Measuring body temperature time series regularity using Approximate Entropy and Sample Entropy.
Approximate Entropy (ApEn) and Sample Entropy (SampEn) have proven to be a valuable analyzing tool for a number of physiological signals. However, the characterization of these metrics is still lacking. We applied ApEn and SampEn to body temperature time series recorded from patients in critical state. This study was aimed at finding the optimal analytical configuration to best distinguish between survivor and non-survivor records, and at gaining additional insight into the characterization of such tools. A statistical analysis of the results was conducted to support the parameter and metric selection criteria for this type of physiological signal.
Cuesta-Frau
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D
Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, 03801 Alcoi, Spain. dcuesta@disca.upv.es
Miro-Martinez
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P
Oltra-Crespo
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S
Varela-Entrecanales
M
M
Aboy
M
M
Novak
D
D
Austin
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D
D000465:Algorithms; D001185:Artificial Intelligence; D001831:Body Temperature; D003198:Computer Simulation; D003201:Computers; D003422:Critical Care / Q000379:methods*; D019277:Entropy; D006801:Humans; D007362:Intensive Care Units; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D012984:Software; D016896:Treatment Outcome