Related Experiment Video
Updated: Mar 6, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
Analysis of physiological signals using state space correlation entropy
Rajesh Kumar Tripathy1, Suman Deb1, Samarendra Dandapat1
1Department of Electronics and Electrical Engineering , Indian Institute of Technology Guwahati , Guwahati 781039 , India.
Abstract:
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.
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