Physiological time-series analysis: what does regularity quantify? Export

@article{citeulike:2316905, abstract = {Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity that appears to have potential application to a wide variety of physiological and clinical time-series data. The focus here is to provide a better understanding of ApEn to facilitate its proper utilization, application, and interpretation. After giving the formal mathematical description of ApEn, we provide a multistep description of the algorithm as applied to two contrasting clinical heart rate data sets. We discuss algorithm implementation and interpretation and introduce a general mathematical hypothesis of the dynamics of a wide class of diseases, indicating the utility of ApEn to test this hypothesis. We indicate the relationship of ApEn to variability measures, the Fourier spectrum, and algorithms motivated by study of chaotic dynamics. We discuss further mathematical properties of ApEn, including the choice of input parameters, statistical issues, and modeling considerations, and we conclude with a section on caveats to ensure correct ApEn utilization. (Citado por 285)}, address = {Department of Medicine, Beth Israel Hospital, Boston, Massachusetts 02215.}, author = {Pincus, S. M. and Goldberger, A. L.}, citeulike-article-id = {2316905}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/8184944}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=8184944}, issn = {0002-9513}, journal = {Am J Physiol}, keywords = {approximate\_entropy, entropy}, month = {April}, number = {4 Pt 2}, posted-at = {2008-02-01 00:00:15}, priority = {2}, title = {Physiological time-series analysis: what does regularity quantify?}, url = {http://view.ncbi.nlm.nih.gov/pubmed/8184944}, volume = {266}, year = {1994} }

TY - JOUR ID - citeulike:2316905 L3 - citeulike-article-id:2316905 N2 - Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity that appears to have potential application to a wide variety of physiological and clinical time-series data. The focus here is to provide a better understanding of ApEn to facilitate its proper utilization, application, and interpretation. After giving the formal mathematical description of ApEn, we provide a multistep description of the algorithm as applied to two contrasting clinical heart rate data sets. We discuss algorithm implementation and interpretation and introduce a general mathematical hypothesis of the dynamics of a wide class of diseases, indicating the utility of ApEn to test this hypothesis. We indicate the relationship of ApEn to variability measures, the Fourier spectrum, and algorithms motivated by study of chaotic dynamics. We discuss further mathematical properties of ApEn, including the choice of input parameters, statistical issues, and modeling considerations, and we conclude with a section on caveats to ensure correct ApEn utilization. (Citado por 285) IS - 4 Pt 2 JF - Am J Physiol SN - 0002-9513 AD - Department of Medicine, Beth Israel Hospital, Boston, Massachusetts 02215. TI - Physiological time-series analysis: what does regularity quantify? VL - 266 KW - approximate_entropy KW - entropy AU - Pincus, SM AU - Goldberger, AL PY - 1994/04// UR - http://view.ncbi.nlm.nih.gov/pubmed/8184944 ER -