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Ferreira J, Peixoto R, Lopes L, Beniczky S, Ryvlin P, Conde C, Claro J. User involvement in the design and development of medical devices in epilepsy: A systematic review. Epilepsia Open 2024. [PMID: 39324505 DOI: 10.1002/epi4.13038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 07/27/2024] [Accepted: 08/13/2024] [Indexed: 09/27/2024] Open
Abstract
OBJECTIVE This systematic review aims to describe the involvement of persons with epilepsy (PWE), healthcare professionals (HP) and caregivers (CG) in the design and development of medical devices is epilepsy. METHODS A systematic review was conducted, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligibility criteria included peer-reviewed research focusing on medical devices for epilepsy management, involving users (PWE, CG, and HP) during the MDD process. Searches were performed on PubMed, Web of Science, and Scopus, and a total of 55 relevant articles were identified and reviewed. RESULTS From 1999 to 2023, there was a gradual increase in the number of publications related to user involvement in epilepsy medical device development (MDD), highlighting the growing interest in this field. The medical devices involved in these studies encompassed a range of seizure detection tools, healthcare information systems, vagus nerve stimulation (VNS) and electroencephalogram (EEG) technologies reflecting the emphasis on seizure detection, prediction, and prevention. PWE and CG were the primary users involved, underscoring the importance of their perspectives. Surveys, usability testing, interviews, and focus groups were the methods used for capturing user perspectives. User involvement occurs in four out of the five stages of MDD, with production being the exception. SIGNIFICANCE User involvement in the MDD process for epilepsy management is an emerging area of interest holding a significant promise for improving device quality and patient outcomes. This review highlights the need for broader and more effective user involvement, as it currently lags in the development of commercially available medical devices for epilepsy management. Future research should explore the benefits and barriers of user involvement to enhance medical device technologies for epilepsy. PLAIN LANGUAGE SUMMARY This review covers studies that have involved users in the development process of medical devices for epilepsy. The studies reported here have focused on getting input from people with epilepsy, their caregivers, and healthcare providers. These devices include tools for detecting seizures, stimulating nerves, and tracking brain activity. Most user feedback was gathered through surveys, usability tests, interviews, and focus groups. Users were involved in nearly every stage of device development except production. The review highlights that involving users can improve device quality and patient outcomes, but more effective involvement is needed in commercial device development. Future research should focus on the benefits and challenges of user involvement.
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Affiliation(s)
- João Ferreira
- Faculty of Engineering, University of Porto, Porto, Portugal
- Biostrike Unipessoal Lda, Porto, Portugal
| | - Ricardo Peixoto
- Faculty of Engineering, University of Porto, Porto, Portugal
- Biostrike Unipessoal Lda, Porto, Portugal
| | - Lígia Lopes
- Faculty of Engineering, University of Porto, Porto, Portugal
- FBAUP-Faculty of Fine Arts, University of Porto, Porto, Portugal
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | - Carlos Conde
- i3S, Instituto de Investigação e Inovação Em Saúde, University of Porto, Porto, Portugal
- School of Medicine and Biomedical Sciences, University of Porto, Porto, Portugal
- Institute for Molecular and Cell Biology, University of Porto, Porto, Portugal
| | - João Claro
- Faculty of Engineering, University of Porto, Porto, Portugal
- INESC TEC, Porto, Portugal
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Watkins L, Henning O, Bassett P, Ashby S, Tromans S, Shankar R. Epilepsy professionals' views on sudden unexpected death in epilepsy counselling: A tale of two countries. Eur J Neurol 2024; 31:e16375. [PMID: 38837829 PMCID: PMC11295158 DOI: 10.1111/ene.16375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/30/2024] [Accepted: 05/16/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND AND PURPOSE Sudden unexpected death in epilepsy (SUDEP) is a leading cause of epilepsy mortality. All international guidance strongly advocates for clinicians working with people with epilepsy (PWE) to discuss SUDEP. Clinician views working with PWE in the UK and Norway on SUDEP counselling are compared. METHODS A cross-sectional online mixed methodology survey of 17 Likert and free-text response questions using validated themes was