A gradient-based adaptive learning framework for online seizure prediction
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Summary
An adaptive prediction framework is proposed, which is capable of accumulating knowledge of pre-seizure EEG patterns by monitoring long-term EEG recordings and is effective to improve prediction accuracy over time and thus achieve a personalized seizure predication for each patient.
- Type
- article
- Published
- 2014-06-01
- Cited by
- 3
- References
- 24
- OpenAlex
- https://openalex.org/W2082600129
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14911503
Keywords
Computer science, Artificial intelligence, Machine learning
References
- Multivariate linear discrimination of seizures.
- Epilepsy : a comprehensive textbook
- Kulback-Leibler and renormalized entropies: applications to electroencephalograms of epilepsy patients.
- Correlation Dimension Maps of EEG from Epileptic Absences
- The First International Collaborative Workshop on Seizure Prediction: summary and data description.
- Dynamics of brain electrical activity
- Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings.
- Nonlinear dynamical analysis of EEG and MEG: review of an emerging field.
- CAN EPILEPTIC SEIZURES BE PREDICTED? EVIDENCE FROM NONLINEAR TIME SERIES ANALYSIS OF BRAIN ELECTRICAL ACTIVITY
- Seizure prediction by non‐linear time series analysis of brain electrical activity
- Changes in a measure of cardiac vagal activity before and after epileptic seizures.
- Seizure prediction: the long and winding road.
- On the predictability of epileptic seizures.
- Adaptive epileptic seizure prediction system
- How well can epileptic seizures be predicted? An evaluation of a nonlinear method.
- Epileptic Seizures May Begin Clinical Study Hours in Advance of Clinical Onset: A Report of Five Patients
- [Anatomy of the brain].
- On the dynamics of the human brain in temporal lobe epilepsy.
- Anticipation of epileptic seizures from standard EEG recordings.
- [Anatomy of the brain].
Cited by
- Mathematical Programming Formulations and Algorithms for Discrete k-Median Clustering of Time-Series Data
- An Interactive Multisensing Framework for Personalized Human Robot Collaboration and Assistive Training Using Reinforcement Learning
- Feasibility of using Error-related potentials as an appropriate method for adaptation in a brain-computer interface
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