Analysis of physiological signals using state space correlation entropy

Rajesh Kumar Tripathy; Suman Deb; Samarendra Dandapat

Analysis of physiological signals using state space correlation entropy

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Author(s): Rajesh Kumar Tripathy ¹ ; Suman Deb ¹ ; Samarendra Dandapat ¹
- Affiliations: 1: Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India
Source: Volume 4, Issue 1, February 2017, p. 30 – 33
DOI: 10.1049/htl.2016.0065 , Online ISSN 2053-3713

Received 27/07/2016, Accepted 19/10/2016, Revised 15/10/2016, Published 16/02/2017

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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Analysis of physiological signals using state space correlation entropy

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