ArticleFrontiers in physiology2018
Estimation of Complexity of Sampled Biomedical Continuous Time Signals Using Approximate Entropy.
Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Detecting Unusual Trading Patterns on Cryptocurrency Exchanges by Means of Complexity Measures.Entropy (Basel, Switzerland) · 2026Article
- Electrophysiological biomarkers for foundational signal markers in chronic pancreatitis using machine learning models.Frontiers in pain research (Lausanne, Switzerland) · 2026Article
- Pupillary Hippus as a Biomarker: Spectral Signatures and Complexity Approaches in Autonomic and Clinical Contexts.Bioengineering (Basel, Switzerland) · 2025Review
- Closed-Loop Transcranial Electrical Neurostimulation for Sustained Attention Enhancement: A Pilot Study towards Personalized Intervention Strategies.Bioengineering (Basel, Switzerland) · 2024Article
- Electroencephalography-Based Brain-Machine Interfaces in Older Adults: A Literature Review.Bioengineering (Basel, Switzerland) · 2023Review
- Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review.International journal of environmental research and public health · 2022Review
- On Quantization Errors in Approximate and Sample Entropy.Entropy (Basel, Switzerland) · 2021Article
- Associations between Cardiovascular Signal Entropy and Cognitive Performance over Eight Years.Entropy (Basel, Switzerland) · 2021Article
- Measures of repolarization variability predict ventricular arrhythmogenesis in heptanol-treated Langendorff-perfused mouse hearts.Current research in physiology · 2021Article
- Associations between Neurocardiovascular Signal Entropy and Physical Frailty.Entropy (Basel, Switzerland) · 2020Article
- Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators.Entropy (Basel, Switzerland) · 2020Article
- [Research progress on multiscale entropy algorithm and its application in neural signal analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Entropy Analysis for the Evaluation of Respiratory Changes Due to Asbestos Exposure and Associated Smoking.Entropy (Basel, Switzerland) · 2019Article
- Quantification of Beat-To-Beat Variability of Action Potential Durations in Langendorff-Perfused Mouse Hearts.Frontiers in physiology · 2018Article
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1 author.
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Abstract
Non-linear analysis found many applications in biomedicine. Approximate entropy (ApEn) is a popular index of complexity often applied to biomedical data, as it provides quite stable indications when processing short and noisy epochs. However, ApEn strongly depends on parameters, which were chosen in the literature in wide ranges. This paper points out that ApEn depends on sampling rate of continuous time signals, embedding dimension, tolerance (under which a match is identified), epoch duration and low frequency trends. Moreover, contradicting results can be obtained changing parameters. This was found both in simulations and in experimental EEG. These limitations of ApEn suggest the introduction of an alternative index, here called modified ApEn, which is based on the following principles: oversampling is compensated, self-recurrences are ignored, a fixed percentage of recurrences is selected and low frequency trends are removed. The modified index allows to get more stable measurements of the complexity of the tested simulated data and EEG. The final conclusions are that, in order to get a reliable estimation of complexity using ApEn, parameters should be chosen within specific ranges, data must be sampled close to the Nyquist limit and low frequency trends should be removed. Following these indications, different studies could be more easily compared, interpreted and replicated. Moreover, the modified ApEn can be an interesting alternative, which extends the range of parameters for which stable indications can be achieved.
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