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Estimation of Complexity of Sampled Biomedical Continuous Time Signals Using Approximate Entropy
1Mathematical Biology and Physiology, Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Turin, Italy.
Frontiers in Physiology
|June 27, 2018
Summary
Approximate entropy (ApEn) is a popular biomedical complexity index, but its results vary with parameters. A modified ApEn offers more stable complexity measurements for simulated data and EEG, improving data analysis reliability.
Area of Science:
- Biomedical Engineering
- Non-linear Dynamics
- Signal Processing
Background:
- Approximate entropy (ApEn) is widely used for biomedical data complexity analysis.
- ApEn provides stable complexity indications for short, noisy data segments.
- Existing literature shows ApEn's sensitivity to parameter choices, leading to inconsistent results.
Purpose of the Study:
- To investigate the parameter dependency of Approximate entropy (ApEn) in biomedical signal analysis.
- To introduce a modified ApEn index addressing the limitations of the standard ApEn.
- To enhance the reliability and comparability of complexity measurements in biomedical data.
Main Methods:
- Non-linear analysis of simulated and experimental electroencephalogram (EEG) data.
- Evaluation of ApEn's sensitivity to sampling rate, embedding dimension, tolerance, epoch duration, and low-frequency trends.
- Development and application of a modified ApEn by compensating for oversampling, ignoring self-recurrences, selecting a fixed recurrence percentage, and removing low-frequency trends.
Main Results:
- ApEn's results are highly dependent on parameter selection, leading to contradicting findings.
- Parameter variations were shown to affect ApEn in both simulated data and experimental EEG.
- The modified ApEn demonstrated more stable complexity measurements for simulated data and EEG compared to standard ApEn.
Conclusions:
- Standard ApEn requires specific parameter ranges, Nyquist-limit sampling, and removal of low-frequency trends for reliable complexity estimation.
- These guidelines can improve the comparability, interpretation, and replication of biomedical complexity studies.
- The modified ApEn presents a valuable alternative, extending stable complexity measurement possibilities across a wider parameter range.
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