Literature DB >> 26552573

Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies.

Jonathan Bidwell1, Thanin Khuwatsamrit2, Brittain Askew3, Joshua Andrew Ehrenberg3, Sandra Helmers3.   

Abstract

This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use.
Copyright © 2015. Published by Elsevier Ltd.

Entities:  

Keywords:  Epilepsy, Seizure reporting, Accelerometry, Non-EEG seizure detection, ECG-based seizure detection, Automated seizure detection

Mesh:

Substances:

Year:  2015        PMID: 26552573     DOI: 10.1016/j.seizure.2015.09.006

Source DB:  PubMed          Journal:  Seizure        ISSN: 1059-1311            Impact factor:   3.184


  3 in total

Review 1.  Seizure detection: do current devices work? And when can they be useful?

Authors:  Xiuhe Zhao; Samden D Lhatoo
Journal:  Curr Neurol Neurosci Rep       Date:  2018-05-23       Impact factor: 5.081

2.  Self-Management Apps for People With Epilepsy: Systematic Analysis.

Authors:  Mohsen Zaied Alzamanan; Kheng-Seang Lim; Maizatul Akmar Ismail; Norjihan Abdul Ghani
Journal:  JMIR Mhealth Uhealth       Date:  2021-05-28       Impact factor: 4.773

3.  Performance of ECG-based seizure detection algorithms strongly depends on training and test conditions.

Authors:  Amirhossein Jahanbekam; Jan Baumann; Robert D Nass; Christian Bauckhage; Holger Hill; Christian E Elger; Rainer Surges
Journal:  Epilepsia Open       Date:  2021-07-20
  3 in total

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