circulated via International League against Epilepsy/Epilepsy Specialist Nurses Association in the UK and International League against Epilepsy/Epilepsinet in Norway using a non-discriminatory exponential snowballing technique leading to non-probability sampling. Quantitative data were analysed using descriptive statistics and Mann-Whitney, Kruskal-Wallis, chi-squared and Fisher's exact tests. Significance was accepted at p < 0.05. Thematic analysis was conducted on free-text responses. RESULTS Of 309 (UK 197, Norway 112) responses, UK clinicians were more likely to have experienced an SUDEP (p < 0.001), put greater importance on SUDEP communication (p < 0.001), discuss SUDEP with all PWE particularly new patients (p < 0.001), have access and refer to bereavement support (p < 0.001) and were less likely to never discuss SUDEP (p < 0.001). Significant differences existed between both countries' neurologists and nurses in SUDEP counselling with UK clinicians generally being more supportive. UK responders were more likely to be able to identify bereavement support (p < 0.001). Thematic analysis highlighted four shared themes and two specific to Norwegians. DISCUSSION Despite all international guidelines stating the need/importance to discuss SUDEP with all PWE there remain hesitation, avoidance and subjectivity in clinicians having SUDEP-related conversations, more so in Norway than the UK. Training and education are required to improve communication, engagement and decision making.
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Affiliation(s)
- Lance Watkins
- University of South WalesPontypriddUK
- Swansea Bay University Health BoardPort TalbotUK
- Cornwall Intellectual Disability Equitable Research (CIDER)University of Plymouth Peninsula School of MedicineTruroUK
| | - Oliver Henning
- National Epilepsy CenterOslo University HospitalOsloNorway
| | | | | | - Samuel Tromans
- SAPPHIRE Group, Department of Population Health SciencesUniversity of LeicesterLeicesterUK
- Adult Learning Disability ServiceLeicestershire Partnership NHS TrustLeicesterUK
| | - Rohit Shankar
- Cornwall Intellectual Disability Equitable Research (CIDER)University of Plymouth Peninsula School of MedicineTruroUK
- Cornwall Intellectual Disability Equitable Research (CIDER)Cornwall Partnership NHS Foundation TrustTruroUK
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Ali A, Clarke DF. Digital measures in epilepsy in low-resourced environments. Expert Rev Pharmacoecon Outcomes Res 2024; 24:705-712. [PMID: 37818647 DOI: 10.1080/14737167.2023.2270163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/09/2023] [Indexed: 10/12/2023]
Abstract
INTRODUCTION Digital measures and digital health-care delivery have been rarely implemented in lower-and-middle-income countries (LMICs), contributing to worsening global disparities and inequities. Sustainable ways to implement and use digital approaches will help to improve time to access, management, and quality of life in persons with epilepsy, goals that remain unreachable in under-resourced communities. As under-resourced environments differ in human and economic resources, no one approach will be appropriate to all LMICs. AREAS COVERED Digital health and tools to monitor and measure digital endpoints and metrics of quality of life will need to be developed or adapted to the specific needs of under-resourced areas. Portable technologies may partially address the urban-rural divide. Careful delineation of stakeholders and their engagement and alignment in all efforts is critically important if these initiatives are to be successfully sustained. Privacy issues, neglected in many regions globally, must be purposefully addressed. EXPERT OPINION Epilepsy care in under-resourced environments has been limited by the lack of relevant technologies for diagnosis and treatment. Digital biomarkers, and investigative technological advances, may finally make it feasible to sustainably improve care delivery and ultimately quality of life including personalized epilepsy care.
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Affiliation(s)
- Amza Ali
- Department of Medicine, Faculty of Medical Sciences, Mona, Kingston, Jamaica
| | - Dave F Clarke
- Dell Medical School, University of Texas at Austin, Austin, TX, USA
- Department of Pediatric Epilepsy, Dell Children's Medical Center of Central Texas, Austin, TX, USA
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Ahmedt-Aristizabal D, Armin MA, Hayder Z, Garcia-Cairasco N, Petersson L, Fookes C, Denman S, McGonigal A. Deep learning approaches for seizure video analysis: A review. Epilepsy Behav 2024; 154:109735. [PMID: 38522192 DOI: 10.1016/j.yebeh.2024.109735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/06/2024] [Accepted: 03/03/2024] [Indexed: 03/26/2024]
Abstract
Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.
