Accelerated Long Term Forgetting in patients with focal seizures: Incidence rate and contributing factors

Background: Accelerated Long Term Forgetting (ALF) is usually defined as a memory impairment that is seen only at long delays (e.g., after days or weeks) and not at shorter delays (e.g., 30 min) typically used in clinical settings. Research indicates that ALF occurs in some patients with epilepsy, but the incidence rates and underlying causes have not been established. In this study, we considered these issues. Methods: Forty-four patients with a history of focal seizures were tested at 30 min and 7 day delays for material from the Rey Auditory Verbal Learning Test (RAVLT) and Aggie Figures Test. Recently published norms from a matched group of 60 control subjects (Miller et al., 2015 ) were used to determine whether patients demonstrated ALF, impairment at 30 min or intact memory performance. Results: The incidence of ALF in the epilepsy patients (18%) was > 3 times higher than normal on the RAVLT, but no different (7%) from the incidence in normal subjects on the Aggie Figures. A different, but again significantly high, proportion of patients (36%) showed shorter-term memory deficits on at least one task. ALF was found mainly in patients with temporal-lobe epilepsy, but also occurred in one patient with an extratemporal seizure focus. Presence of a hippocampal lesion was the main predicting factor of ALF. Conclusions: Many patients with a focal seizure disorder show memory deficits after longer delays that are not evident on standard assessment. The present study explored the factors associated with this ALF memory profile. These new findings will enhance clinical practice, particularly the management of patients with memory complaints

Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals

Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This could lead to a new breed of closed-loop responsive and personalised feedback, which we describe as Neuromorphic Neuromodulation. This can empower precise and adaptive modulation strategies by integrating neuromorphic AI as tightly as possible to the site of the sensors and stimulators. This paper presents a perspective on the potential of Neuromorphic Neuromodulation, emphasizing its capacity to revolutionize implantable brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page

Return to driving after a diagnosis of epilepsy: A prospective registry study

Summary Objective To determine the frequency and predictors of return to driving within 1 year after a diagnosis of epilepsy. Methods SEISMIC (the Sydney Epilepsy Incidence Study to Measure Illness Consequences) was a prospective, multicenter, community-wide study of people of all ages with newly diagnosed epilepsy in Sydney, Australia. Demographic, socioeconomic, and clinical characteristics and driving status were obtained as soon as possible after baseline registration with a diagnosis of epilepsy. Multivariate logistic regression was used to determine predictors of return to driving at 12-month follow-up. Results Among 181 (76%) adult participants (≥18 years old) who reported driving before an epilepsy diagnosis, 152 provided information on driving at 12 months, of whom 118 (78%) had returned to driving. Driving for reasons of getting to work or place of education (odds ratio [OR] = 4.70, 95% confidence intervals [CI] = 1.87-11.86), no seizure recurrence (OR = 5.15, 95% CI = 2.07-12.82), and being on no or a single antiepileptic drug (OR = 4.54, 95% CI = 1.45-14.22) were associated with return to driving (C statistic = 0.79). More than half of participants with recurrent seizures were driving at follow-up. Significance Early return to driving after a diagnosis of epilepsy is related to work/social imperatives and control of seizures, but many people with recurrent seizures continue to drive. Further efforts are required to implement driving restriction policies and to provide transport options for people with epilepsy

Course and impact of sleep disturbance in newly diagnosed epilepsy: A prospective registry study

Objective To determine the course of sleep distrurbance (insomnia symptoms and short sleep duration) after a diagnosis of epilepsy and their associations with seizure control, mood, disability, and quality of life. Patients and methods One hundred and sixty-nine adults were drawn from the Sydney Epilepsy Incidence Study to Measure Illness Consequences (SEISMIC), a prospective, multicenter, community-wide study in Sydney, Australia. Socio-demographic, psychosocial, clinical characteristics, and information on sleep disturbance were obtained early (median 48 [IQR15-113] days) after a diagnosis of epilepsy, and at 12 months. Logistic regression models were used to determine associations between patterns of sleep disturbance with outcomes at 12 months. Results Insomnia symptoms and/or short sleep duration were present in 18-23% of participants at both time points, with over half (54-61%) showing a chronic pattern. There was no association of sleep disturbance pattern with recurrent seizures, medication use or disability. Chronic insomnia symptoms and short sleep duration were strongly associated with worse mental health (aOR 3.76, 95% CI 1.28-11.06; and aOR 5.41, 95% CI 1.86-15.79) and poorer quality of life at 12 months (aOR 3.02, 95% CI 1.03-8.84; and aOR 3.11, 95% CI 1.10-8.82), after adjusting for clinical features of epilepsy and comorbidity. Those whose sleep disturbance remitted had no adverse outcomes. Conclusions Insomnia symptoms and short sleep duration are less common in people with recently-diagnosed than chronic epilepsy. The temporal association with poor psycholosocial outcomes supports specific interventions addressing sleep disturbance

