Improving nocturnal event monitoring in people with intellectual disability in community using an artificial intelligence camera

Epilepsy professionals' views on sudden unexpected death in epilepsy counselling: A tale of two countries

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.

Digital measures in epilepsy in low-resourced environments

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.

Deep learning approaches for seizure video analysis: A review

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.

Computer vision for automated seizure detection and classification: A systematic review

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.

Neurotechnological solutions for post-traumatic stress disorder: A perspective review and concept proposal

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.