Paper Info
Title
On applying approximate entropy to ECG signals for knowledge discovery on the example of big sensor data
Abstract
Information entropy as a universal and fascinating statistical concept is helpful for numerous problems in the computational sciences. Approximate entropy (ApEn), introduced by Pincus (1991), can classify complex data in diverse settings. The capability to measure complexity from a relatively small amount of data holds promise for applications of ApEn in a variety of contexts. In this work we apply ApEn to ECG data. The data was acquired through an experiment to evaluate human concentration from 26 individuals. The challenge is to gain knowledge with only small ApEn windows while avoiding modeling artifacts. Our central hypothesis is that for intra subject information (e.g. tendencies, fluctuations) the ApEn window size can be significantly smaller than for inter subject classification. For that purpose we propose the term truthfulness to complement the statistical validity of a distribution, and show how truthfulness is able to establish trust in their local properties.
Year
DOI
Venue
2012
10.1007/978-3-642-35236-2_64
AMT
Keywords
Field
DocType
ecg data,ecg signal,small amount,intra subject information,small apen windows,knowledge discovery,apen window size,information entropy,fascinating statistical concept,complex data,approximate entropy,big sensor data,inter subject classification,big data
Data mining,Approximate entropy,Sample entropy,Validity,Computer science,Complex data type,Knowledge extraction,Artificial intelligence,Entropy (information theory),Big data,Machine learning
Conference
Citations 
PageRank 
References 
8
0.90
7
Authors
7
Name
Order
Citations
PageRank
Andreas Holzinger12886253.75
Christof Stocker2745.07
Manuel Bruschi3171.47
Andreas Auinger414818.55
Hugo Silva522730.18
Hugo Gamboa6409100.80
Ana Fred721617.07