Sample Entropy

This short code calculates the sample entropy (SampEn) of a given time series data.

Description

SampEn is a measure of complexity that can be easily applied to any type of time series data, including physiological data such as heart rate variability and EEG data.

SampEn is conceptually similar to approximate entropy (ApEn), but has following differences:

1) SampEn does not count self-matching. The possible trouble of having log(0) is avoided by taking logarithm at the latest step.
2) SampEn does not depend on the datasize as much as ApEn does. The comparison is shown in the graph above. This property makes it amenable to applications with relatively short data size.

This code uses the same vectorisation technique as in Fast Approximate Entropy, another submission from the same author.

Acknowledgements

Fast Approximate Entropy inspired this file.

• MATLAB release: MATLAB 7.13 (R2011b)

[miss khatimah]

 

[Alexander]

Regard my previous one star rating as a mistake. Apparently mathworks immediately submitted the rating when I clicked on one of the stars.

[Alexander]

 

[Alexander]

 

[Kavitha]

May I know what is the first argument dim in the function? I want to pass on-channel EEG signal 500 samples (with sampling frequency 128 Hz) to the program to calculate entropy? Also please advice me how to use 'r'?

[Hugh]

oops... I found that function rand is uniform distribution, not white noise. Your code is right!

[Hugh]

I have a question.
It is known that if the input data is white noise, SampEn will decrease as tau increases. However, I tried this code with input rand(1,2000) and different tau, and it seems that SampEn will have similar values no matter what tau. What's wrong with this?