Approximate Entropy and Absolute Band Power -Frequency Bands

Feature extraction of nonlinear (approximate entropy) and linear (absolute band power) band features for disease characterization


Updated 18 Dec 2022

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Pograms used for feature extraction in neuropathic pain characterization (n=36) vs a control group (n=13). Thus, three sets of codes are available.
Main.m -> main program that applies function of FeatureExtraction to each subject (if subjects are in ID folders)
FeatureExtraction_NP.m -> Function that extracts approximate entropy and absolute band power for 22 electrodes in the five clinical frequency bands (delta, theta, alpha, beta, gamma)
ENTROPY.m -> Function that applies approximate entropy algorithm using two functions of the Predictive Maintaeance Toolbox: PhaseSpaceReconstruction and approximateEntropy. Dimension (m) is defined as 3, but the lag or "delay" is computed specifically for subject and condition.
The same programs but applied for a control dataset included feature extraction of 19 electrodes and 13 subjects:
ENTROPY.m (the same)
% ====== FEATURE EXTRACTION For Chronic NP - 36 subjects======
% (1) Source 1: Entropies = 5 x 22
% -- 5 bands: delta, theta, alpha, beta, gamma
% -- 22 channels
% (2) Source 2: Absolute Band Power = 5 x 22
% -- 5 bands: delta, theta, alpha, beta and gamma
% -- 22 channels
% (3) Total number of features: 110 x 110 = 220 (K)
% (4) Total number of observations: 10 segments x 36 participants = 360 (N)
% (5) Apply FeatureExtraction function for the individual vector (consisting of Algorithm 1 and 2)
% (6) Both entropy and power features (220 K) are returned to the original features vector in Main program, and next subject is computed

Cite As

Daniela Montemayor Zolezzi (2023). Approximate Entropy and Absolute Band Power -Frequency Bands (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2021b
Compatible with any release
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