Literature DB >> 32385909

Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers?

Elisa Bruno1, Pedro F Viana1,2,3, Michael R Sperling4, Mark P Richardson1.   

Abstract

In patients with epilepsy, the potential to prevent seizure-related injuries and to improve the unreliability of seizure self-report have fostered the development and marketing of numerous seizure detection devices for home use. Understanding the requirements of users (patients and caregivers) is essential to improve adherence and mitigate barriers to the long-term use of such devices. Here we reviewed the evidence on the needs and preferences of users and provided an overview of currently marketed devices for seizure detection (medically approved or with published evidence for their performance). We then compared devices with known needs. Seizure-detection devices are expected to improve safety and clinical and self-management, and to provide reassurance to users. Key factors affecting a device's usability relate to its design (attractive appearance, low visibility, low intrusiveness), comfort of use, confidentiality of recorded data, and timely support from both technical and clinical ends. High detection sensitivity and low false alarm rates are paramount. Currently marketed devices are focused primarily on the recording of non-electroencephalography (EEG) signals associated with tonic-clonic seizures, whereas the detection of focal seizures without major motor features remains a clear evidence gap. Moreover, there is paucity of evidence coming from real-life settings. A joint effort of clinical and nonclinical experts, patients, and caregivers is required to ensure an optimal level of acceptability and usability, which are key aspects for a successful continuous monitoring aimed at seizure detection at home.
© 2020 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.

Entities:  

Keywords:  acceptability; epilepsy; mHealth; seizure detection; usability; wearables

Mesh:

Year:  2020        PMID: 32385909     DOI: 10.1111/epi.16521

Source DB:  PubMed          Journal:  Epilepsia        ISSN: 0013-9580            Impact factor:   5.864


  11 in total

1.  Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models.

Authors:  Pedro F Viana; Tal Pal Attia; Mona Nasseri; Jonas Duun-Henriksen; Andrea Biondi; Joel S Winston; Isabel Pavão Martins; Ewan S Nurse; Matthias Dümpelmann; Andreas Schulze-Bonhage; Dean R Freestone; Troels W Kjaer; Mark P Richardson; Benjamin H Brinkmann
Journal:  Epilepsia       Date:  2022-04-08       Impact factor: 6.740

2.  Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients.

Authors:  Sebastian Böttcher; Elisa Bruno; Nino Epitashvili; Matthias Dümpelmann; Nicolas Zabler; Martin Glasstetter; Valentina Ticcinelli; Sarah Thorpe; Simon Lees; Kristof Van Laerhoven; Mark P Richardson; Andreas Schulze-Bonhage
Journal:  Sensors (Basel)       Date:  2022-04-26       Impact factor: 3.847

3.  Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study.

Authors:  Andrea Biondi; Petroula Laiou; Elisa Bruno; Pedro F Viana; Martijn Schreuder; William Hart; Ewan Nurse; Deb K Pal; Mark P Richardson
Journal:  JMIR Res Protoc       Date:  2021-03-19

4.  The Individual Ictal Fingerprint: Combining Movement Measures With Ultra Long-Term Subcutaneous EEG in People With Epilepsy.

Authors:  Troels W Kjaer; Line S Remvig; Asbjoern W Helge; Jonas Duun-Henriksen
Journal:  Front Neurol       Date:  2021-12-23       Impact factor: 4.003

5.  Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning.

Authors:  Mona Nasseri; Tal Pal Attia; Boney Joseph; Nicholas M Gregg; Ewan S Nurse; Pedro F Viana; Gregory Worrell; Matthias Dümpelmann; Mark P Richardson; Dean R Freestone; Benjamin H Brinkmann
Journal:  Sci Rep       Date:  2021-11-09       Impact factor: 4.379

6.  A Patient Perspective on Seizure Detection and Forecasting.

Authors:  Aria Moss; Evan Moss; Robert Moss; Lisa Moss; Sharon Chiang; Peter Crino
Journal:  Front Neurol       Date:  2022-02-11       Impact factor: 4.003

7.  Expert Perspective: Who May Benefit Most From the New Ultra Long-Term Subcutaneous EEG Monitoring?

Authors:  Jay Pathmanathan; Troels W Kjaer; Andrew J Cole; Norman Delanty; Rainer Surges; Jonas Duun-Henriksen
Journal:  Front Neurol       Date:  2022-01-20       Impact factor: 4.003

8.  Remote Electroencephalography Monitoring of Epilepsy in Adults: Protocol for a Scoping Review.

Authors:  Madison Milne-Ives; Rohit Shankar; Brendan McLean; Jonas Duun-Henriksen; Lykke Blaabjerg; Edward Meinert
Journal:  JMIR Res Protoc       Date:  2022-02-25

9.  Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method.

Authors:  Swagata Devi; Koushik Guha; Olga Jakšić; Krishna Lal Baishnab; Zoran Jakšić
Journal:  Micromachines (Basel)       Date:  2022-07-14       Impact factor: 3.523

10.  Informing the Development of a Digital Health Platform Through Universal Points of Care: Qualitative Survey Study.

Authors:  Michael P Craven; Jacob A Andrews; Alexandra R Lang; Sara K Simblett; Stuart Bruce; Sarah Thorpe; Til Wykes; Richard Morriss; Chris Hollis
Journal:  JMIR Form Res       Date:  2020-11-26
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