Water-soluble adhesive for stable long-term ambulatory EEG recordings

Economic aspects of prolonged home video-EEG monitoring: a simulation study

INTRODUCTION

Video EEG monitoring (VEM) is an important tool for characterizing clinical events suspected as seizures. It is also used for pre-surgical workups in patients with drug-resistant epilepsy (DRE). In-hospital VEM high cost, long admission waiting periods and some other inconveniences led to an interest in home VEM (HVEM). However, because antiseizure medications cannot be reduced at home, HVEM may require longer monitoring. While the economic aspect is one of the main motivations for HVEM, the cost of HVEM lasting several weeks has not been assessed.

METHODS

We modeled the cost of HVEM for 8 weeks and compared it to the cost of 1-week in-hospital VEM. Additionally, we modeled the per-patient cost for a combination of HVEM and in-hospital VEM, considering that if in a proportion of patients HVEM fails to achieve its goal, they should undergo in-hospital VEM with drug reduction.

RESULTS

The average cost of HVEM up to 4-6 weeks of monitoring was lower than that for the 1-week in-hospital VEM. Combining the 3-week HVEM with 1-week in-hospital VEM (if needed) reduced the per-patient cost by 6.6-28.6% as compared to the situation when all the patients with DRE were referred to the in-hospital VEM.

CONCLUSIONS

A prolonged intermittent HVEM can be cost-effective, especially if the minimal seizure frequency is about one seizure per week. The study findings support directing efforts into clinical trials and technology development.

Improving epilepsy diagnosis across the lifespan: approaches and innovations

Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.

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.

Bioadhesive Technology Platforms

Bioadhesives have emerged as transformative and versatile tools in healthcare, offering the ability to attach tissues with ease and minimal damage. These materials present numerous opportunities for tissue repair and biomedical device integration, creating a broad landscape of applications that have captivated clinical and scientific interest alike. However, fully unlocking their potential requires multifaceted design strategies involving optimal adhesion, suitable biological interactions, and efficient signal communication. In this Review, we delve into these pivotal aspects of bioadhesive design, highlight the latest advances in their biomedical applications, and identify potential opportunities that lie ahead for bioadhesives as multifunctional technology platforms.

The unforeseen future: Impacts of the COVID-19 pandemic on home video-EEG telemetry

The coronavirus disease 2019 (COVID-19) pandemic had widespread impact on health care systems globally-particularly services arranged around elective admission and attendance such as epilepsy monitoring units and home video-EEG telemetry (HVET). Here, we review the ongoing impacts of the pandemic on HVET services among several different providers who used different initial models of HVET. We discuss the features of HVET that led to success in providing continued diagnostic services to patients with epilepsy and related disorders and through retrospective audit of our services demonstrate the high diagnostic yield of HVET. We reflect on this unforeseen future and its implications for other diagnostic techniques and approaches.

Ambulatory video EEG extended to 10 days: A retrospective review of a large database of ictal events

OBJECTIVE

This work aims to determine the ambulatory video electroencephalography monitoring (AVEM) duration and number of captured seizures required to resolve different clinical questions, using a retrospective review of ictal recordings.

METHODS

Patients who underwent home-based AVEM had event data analyzed retrospectively. Studies were grouped by clinical indication: differential diagnosis, seizure type classification, or treatment assessment. The proportion of studies where the conclusion was changed after the first seizure was determined, as was the AVEM duration needed for at least 99% of studies to reach a diagnostic conclusion.

RESULTS

The referring clinical question was not answered entirely by the first event in 29.6% (n = 227) of studies. Diagnostic and classification indications required a minimum of 7 days for at least 99% of studies to be answered, whilst treatment-assessment required at least 6 days.

CONCLUSIONS

At least 7 days of monitoring, and potentially multiple events, are required to adequately answer these clinical questions in at least 99% of patients. The widely applied 72 h or single event recording cut-offs may be inadequate to adequately answer these three indications in a substantial proportion of patients.

SIGNIFICANCE

Extended duration of monitoring and capturing multiple events should be considered when attempting to capture seizures on video-EEG.

