Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/34229
Title: EEG based automated seizure detection - A survey of medical professionals.
Austin Authors: Wong, Sheng;Simmons, Anj;Rivera-Villicana, Jessica;Barnett, Scott;Sivathamboo, Shobi;Perucca, Piero ;Kwan, Patrick;Kuhlmann, Levin;Vasa, Rajesh;O'Brien, Terence J
Affiliation: Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia.
Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.
Epilepsy Research Centre
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.
Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia;
Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia; Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.
Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia.
Issue Date: 10-Nov-2023
Date: 2023
Publication information: Epilepsy & Behavior: E&B 2023-11-10; 149
Abstract: Diagnosing and managing seizures presents substantial challenges for clinicians caring for patients with epilepsy. Although machine learning (ML) has been proposed for automated seizure detection using EEG data, there is little evidence of these technologies being broadly adopted in clinical practice. Moreover, there is a noticeable lack of surveys investigating this topic from the perspective of medical practitioners, which limits the understanding of the obstacles for the development of effective automated seizure detection. Besides the issue of generalisability and replicability seen in a small amount of studies, obstacles to the adoption of automated seizure detection remain largely unknown. To understand the obstacles preventing the application of seizure detection tools in clinical practice, we conducted a survey targeting medical professionals involved in the management of epilepsy. Our study aimed to gather insights on various factors such as the clinical utility, professional sentiment, benchmark requirements, and perceived barriers associated with the use of automated seizure detection tools. Our key findings are: I) The minimum acceptable sensitivity reported by most of our respondents (80%) seems achievable based on studies reported from most currently available ML-based EEG seizure detection algorithms, but replication studies often fail to meet this minimum. II) Respondents are receptive to the adoption of ML seizure detection tools and willing to spend time in training. III) The top three barriers for usage of such tools in clinical practice are related to availability, lack of training, and the blackbox nature of ML algorithms. Based on our findings, we developed a guide that can serve as a basis for developing ML-based seizure detection tools that meet the requirements of medical professionals, and foster the integration of these tools into clinical practice.
URI: https://ahro.austin.org.au/austinjspui/handle/1/34229
DOI: 10.1016/j.yebeh.2023.109518
ORCID: 
Journal: Epilepsy & Behavior : E&B
Start page: 109518
PubMed URL: 37952416
ISSN: 1525-5069
Type: Journal Article
Subjects: EEG
Epilepsy
Machine Learning
Questionnaires
Seizure detections
Appears in Collections:Journal articles

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