ArticleNeural regeneration research2012
Application of approximate entropy on dynamic characteristics of epileptic absence seizure.
Article in Neural regeneration research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- A comprehensive analysis of brain network complexity in task-based fMRI using entropy: systematic review.Brain imaging and behavior · 2026Pooled it
- An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network.IEEE journal of translational engineering in health and medicine · 2024Article
- Closed-loop optogenetic control of the dynamics of neural activity in non-human primates.Nature biomedical engineering · 2023Article
- Spatio-Temporal Dynamics of Entropy in EEGS during Music Stimulation of Alzheimer's Disease Patients with Different Degrees of Dementia.Entropy (Basel, Switzerland) · 2022Article
- Increased Sample Entropy in EEGs During the Functional Rehabilitation of an Injured Brain.Entropy (Basel, Switzerland) · 2019Article
- Automatic seizure detection using three-dimensional CNN based on multi-channel EEG.BMC medical informatics and decision making · 2018Article
- Approximate entropy and support vector machines for electroencephalogram signal classification.Neural regeneration research · 2013Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalogram signals are time-varying complex electrophysiological signals. Existing studies show that approximate entropy, which is a nonlinear dynamics index, is not an ideal method for electroencephalogram analysis. Clinical electroencephalogram measurements usually contain electrical interference signals, creating additional challenges in terms of maintaining robustness of the analytic methods. There is an urgent need for a novel method of nonlinear dynamical analysis of the electroencephalogram that can characterize seizure-related changes in cerebral dynamics. The aim of this paper was to study the fluctuations of approximate entropy in preictal, ictal, and postictal electroencephalogram signals from a patient with absence seizures, and to improve the algorithm used to calculate the approximate entropy. The approximate entropy algorithm, especially our modified version, could accurately describe the dynamical changes of the brain during absence seizures. We could also demonstrate that the complexity of the brain was greater in the normal state than in the ictal state. The fluctuations of the approximate entropy before epileptic seizures observed in this study can form a good basis for further study on the prediction of seizures with nonlinear dynamics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.