Open AccessAnalysis of physiological signals using state space correlation entropy
Author(s) -
Tripathy Rajesh Kumar,
Deb Suman,
Dandapat Samarendra
Publication year - 2017
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2016.0065
Subject(s) - entropy (arrow of time) , pattern recognition (psychology) , correlation , sample entropy , mathematics , transfer entropy , artificial intelligence , measure (data warehouse) , time series , embedding , computer science , statistical physics , statistics , data mining , principle of maximum entropy , physics , geometry , quantum mechanics
In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia.