Key result
The modified Approximate Entropy index provided more stable measurements of complexity and better discriminated between simulated signals and experimental EEG states compared to standard ApEn.
A modified Approximate Entropy index provides more stable complexity measurements for biomedical signals by compensating for oversampling and removing low frequency trends.
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ApEn interpretation warrants caution in clinical signals; leaves open optimal parameter standardization for reliable use.
Luca Mesin (2018) studied Vegetative state (n=2). Modified Approximate Entropy (ApEn) algorithm vs. Standard Approximate Entropy was evaluated on Stability and discrimination capability of complexity estimation. The modified Approximate Entropy index provided more stable measurements of complexity and better discriminated between simulated signals and experimental EEG states compared to standard ApEn.
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