access icon openaccess 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 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.

Inspec keywords: speech; entropy; speech processing; electrocardiography; correlation methods; support vector machines; signal reconstruction; time series; state-space methods; medical disorders; medical signal processing; electroencephalography; signal classification

Other keywords: ECG; state space reconstruction; shockable ventricular arrhythmia; state space correlation entropy; sample entropy; speech; synthetic valued signals; EEG; physiological signals; permutation entropy; SVM classifier; support vector machine; real valued signals; time series; SSCE

Subjects: Speech and audio signal processing; Other topics in statistics; Electrodiagnostics and other electrical measurement techniques; Electrical activity in neurophysiological processes; Knowledge engineering techniques; Other topics in statistics; Digital signal processing; Speech and biocommunications; Biology and medical computing; Speech processing techniques; Bioelectric signals; Probability theory, stochastic processes, and statistics

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