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Affiliation(s)
- David Ahmedt-Aristizabal
- Imaging and Computer Vision Group, CSIRO Data61, Australia; SAIVT Laboratory, Queensland University of Technology, Australia.
| | | | - Zeeshan Hayder
- Imaging and Computer Vision Group, CSIRO Data61, Australia.
| | - Norberto Garcia-Cairasco
- Physiology Department and Neuroscience and Behavioral Sciences Department, Ribeirão Preto Medical School, University of São Paulo, Brazil.
| | - Lars Petersson
- Imaging and Computer Vision Group, CSIRO Data61, Australia.
| | - Clinton Fookes
- SAIVT Laboratory, Queensland University of Technology, Australia.
| | - Simon Denman
- SAIVT Laboratory, Queensland University of Technology, Australia.
| | - Aileen McGonigal
- Neurosciences Centre, Mater Hospital, Australia; Queensland Brain Institute, The University of Queensland, Australia.
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Brown BM, Boyne AMH, Hassan AM, Allam AK, Cotton RJ, Haneef Z. Computer vision for automated seizure detection and classification: A systematic review. Epilepsia 2024; 65:1176-1202. [PMID: 38426252 DOI: 10.1111/epi.17926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.
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Affiliation(s)
- Brandon M Brown
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - Aidan M H Boyne
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - Adel M Hassan
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - Anthony K Allam
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - R James Cotton
- Shirley Ryan Ability Lab, Chicago, Illinois, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, USA
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
- Neurology Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, USA
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Laugharne R, Farid M, James C, Dutta A, Mould C, Molten N, Laugharne J, Shankar R. Neurotechnological solutions for post-traumatic stress disorder: A perspective review and concept proposal. Healthc Technol Lett 2023; 10:133-138. [PMID: 38111800 PMCID: PMC10725721 DOI: 10.1049/htl2.12055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 12/20/2023] Open
Abstract
Post-traumatic stress disorder (PTSD) is an anxiety condition caused by exposure to severe trauma. It is characterised by nightmares, flashbacks, hyper-vigilance and avoidance behaviour. These all lead to impaired functioning reducing quality of life. PTSD affects 2-5% of the population globally. Most sufferers cannot access effective treatment, leading to impaired psychological functioning reducing quality of life. Eye movement desensitisation and reprocessing (EMDR) is a non-invasive brain stimulation treatment that has shown significant clinical effectiveness in PTSD. Another treatment modality, that is, trauma-focused cognitive behavioural therapy is also an effective intervention. However, both evidence-based treatments are significantly resource intensive as they need trained therapists to deliver them. A concept of a neuro-digital tool for development is proposed to put to clinical practice of delivering EMDR to improve availability, efficiency and effectiveness of treatment. The evidence in using new technologies to measure sleep, geolocation and conversational analysis of social media to report objective outcome measures is explored. If achieved, this can be fed back to users with data anonymously collated to evaluate and improve the tool. Coproduction would be at the heart of product development so that the tool is acceptable and accessible to people with the condition.
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Affiliation(s)
- Richard Laugharne
- Psychoanalytica Community Interest CompanySt GermanUK
- Cornwall Intellectual Disability Equitable ResearchUniversity of Plymouth and Cornwall Partnership NHS Foundation TrustTruroUK
| | - Mohsen Farid
- Data Science Research CentreUniversity of DerbyDerbyUK
| | | | - Anirban Dutta
- Biomedical Engineering DepartmentUniversity of LincolnLincolnUK
| | | | | | | | - Rohit Shankar
- Psychoanalytica Community Interest CompanySt GermanUK
- Cornwall Intellectual Disability Equitable ResearchUniversity of Plymouth and Cornwall Partnership NHS Foundation TrustTruroUK
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