Tablet-based electroencephalography diagnostics for patients with epilepsy in the West African Republic of Guinea

E = mc2 : Education (E), medication (m), and conditional cash (c2 ) to improve uptake of antiseizure medications in a low-resource population: Protocol for randomized trial

OBJECTIVE

Most people with epilepsy (PWE) could live seizure-free if treated with one or more antiseizure medications (ASMs). The World Health Organization (WHO) estimates that 75% of PWE in low-resource settings lack adequate antiseizure treatment. Limited education surrounding epilepsy and the out-of-pocket costs of ASMs in particular pose barriers to managing epilepsy in resource-poor, low-income settings. The aim of this study is to implement and test a novel strategy to improve outcomes across the epilepsy care cascade marked by (1) retention in epilepsy care, (2) adherence to ASMs, and (3) seizure reduction, with the measured goal of seizure freedom.

METHODS

A randomized, double-blinded clinical trial will be performed, centered at the Ignace Deen Hospital in Conakry, Republic of Guinea, in Western Sub-Saharan Africa. Two hundred people with clinically diagnosed epilepsy, ages 18 years and above, will receive education on epilepsy and then be randomized to (i) free ASMs versus (ii) conditional cash, conditioned upon return to the epilepsy clinic. Participants will be followed for 360 days with study visits every 90 days following enrollment.

SIGNIFICANCE

We design a randomized trial for PWE in Guinea, a low-resource setting with a high proportion of untreated PWE and a nearly completely privatized healthcare system. The trial includes a conditional cash transfer intervention, which has yet to be tested as a targeted means to improve outcomes for people with a chronic neurological disorder. The trial aims to provide an evidence base for the treatment of epilepsy in such settings.

PLAIN LANGUAGE SUMMARY

We present a clinical trial protocol for a randomized, blinded study of 200 people with epilepsy in the low-resource African Republic of Guinea, providing an educational intervention (E), and then randomizing in a 1:1 allocation to either free antiseizure medication (m) or conditional cash (c2 ) for 360 days. Measured outcomes include (1) returning to outpatient epilepsy care, (2) adherence to antiseizure medications (ASMs), and (3) reducing the number of seizures. This study is an initial look at giving small amounts of cash for desired results (or "nudges") for improving epilepsy outcomes in the sub-Saharan African and brain disorder contexts.

Artificial intelligence in epilepsy phenotyping

Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.

HAPPILEE: HAPPE In Low Electrode Electroencephalography, a standardized pre-processing software for lower density recordings

Lower-density Electroencephalography (EEG) recordings (from 1 to approximately 32 electrodes) are widely-used in research and clinical practice and enable scalable brain function measurement across a variety of settings and populations. Though a number of automated pipelines have recently been proposed to standardize and optimize EEG pre-processing for high-density systems with state-of-the-art methods, few solutions have emerged that are compatible with lower-density systems. However, lower-density data often include long recording times and/or large sample sizes that would benefit from similar standardization and automation with contemporary methods. To address this need, we propose the HAPPE In Low Electrode Electroencephalography (HAPPILEE) pipeline as a standardized, automated pipeline optimized for EEG recordings with lower density channel layouts of any size. HAPPILEE processes task-free (e.g., resting-state) and task-related EEG (including event-related potential data by interfacing with the HAPPE+ER pipeline), from raw files through a series of processing steps including filtering, line noise reduction, bad channel detection, artifact correction from continuous data, segmentation, and bad segment rejection that have all been optimized for lower density data. HAPPILEE also includes post-processing reports of data and pipeline quality metrics to facilitate the evaluation and reporting of data quality and processing-related changes to the data in a standardized manner. Here the HAPPILEE steps and their optimization with both recorded and simulated EEG data are described. HAPPILEE's performance is then compared relative to other artifact correction and rejection strategies. The HAPPILEE pipeline is freely available as part of HAPPE 2.0 software under the terms of the GNU General Public License at: https://github.com/PINE-Lab/HAPPE.

Use of Artificial Intelligence in Clinical Neurology

Artificial intelligence is already innovating in the provision of neurologic care. This review explores key artificial intelligence concepts; their application to neurologic diagnosis, prognosis, and treatment; and challenges that await their broader adoption. The development of new diagnostic biomarkers, individualization of prognostic information, and improved access to treatment are among the plethora of possibilities. These advances, however, reflect only the tip of the iceberg for the ways in which artificial intelligence may transform neurologic care in the future.

Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review

In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

Utilization of epilepsy surgery in the United States: A study of the National Inpatient Sample investigating the roles of race, socioeconomic status, and insurance

Background:

Epilepsy is estimated to affect 70 million people worldwide and is medically refractory in 30% of cases.

Methods:

This is a retrospective cross-sectional study using a US database from 2012 to 2014 to identify patients aged ≥18 years admitted to the hospital with epilepsy as the primary diagnosis. The sampled population was weighted using Healthcare Cost and Utilization Project guidelines. Procedural ICD-9 codes were utilized to stratify the sampled population into two cohorts: resective surgery and implantation or stimulation procedure.

Results:

Query of the database yielded 152,925 inpatients, of which 8535 patients underwent surgical intervention. The nonprocedural group consisted of 76,000 White patients (52.6%) and 28,390 Black patients (19.7%) while the procedural group comprised 5550 White patients (64%) and 730 Black patients (8.6%) (P < 0.001). Patients with Medicare were half as likely to receive a surgical procedure (14.8% vs. 28.4%) while patients with private insurance were twice as likely to receive a procedure (53.4% vs. 29.3%), both were statistically significant (P < 0.01). Those in the lowest median household income quartile by zip code (<$40,000) were 68% less likely to receive a procedure (21.5% vs. 31.4%) while the highest income quartile was 133% more likely to receive a procedure (26.1% vs. 19.5%). Patients from rural and urban nonteaching hospitals were, by a wide margin, less likely to receive a surgical procedure.

Conclusion:

We demonstrate an area of need and significant improvement at institutions that have the resources and capability to perform epilepsy surgery. The data show that institutions may not be performing enough epilepsy surgery as a result of racial and socioeconomic bias. Admissions for epilepsy continue to increase without a similar trend for epilepsy surgery despite its documented effectiveness. Race, socioeconomic status, and insurance all represent significant barriers in access to epilepsy surgery. The barriers can be remedied by improving referral patterns and implementing cost-effective measures to improve inpatient epilepsy services in rural and nonteaching hospitals.

The Recent Progress and Applications of Digital Technologies in Healthcare: A Review

BACKGROUND

The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects.

PURPOSE

The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, "big data," telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.