
J Phys Chem Lett
January 2025
Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States.
Continuous production of entropy and the corresponding energy dissipation is a defining characteristic of nonequilibrium systems. When a system's full chemical kinetic description is known, its entropy production rate can be computed from the microscopic rate constants. However, such a calculation typically underestimates energy dissipation when the states of the underlying system are mesoscopic, i.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human-computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Computer Science, Minnan Normal University, Zhangzhou 363000, China.
With the development of intelligent technology, data in practical applications show exponential growth in quantity and scale. Extracting the most distinguished attributes from complex datasets becomes a crucial problem. The existing attribute reduction approaches focus on the correlation between attributes and labels without considering the redundancy.
View Article and Find Full Text PDFEntropy (Basel)
January 2025
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China.
This paper selects daily stock market trading data of RCEP member countries from 3 December 2007 to 9 December 2024 and employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model and transfer entropy to measure the time-varying volatility spillover effects among the stock markets of the sampled countries. The results indicate that the signing of the RCEP has strengthened the interconnectedness of member countries' stock markets, with an overall upward trend in volatility spillover effects, which become even more pronounced during periods of financial turbulence. Within the structure of RCEP member stock markets, China is identified as a net risk receiver, while countries like Japan and South Korea act as net risk spillover contributors.
View Article and Find Full Text PDFEntropy (Basel)
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LARIS, SFR MATHSTIC, Univ Angers, F-49000 Angers, France.
Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt changes in signals.
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