Analysis of physiological signals using state space correlation entropy

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 cor...

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Main Authors: Rajesh Kumar Tripathy, Suman Deb, Samarendra Dandapat
Format: Article
Language:English
Published: Wiley 2016-11-01
Series:Healthcare Technology Letters
Subjects:
ECG
EEG
Online Access:https://digital-library.theiet.org/content/journals/10.1049/htl.2016.0065
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spelling doaj-ffc5b23390b243728e3dc87eb3ea47ce2021-04-02T14:17:43ZengWileyHealthcare Technology Letters2053-37132016-11-0110.1049/htl.2016.0065HTL.2016.0065Analysis of physiological signals using state space correlation entropyRajesh Kumar Tripathy0Suman Deb1Suman Deb2Samarendra Dandapat3Samarendra Dandapat4Indian Institute of Technology GuwahatiIndian Institute of Technology GuwahatiIndian Institute of Technology GuwahatiIndian Institute of Technology GuwahatiIndian Institute of Technology GuwahatiIn 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.https://digital-library.theiet.org/content/journals/10.1049/htl.2016.0065medical disorderselectrocardiographyelectroencephalographyspeechmedical signal processingspeech processingentropystate-space methodscorrelation methodstime seriessignal reconstructionsupport vector machinessignal classificationphysiological signalsstate space correlation entropytime seriesSSCEstate space reconstructionsynthetic valued signalsreal valued signalsSVM classifiersupport vector machinesample entropypermutation entropyshockable ventricular arrhythmiaECGEEGspeech
collection DOAJ
language English
format Article
sources DOAJ
author Rajesh Kumar Tripathy
Suman Deb
Suman Deb
Samarendra Dandapat
Samarendra Dandapat
spellingShingle Rajesh Kumar Tripathy
Suman Deb
Suman Deb
Samarendra Dandapat
Samarendra Dandapat
Analysis of physiological signals using state space correlation entropy
Healthcare Technology Letters
medical disorders
electrocardiography
electroencephalography
speech
medical signal processing
speech processing
entropy
state-space methods
correlation methods
time series
signal reconstruction
support vector machines
signal classification
physiological signals
state space correlation entropy
time series
SSCE
state space reconstruction
synthetic valued signals
real valued signals
SVM classifier
support vector machine
sample entropy
permutation entropy
shockable ventricular arrhythmia
ECG
EEG
speech
author_facet Rajesh Kumar Tripathy
Suman Deb
Suman Deb
Samarendra Dandapat
Samarendra Dandapat
author_sort Rajesh Kumar Tripathy
title Analysis of physiological signals using state space correlation entropy
title_short Analysis of physiological signals using state space correlation entropy
title_full Analysis of physiological signals using state space correlation entropy
title_fullStr Analysis of physiological signals using state space correlation entropy
title_full_unstemmed Analysis of physiological signals using state space correlation entropy
title_sort analysis of physiological signals using state space correlation entropy
publisher Wiley
series Healthcare Technology Letters
issn 2053-3713
publishDate 2016-11-01
description 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.
topic medical disorders
electrocardiography
electroencephalography
speech
medical signal processing
speech processing
entropy
state-space methods
correlation methods
time series
signal reconstruction
support vector machines
signal classification
physiological signals
state space correlation entropy
time series
SSCE
state space reconstruction
synthetic valued signals
real valued signals
SVM classifier
support vector machine
sample entropy
permutation entropy
shockable ventricular arrhythmia
ECG
EEG
speech
url https://digital-library.theiet.org/content/journals/10.1049/htl.2016.0065
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AT sumandeb analysisofphysiologicalsignalsusingstatespacecorrelationentropy
AT samarendradandapat analysisofphysiologicalsignalsusingstatespacecorrelationentropy
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