Physiological time-series analysis using approximate entropy and sample entropy.

By Joshua S. Richman, J. R. Moorman. American Journal of Physiology. Heart and Circulatory Physiology, 2000

7,298 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b2895c22fa8af7a3eddf1ebf0bedbd66fc5d3fcf

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

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Citations in the corpus

Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks (2007, methodology): “The value of the ApEn is determined as shown in the following steps [11], [16].”
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques (2015, methodology): “Time domain features mainly include sample entropy [12], approximate entropy [12], permutation entropy, fractal dimension, Hjorth parameters [13], Hurst component [10], and Lyapunov exponent [14].”
Multiscale entropy analysis of complex physiologic time series. (2002, methodology): “Entropy-based algorithms [5, 6] for measuring the complexity of physiologic time series have been widely used.”
Multi-scale entropy analysis of Mississippi River flow (2007, methodology): “To overcome some practical limitations of the K-S entropy related to the finite length of ‘‘real-world’’ time series and noise contamination, AE entropy (Pincus 1991) and sample entropy ( Richman and Moorman 2000 ) were developed to quantify the degree of irregularity of a signal.”
A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction (2010, methodology): “Two of these alternatives are approximate entropy (ApEn) and sample entropy (SampEn), which have found many applications in processing physiological signals (Pincus, 1991; Richman and Moorman, 2000; Chen et al., 2006; Kaffashi et al., 2008; Liao et al., 2008) as well as mechanical vibration signal”
A new approach to quantify power-law cross-correlation and its application to commodity markets (2011, background): “Richman and Moorman measure the similarity of two distinct physiological time series by means of cross approximation entropy and cross sample entropy [19].”
Sample entropy analysis of neonatal heart rate variability. (2002, background): “We developed a new related measure of time series regularity that we have called sample entropy (SampEn) (22).”
Differential entropy feature for EEG-based vigilance estimation (2013, background): “Various EEG features can be classified into four categories: time series feature [9], spectral feature [5], spatial synchronization feature [10], and complexity measure feature [11].”
Intelligent prognostics for battery health monitoring based on sample entropy (2011, methodology): “The use of SampEn was introduced by Richman and Moorman (2000) which was the improvement of previous method namely approximate entropy (ApEn) by Pincus (1991) for measuring the complexity of time series.”