Determining the role and responsibilities of the Australian epilepsy nurse in the management of epilepsy: a study protocol

Introduction Epilepsy is a common neurological condition affecting between 3% and 3.5% of the Australian population at some point in their lifetime. The effective management of chronic and complex conditions such as epilepsy requires person-centred and coordinated care across sectors, from primary to tertiary healthcare. Internationally, epilepsy nurse specialists are frequently identified as playing a vital role in improving the integration of epilepsy care and enhancing patient self-management. This workforce has not been the focus of research in Australia to date. Methods and analysis This multistage mixed-method study examines the role and responsibilities of epilepsy nurses, particularly in primary and community care settings, across Australia, including through the provision of a nurse helpline service. A nationwide sample of 30 epilepsy nurses will be purposively recruited via advertisements distributed by epilepsy organisations and through word-of-mouth snowball sampling. Two stages (1 and 3) consist of a demographic questionnaire and semistructured interviews (individual or group) with epilepsy nurse participants, with the thematic data analysis from this work informing the areas for focus in stage 3. Stage 2 comprises of a retrospective descriptive analysis of phone call data from Epilepsy Action Australia’s National Epilepsy Line service to identify types of users, their needs and reasons for using the service, and to characterise the range of activities undertaken by the nurse call takers. Ethics and dissemination Ethics approval for this study was granted by Macquarie University (HREC: 52020668117612). Findings of the study will be published through peer-reviewed journal articles and summary reports to key stakeholders, and disseminated through public forums and academic conference presentations. Study findings will also be communicated to people living with epilepsy and families

Determining the role and responsibilities of the community epilepsy nurse in the management of epilepsy

Aims and Objectives: The aim of this study is to enhance the understanding of the core elements and influencing factors on the community‐based epilepsy nurse's role and responsibilities. Background: Internationally, epilepsy nurse specialists play a key role in providing person‐centred care and management of epilepsy but there is a gap in understanding of their role in the community. Design: A national three‐stage, mixed‐method study was conducted. Methods: One‐on‐one, in‐depth semi‐structured qualitative interviews were conducted online with 12 community‐based epilepsy nurses (Stage 1); retrospective analysis of data collected from the National Epilepsy Line, a nurse‐led community helpline (Stage 2); and focus group conducted with four epilepsy nurses, to delve further into emerging findings (Stage 3). A thematic analysis was conducted in Stages 1 and 3, and a descriptive statistical analysis of Stage 2 data. Consolidated Criteria for Reporting Qualitative studies checklist was followed for reporting. Results: Three key themes emerged: (1) The epilepsy nurse career trajectory highlighted a lack of standardised qualifications, competencies, and career opportunities. (2) The key components of the epilepsy nurse role explored role diversity, responsibilities, and models of practice in the management of living with epilepsy, and experiences navigating complex fragmented systems and practices. (3) Shifting work practices detailed the adapting work practices, impacted by changing service demands, including COVID‐19 pandemic experiences, role boundaries, funding, and resource availability. Conclusion: Community epilepsy nurses play a pivotal role in providing holistic, person‐centred epilepsy management They contribute to identifying and addressing service gaps through innovating and implementing change in service design and delivery. Relevance to Clinical Practice: Epilepsy nurses' person‐centred approach to epilepsy management is influenced by the limited investment in epilepsy‐specific integrated care initiatives, and their perceived value is impacted by the lack of national standardisation of their role and scope of practice. No Patient or Public Contribution: Only epilepsy nurses' perspectives were sought

Weak self-supervised learning for seizure forecasting: a feasibility study.

This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system