학술논문
Automated spike and seizure detection: Are we ready for implementation?
Document Type
Academic Journal
Author
Reus EEM; Stichting Epilepsie Instellingen Nederland (SEIN). Electronic address: ereus@sein.n.; Visser GH; Stichting Epilepsie Instellingen Nederland (SEIN).; Sommers-Spijkerman MPJ; Department of Rehabilitation, Physical Therapy Science and Sports, University Medical Center Utrecht, the Netherlands.; van Dijk JG; Department of Neurology, Leiden University Medical Centre, Leiden, the Netherlands.; Cox FME; Stichting Epilepsie Instellingen Nederland (SEIN).
Source
Publisher: Elsevier Country of Publication: England NLM ID: 9306979 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-2688 (Electronic) Linking ISSN: 10591311 NLM ISO Abbreviation: Seizure Subsets: MEDLINE
Subject
Language
English
Abstract
Objective: Automated detection of spikes and seizures has been a subject of research for several decades now. There have been important advances, yet automated detection in EMU (Epilepsy Monitoring Unit) settings has not been accepted as standard practice. We intend to implement this software at our EMU and so carried out a qualitative study to identify factors that hinder ('barriers') and facilitate ('enablers') implementation.
Method: Twenty-two semi-structured interviews were conducted with 14 technicians and neurologists involved in recording and reporting EEGs and eight neurologists who receive EEG reports in the outpatient department. The study was reported according to the Consolidated Criteria for Reporting Qualitative Studies (COREQ).
Results: We identified 14 barriers and 14 enablers for future implementation. Most barriers were reported by technicians. The most prominent barrier was lack of trust in the software, especially regarding seizure detection and false positive results. Additionally, technicians feared losing their EEG review skills or their jobs. Most commonly reported enablers included potential efficiency in the EEG workflow, the opportunity for quantification of EEG findings and the willingness to try the software.
Conclusions: This study provides insight into the perspectives of users and offers recommendations for implementing automated spike and seizure detection in EMUs.
Competing Interests: Declaration of competing interest None of the authors has any conflict of interest to disclose.
(Copyright © 2023. Published by Elsevier Ltd.)
Method: Twenty-two semi-structured interviews were conducted with 14 technicians and neurologists involved in recording and reporting EEGs and eight neurologists who receive EEG reports in the outpatient department. The study was reported according to the Consolidated Criteria for Reporting Qualitative Studies (COREQ).
Results: We identified 14 barriers and 14 enablers for future implementation. Most barriers were reported by technicians. The most prominent barrier was lack of trust in the software, especially regarding seizure detection and false positive results. Additionally, technicians feared losing their EEG review skills or their jobs. Most commonly reported enablers included potential efficiency in the EEG workflow, the opportunity for quantification of EEG findings and the willingness to try the software.
Conclusions: This study provides insight into the perspectives of users and offers recommendations for implementing automated spike and seizure detection in EMUs.
Competing Interests: Declaration of competing interest None of the authors has any conflict of interest to disclose.
(Copyright © 2023. Published by Elsevier Ltd.)