Analysis of physiological signals using state space correlation entropy

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.

Keywords

• 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