Literature DB >> 22050981

Pre-ictal autonomic changes.

Robert S Delamont1, Matthew C Walker.   

Abstract

Autonomic measures frequently alter with seizure activity and with brain state and so theoretically, there could be pre-ictal changes in autonomic function. However, there are considerable confounders. First, the measurement of autonomic function is not straightforward; heart rate and measures derived form heart rate have been those that have used the most in assessing changes in autonomic function. Second, autonomic function can vary considerably over the 24h cycle and can change suddenly depending on internal and external stimuli (e.g. fear, pain) and so any measures of changes in autonomic function will lose specificity. Third, changes in autonomic function in response to seizures, depends upon the individual, seizure type and spread of the seizure and even then can vary from seizure to seizure in the same individual. The idea that there will be well-defined, unique autonomic changes that occur in the pre-ictal period is very unlikely. These factors make it unlikely that autonomic function monitoring can be used successfully as a means of seizure prediction. However, in sleep, changes in autonomic function relate to changes in arousal state and since such states and the transition between such states may predict seizure occurrence in certain individuals, autonomic function could be a helpful determinant of seizure risk at certain stages of sleep. This hypothesis has, however, yet to be tested.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 22050981     DOI: 10.1016/j.eplepsyres.2011.10.016

Source DB:  PubMed          Journal:  Epilepsy Res        ISSN: 0920-1211            Impact factor:   3.045


  4 in total

1.  Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning.

Authors:  Thomas De Cooman; Kaat Vandecasteele; Carolina Varon; Borbála Hunyadi; Evy Cleeren; Wim Van Paesschen; Sabine Van Huffel
Journal:  Front Neurol       Date:  2020-02-26       Impact factor: 4.003

2.  Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability.

Authors:  Toshitaka Yamakawa; Miho Miyajima; Koichi Fujiwara; Manabu Kano; Yoko Suzuki; Yutaka Watanabe; Satsuki Watanabe; Tohru Hoshida; Motoki Inaji; Taketoshi Maehara
Journal:  Sensors (Basel)       Date:  2020-07-17       Impact factor: 3.576

3.  Ictal autonomic changes as a tool for seizure detection: a systematic review.

Authors:  Anouk van Westrhenen; Thomas De Cooman; Richard H C Lazeron; Sabine Van Huffel; Roland D Thijs
Journal:  Clin Auton Res       Date:  2018-10-30       Impact factor: 4.435

4.  Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy.

Authors:  Adriana Leal; Mauro F Pinto; Fábio Lopes; Anna M Bianchi; Jorge Henriques; Maria G Ruano; Paulo de Carvalho; António Dourado; César A Teixeira
Journal:  Sci Rep       Date:  2021-03-16       Impact factor: 4.379

  4 in total

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