Howtosignalprocessing
- What does sample entropy measure?
- How do you measure entropy of a signal?
- What is embedding dimension in sample entropy?
- What is cross sample entropy?
What does sample entropy measure?
Sample Entropy is a useful tool for investigating the dynamics of heart rate and other time series. Sample Entropy is the negative natural logarithm of an estimate of the conditional probability that subseries (epochs) of length m that match pointwise within a tolerance r also match at the next point.
How do you measure entropy of a signal?
To compute the instantaneous spectral entropy given a time-frequency power spectrogram S(t,f), the probability distribution at time t is: P ( t , m ) = S ( t , m ) ∑ f S ( t , f ) . Then the spectral entropy at time t is: H ( t ) = − ∑ m = 1 N P ( t , m ) log 2 P ( t , m ) .
What is embedding dimension in sample entropy?
Details. The sample entropy is computed using: hq(m,r) = log(Cq(m,r)/Cq(m+1,r)), where m is the embedding dimension and r is the radius of the neighbourhood.
What is cross sample entropy?
Cross-sample entropy (CSE) allows to analyze the association level between two time series that are not necessarily stationary. The current criteria to estimate the CSE are based on the normality assumption, but this condition is not necessarily satisfied in reality.
