At home EEG monitoring technologies for people with epilepsy and intellectual disabilities: A scoping review

Relationship between anti-seizure medication and behaviors that challenge in older persons with intellectual disability and epilepsy: a review

INTRODUCTION

There is increased focus on the negative impact of the overprescribing of medication, specifically psychotropic medication, including anti-seizure medications (ASM), in people with Intellectual Disability (ID). This is particularly important for the older adult population, where multi-morbidity and polypharmacy are more common. ASMs are associated with psychiatric and behavioral adverse effects. Furthermore, there is growing awareness of the anticholinergic burden for older adults with epilepsy and ID and the relationship with behaviors that challenge (BtC).

AREAS COVERED

This review defines the older adult population and outlines the relationship between epilepsy and ID. BtC is outlined in the context of the population and the relationship with ASMs. The evidence base to guide prescribing and de-prescribing for newer ASMs is also presented, including pragmatic data.

EXPERT OPINION

Polypharmacy, particularly psychotropics, are a mortality risk factor for older adults with epilepsy and ID. Therefore, any BtC requires a holistic assessment with a multi-disciplinary approach. This includes specific consideration of all prescribed medicines in the context of polypharmacy. There should be routine reviews, at least annually, for those aged 40 years and over particularly focused on anticholinergic burden and/or polypharmacy.

Outcome Measures in intellectual disability: A Review and narrative synthesis of validated instruments

BACKGROUND

Outcome measurement is essential to determine the effectiveness of health interventions and improve the quality of services. The interplay of social, individual, and biological factors makes this a complex process in the psychiatry of people with intellectual disability (PwID).

AIM

Review of outcome measures which are validated in PwID.

METHODS

A PRISMA-guided review was conducted, using a predefined criteria and a relevant word combination on four databases: EMBASE, Medline, CINAHL and PsycINFO. Each included study was examined for relevance to intellectual disability psychiatry. The psychometric data of each tool was critically assessed. Findings were narratively synthesised.

RESULTS

Of 1,548 articles, 35 met the inclusion criteria. Several outcome measures were identified relevant to intellectual disability psychiatry, including tools for challenging/offending behavior, specific neurodevelopmental/clinical conditions such as ADHD, epilepsy, and dementia however, psychometric properties, validity and reliability varied considerably. The tools identified were largely clinician rated, with a dearth of measures suitable for completion by patients or their family carers.

CONCLUSION

Most outcome measures used for PwID lack suitable psychometric properties including validity or reliability for use within the ID population. Of importance, those with alternative expression or are non-verbal have been excluded from the research developing and reporting on measurement instruments. There is an underserved population who risk being left behind in the era of value-based medicine and increasing use of outcome measurement when assessing the effectiveness of healthcare interventions on individual and population levels. This is the first of its kind review in this area.

HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC's implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants' homes. Our results demonstrate HEROIC's capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.

Early discontinuation of ambulatory vEEG among individuals with intellectual disabilities: A retrospective chart review

OBJECTIVE

This retrospective chart review aims to quantify the rate of patients with intellectual disability (ID) accessing an Australian ambulatory EEG service, and understand the clinical implications of discontinuing studies prematurely.

METHODS

Electronic records of referrals, patient monitoring notes, and EEG reports were accessed retrospectively. Each referral was assessed to determine whether the patient had an ID. For each study where patients were discharged prematurely, the outcomes of their EEG report were assessed and compared between the ID and non-ID groups. Exploratory analysis was performed assessing the effects of age, the percentage of the requested monitoring undertaken, and outcome rates as a function of monitoring duration.

RESULTS

There were significantly more patients in the ID group with early disconnection than the non-ID group (Chi squared test, p = 0.000). There was no significant difference in the rates of clinical outcomes between the ID and non-ID groups amongst patients who disconnected early.

CONCLUSIONS

Although rates of early disconnection are higher in those with ID, study outcomes are largely similar between patients with and without ID in this retrospective analysis of an ambulatory EEG service.

SIGNIFICANCE

Ambulatory EEG is a viable modality of EEG monitoring for patients with ID.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

A sustainable artificial-intelligence-augmented digital care pathway for epilepsy: Automating seizure tracking based on electroencephalogram data using artificial intelligence

Objective

Scalp electroencephalograms (EEGs) are critical for neurological evaluations, particularly in epilepsy, yet they demand specialized expertise that is often lacking in many regions. Artificial intelligence (AI) offers potential solutions to this gap. While existing AI models address certain aspects of EEG analysis, a fully automated system for routine EEG interpretation is required for effective epilepsy management and healthcare professionals' decision-making. This study aims to develop an AI-augmented model for automating EEG seizure tracking, thereby supporting a sustainable digital care pathway for epilepsy (DCPE). The goal is to improve patient monitoring, facilitate collaborative decision-making, ensure timely medication adherence, and promote patient compliance.

Method

The study proposes an AI-augmented framework using machine learning, focusing on quantitative analysis of EEG data to automate DCPE. A focus group discussion was conducted with healthcare professionals to find the problem of the current digital care pathway and assess the feasibility, usability, and sustainability of the AI-augmented system in the digital care pathway.

Results

The study found that a combination of random forest with principal component analysis and support vector machines with KBest feature selection achieved high accuracy rates of 96.52% and 95.28%, respectively. Additionally, the convolutional neural networks model outperformed other deep learning algorithms with an accuracy of 97.65%. The focus group discussion revealed that automating the diagnostic process in digital care pathway could reduce the time needed to diagnose epilepsy. However, the sustainability of the AI-integrated framework depends on factors such as technological infrastructure, skilled personnel, training programs, patient digital literacy, financial resources, and regulatory compliance.

Conclusion

The proposed AI-augmented system could enhance epilepsy management by optimizing seizure tracking accuracy, improving monitoring and timely interventions, facilitating collaborative decision-making, and promoting patient-centered care, thereby making the digital care pathway more sustainable.