Characterization of sample entropy in the context of biomedical signal analysis.
Sample Entropy (SampEn) has been proposed as a method to overcome limitations associated with approximate entropy (ApEn). The initial paper describing the SampEn metric included a characterization study comparing both ApEn and SampEn against theoretical results and concluded that SampEn is both more consistent and agrees more closely with theory for known random processes than ApEn. SampEn has been used in several studies to analyze the regularity of clinical and experimental time series. However, questions regarding how to interpret SampEn in certain clinical situations and its relationship to classical signal parameters remain unanswered. In this paper we report the results of a characterization study intended to provide additional insights regarding the interpretability of SampEn in the context of biomedical signal analysis.
Aboy
Mateo
M
Department of Electrical Engineering, Oregon Institute of Technology (Portland Campus), Portland, OR, USA. mateoaboy@ieee.org
Cuesta-Frau
David
D
Austin
Daniel
D
Mico-Tormos
Pau
P
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D019277:Entropy; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted