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.
Keywords: ECG; EEG; SSCE; SVM classifier; correlation methods; electrocardiography; electroencephalography; entropy; medical disorders; medical signal processing; permutation entropy; physiological signals; real valued signals; sample entropy; shockable ventricular arrhythmia; signal classification; signal reconstruction; speech; speech processing; state space correlation entropy; state space reconstruction; state-space methods; support vector machine; support vector machines; synthetic valued signals; time series.