Out-of-hospital multimodal seizure detection: a pilot study

Background

Out-of-hospital seizure detection aims to provide clinicians and patients with objective seizure documentation in efforts to improve the clinical management of epilepsy. In-patient studies have found that combining different modalities helps improve the seizure detection accuracy. In this study, the objective was to evaluate the viability of out-of-hospital seizure detection using wearable ECG, accelerometry and behind-the-ear electroencephalography (EEG). Furthermore, we examined the signal quality of out-of-hospital EEG recordings.

Methods

Seventeen patients were monitored for up to 5 days. A support vector machine based seizure detection algorithm was applied using both in-patient seizures and out-of-hospital electrographic seizures in one patient. To assess the content of noise in the EEG signal, we compared the root-mean-square (RMS) of the recordings to a reference threshold derived from manually categorised segments of EEG recordings.

Results

In total 1427 hours of continuous EEG was recorded. In one patient, we identified 15 electrographic focal impaired awareness seizures with a motor component. After training our algorithm on in-patient data, we found a sensitivity of 91% and a false alarm rate (FAR) of 18/24 hours for the detection of out-of-hospital seizures using a combination of EEG and ECG recordings. We estimated that 30.1% of the recorded EEG signal was physiological EEG, with an RMS value within the reference threshold.

Conclusion

We found that detection of out-of-hospital focal impaired awareness seizures with a motor component is possible and that applying multiple modalities improves the diagnostic accuracy compared with unimodal EEG. However, significant challenges remain regarding a high FAR and that only 30.1% of the EEG data represented usable signal.

Rates of event capture of home video EEG

OBJECTIVE

Recording electrographic and behavioral information during epileptic and other paroxysmal events is important during video electroencephalography (EEG) monitoring. This study was undertaken to measure the event capture rate of an home service operating across Australia using a shoulder-worn EEG device and telescopic pole-mounted camera.

METHODS

Neurologist reports were accessed retrospectively. Studies with confirmed events were identified and assessed for event capture by recording modality, whether events were reported or discovered, and physiological state.

RESULTS

6,265 studies were identified, of which 2,788 (44.50%) had events. A total of 15,691 events were captured, of which 77.89% were reported. The EEG amplifier was active for 99.83% of events. The patient was in view of the camera for 94.90% of events. 84.89% of studies had all events on camera, and 2.65% had zero events on camera (mean = 93.66%, median = 100.00%). 84.42% of events from wakefulness were reported, compared to 54.27% from sleep.

CONCLUSIONS

Event capture was similar to previously reported rates from home studies, with higher capture rates on video. Most patients have all events captured on camera.

SIGNIFICANCE

Home monitoring is capable of high rates of event capture, and the use of wide-angle cameras allows for all events to be captured in the majority of studies.

EEG: Current relevance and promising quantitative analyses

Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.

Forecasting psychogenic non-epileptic seizure likelihood from ambulatory EEG and ECG

Objective.Critical slowing features (variance and autocorrelation) of long-term continuous electroencephalography (EEG) and electrocardiography (ECG) data have previously been used to forecast epileptic seizure onset. This study tested the feasibility of forecasting non-epileptic seizures using the same methods. In doing so, we examined if long-term cycles of brain and cardiac activity are present in clinical physiological recordings of psychogenic non-epileptic seizures (PNES).Approach.Retrospectively accessed ambulatory EEG and ECG data from 15 patients with non-epileptic seizures and no background of epilepsy were used for developing the forecasting system. The median period of recordings was 161 h, with a median of 7 non-epileptic seizures per patient. The phases of different cycles (5 min, 1 h, 6 h, 12 h, 24 h) of EEG and RR interval (RRI) critical slowing features were investigated. Forecasters were generated using combinations of the variance and autocorrelation of both EEG and the RRI of the ECG at each of the aforementioned cycle lengths. Optimal forecasters were selected as those with the highest area under the receiver-operator curve (AUC).Main results.It was found that PNES events occurred in the rising phases of EEG feature cycles of 12 and 24 h in duration at a rate significantly above chance. We demonstrated that the proposed forecasters achieved performance significantly better than chance in 8/15 of patients, and the mean AUC of the best forecaster across patients was 0.79.Significance.To our knowledge, this is the first study to retrospectively forecast non-epileptic seizures using both EEG and ECG data. The significance of EEG in the forecasting models suggests that cyclic EEG features of non-epileptic seizures exist. This study opens the potential of seizure forecasting beyond epilepsy, into other disorders of episodic loss of consciousness or dissociation.