Literature DB >> 26366675

Novel techniques for automated seizure registration: Patients' wants and needs.

Christian Hoppe1, Mieke Feldmann2, Barbara Blachut2, Rainer Surges2, Christian E Elger2, Christoph Helmstaedter2.   

Abstract

OBJECTIVES: Patient-reported seizure frequency is essential for therapy management and clinical research but lacks validity mainly due to seizure-induced seizure unawareness. Automated seizure detection by mobile monitoring devices promises to settle this serious methodological issue. Here, we explored attitudes and preferences towards future devices for seizure detection in adult patients with therapy-refractory epilepsies.
METHODS: A total of 102 inpatients enrolled and underwent a newly constructed semistructured 30-minute interview on automated seizure registration.
RESULTS: Most patients would generally apply and permanently use seizure registration devices. Removable devices were preferred (e.g., wristband sensors), but also patch electrodes at invisible body sites appeared acceptable. Only a minority of patients would accept implantations for seizure registration (especially of depth electrodes). Also, permanent optical or acoustical surveillance were accepted by a few patients only. Most patients were ready to care for the device (e.g., charging battery), to have doctor's appointments for device control, and even to pay for the device. Seizure prediction was evaluated as an essential additional function. Only half of the patients wanted emergency calls in case of a seizure. SIGNIFICANCE: Patients would accept automated seizure registration if the device had as little as possible negative effect on daily living. High acceptance might, therefore, be expected for hardware equipment as it is nowadays used by many healthy subjects for physiological self-monitoring and life-logging. The proper medical engineering task of the future, therefore, is to optimize sensors in those highly feasible devices and to establish reliable biomarkers and outcome measures for a diversity of diseases (including epilepsy) from data obtained by this generic hardware.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Patient survey; Seizure detection; Seizure diary; Seizure frequency; Seizure registration

Mesh:

Year:  2015        PMID: 26366675     DOI: 10.1016/j.yebeh.2015.08.006

Source DB:  PubMed          Journal:  Epilepsy Behav        ISSN: 1525-5050            Impact factor:   2.937


  9 in total

Review 1.  [Mobile seizure monitoring in epilepsy patients].

Authors:  A Schulze-Bonhage; S Böttcher; M Glasstetter; N Epitashvili; E Bruno; M Richardson; K V Laerhoven; M Dümpelmann
Journal:  Nervenarzt       Date:  2019-12       Impact factor: 1.214

2.  Automated real-time detection of tonic-clonic seizures using a wearable EMG device.

Authors:  Sándor Beniczky; Isa Conradsen; Oliver Henning; Martin Fabricius; Peter Wolf
Journal:  Neurology       Date:  2018-01-05       Impact factor: 9.910

Review 3.  Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review.

Authors:  Dongni Johansson; Kristina Malmgren; Margit Alt Murphy
Journal:  J Neurol       Date:  2018-02-09       Impact factor: 4.849

4.  Parental preferences for seizure detection devices: A discrete choice experiment.

Authors:  Anouk van Westrhenen; Ben F M Wijnen; Roland D Thijs
Journal:  Epilepsia       Date:  2022-03-04       Impact factor: 6.740

5.  Individualizing therapies with responsive epilepsy neurostimulation - A mirtazapine case study of hippocampal excitability.

Authors:  Nicole M Warner; Ryder P Gwinn; Michael J Doherty
Journal:  Epilepsy Behav Case Rep       Date:  2016-07-05

6.  Detection of generalized tonic-clonic seizures using surface electromyographic monitoring.

Authors:  Jonathan J Halford; Michael R Sperling; Dileep R Nair; Dennis J Dlugos; William O Tatum; Jay Harvey; Jacqueline A French; John R Pollard; Edward Faught; Katherine H Noe; Thomas R Henry; Gina M Jetter; Octavian V Lie; Lola C Morgan; Michael R Girouard; Damon P Cardenas; Luke E Whitmire; Jose E Cavazos
Journal:  Epilepsia       Date:  2017-10-05       Impact factor: 5.864

7.  Parkinson's disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms - a patient and healthcare professional perspective.

Authors:  Ghayth AlMahadin; Ahmad Lotfi; Eva Zysk; Francesco Luke Siena; Marie Mc Carthy; Philip Breedon
Journal:  BMC Neurol       Date:  2020-11-18       Impact factor: 2.474

Review 8.  Noninvasive detection of focal seizures in ambulatory patients.

Authors:  Philippe Ryvlin; Leila Cammoun; Ilona Hubbard; France Ravey; Sandor Beniczky; David Atienza
Journal:  Epilepsia       Date:  2020-06-02       Impact factor: 5.864

9.  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
  9 in total

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