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• W2793010152 abstract "The occurrence of epileptic seizures in seemingly random patterns takes a great toll on persons with epilepsy and their families. Seizure prediction may markedly improve epilepsy management and, therefore, the quality of life of persons with epilepsy. Literature review. Seizures tend to occur following complex non-random patterns. Circadian oscillators may contribute to the rhythmic patterns of seizure occurrence. Complex mathematical models based on chaos theory try to explain and even predict seizure occurrence. There are several patterns of epileptic seizure occurrence based on seizure location, seizure semiology, and hormonal factors, among others. These patterns are most frequently described for large populations. Inter-individual variability and complex interactions between the rhythmic generators continue to make it more difficult to predict seizures in any individual person. The increasing use of large databases and machine learning techniques may help better define patterns of seizure occurrence in individual patients. Improvements in seizure detection –such as wearable seizure detectors— and in seizure prediction –such as machine learning techniques and artificial as well as neuronal networks— promise to provide further progress in the field of epilepsy and are being applied to closed-loop systems for the treatment of epilepsy. Seizures tend to occur following complex and patient-specific patterns despite their apparently random occurrence. A better understanding of these patterns and current technological advances may allow the implementation of closed-loop detection, prediction, and treatment systems in routine clinical practice." @default.
• W2793010152 created "2018-03-29" @default.
• W2793010152 creator A5024284175 @default.
• W2793010152 creator A5046645524 @default.
• W2793010152 creator A5058311554 @default.
• W2793010152 date "2019-01-01" @default.
• W2793010152 modified "2023-10-09" @default.
• W2793010152 title "Patterns of epileptic seizure occurrence" @default.
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• W2793010152 doi "https://doi.org/10.1016/j.brainres.2018.02.032" @default.
• W2793010152 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29481793" @default.
• W2793010152 hasPublicationYear "2019" @default.
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