Seizure documentation in people living with epilepsy

Over- and underreporting of seizures: How big is the problem?

OBJECTIVE

Clinical decisions on managing epilepsy patients rely on patient accuracy regarding seizure reporting. Studies have noted disparities between patient-reported seizures and electroencephalographic (EEG) findings during video-EEG monitoring periods, chiefly highlighting underreporting of seizures, a well-recognized phenomenon. However, seizure overreporting is a significant problem discussed within the literature, although not in such a large cohort. Our aim is to quantify the over- and underreporting of seizures in a large cohort of ambulatory EEG patients.

METHODS

We performed a retrospective data analysis on 3407 patients referred to a diagnostic service for ambulatory video-EEG between 2020 and 2022. Both patient-reported events and events discovered on review of the video-EEG were analyzed and classified as epileptic, psychogenic (typically clinical motor events, without accompanying EEG change), or noncorrelated events (NCEs; without perceivable clinical or EEG change). Events were analyzed by state of arousal and indication for referral. Subgroup analysis was performed in patients with focal and generalized epilepsies.

RESULTS

A total of 21 024 events were recorded by 3407 patients. Fifty-eight percent of reported events were NCEs, whereas 27% of all events were epileptic. Sixty-four percent of epileptic seizures were not reported by the patient but discovered by the clinical service on review of the recording. NCEs were in the highest proportion in the awake and drowsy arousal states and were the most common event type for the majority of referral indications. Subgroup analysis found a significantly higher proportion of NCEs in the patients with focal epilepsy (23%) compared to generalized epilepsy (10%; p < .001, chi-squared proportion test).

SIGNIFICANCE

Our results reaffirm the phenomenon of underreporting and highlight the prevalence of overreporting. Overreporting likely represents irrelevant symptoms or electrographic discharges not represented on scalp electrodes, identification of which has important clinical relevance. Future studies should analyze events by risk factors to elucidate relationships clinicians can use and investigate the etiology of NCEs.

Insights From Chronic ECoG by RNS

SUMMARY

The NeuroPace responsive neurostimulation system (RNS) has revolutionized the care of patients suffering from focal epilepsy since its approval in 2014. One major advantage of this device is its innate ability to gather long-term electrocorticographic (ECoG) data that the device uses in its novel closed-loop treatment paradigm. Beyond the standard stimulation treatments, which have been demonstrated to be safe and well-tolerated, the data collected by the RNS provide valuable information, such as the long-term circadian and ultradian variations that affect seizure risk, obtained under naturalistic conditions. Additionally, these data inform future surgical procedures, supplementing clinically reported seizures by patients, assessing the response to newly added anti-seizure medications, helping to forecast the risk of future seizures, and understanding the mechanisms of certain long-term outcomes in patients with postsurgical epilepsy. By leveraging these data, the delivery of high-quality clinical care for patients with epilepsy can only be enhanced. Finally, these data open significant avenues of research, including machine learning and artificial intelligence algorithms, which may also translate to improved outcomes in patients who struggle with recurrent seizures.

Add-on treatment with cenobamate is already effective at low doses in refractory focal epilepsy: A prospective observational study

OBJECTIVE

Cenobamate, a novel antiseizure medication with a dual mechanism of action, has been shown in pivotal trials to significantly improve seizure control in treatment-resistant focal epilepsy. We aimed to evaluate whether these promising results could be confirmed in a real-world setting with a follow-up period of up to 12 months.

METHODS

Patients from a tertiary epilepsy center who received cenobamate add-on between June 2021 and October 2023 were followed up prospectively at 3, 6, and 12 months after treatment initiation for assessment of seizure outcomes and treatment-related adverse events.

RESULTS

The clinical cohort included 112 adult patients with 30% nonlesional cases and a wide spectrum of epileptogenic lesions underlying refractory focal epilepsy. We observed a significant reduction in monthly seizure frequency of all seizure types already after 3 months of treatment at a median cenobamate dose of 100 mg/day. Forty-six percent of patients were responders with a ≥50% seizure reduction, 26% had a ≥75% seizure reduction, and 9% became seizure-free. Among the 74 patients with available follow-up of 12 months, the responder rates reached 55%, 35%, and 19% for ≥50%, ≥75%, and 100% seizure reduction, respectively. After 3 months of treatment, 38% of patients reported adverse effects, mainly (84%) mild to moderate in intensity. Adjustment of comedication allowed successful management of adverse effects in 32% of patients. At a group level, there was no correlation between the cenobamate daily dose and the incidence of adverse events.

SIGNIFICANCE

We found a clinically relevant response to cenobamate already at a low daily dose of 100 mg also in a patient cohort with a higher degree of drug resistance than in pivotal trials. Our prospectively collected data provide real-world evidence for high efficacy and good tolerability of the drug, although no standardized treatment protocol or comparison with a control group was applied.

Home recording of 3-Hz spike-wave discharges in adults with absence epilepsy using the wearable Sensor Dot

OBJECTIVE

Home monitoring of 3-Hz spike-wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary with objective counts. We investigated the use and performance of the Sensor Dot (Byteflies) wearable in persons with absence epilepsy in their home environment.

METHODS

Thirteen participants (median age = 22 years, 11 female) were enrolled at the university hospitals of Leuven and Freiburg. At home, participants had to attach the Sensor Dot and behind-the-ear electrodes to record two-channel electroencephalogram (EEG), accelerometry, and gyroscope data. Ground truth annotations were created during a visual review of the full Sensor Dot recording. Generalized SWDs were annotated if they were 3 Hz and at least 3 s on EEG. Potential 3-Hz SWDs were flagged by an automated seizure detection algorithm, (1) using only EEG and (2) with an additional postprocessing step using accelerometer and gyroscope to discard motion artifacts. Afterward, two readers (W.V.P. and L.S.) reviewed algorithm-labeled segments and annotated true positive detections. Sensitivity, precision, and F1 score were calculated. Patients had to keep a seizure diary and complete questionnaires about their experiences.

RESULTS

Total recording time was 394 h 42 min. Overall, 234 SWDs were captured in 11 of 13 participants. Review of the unimodal algorithm-labeled recordings resulted in a mean sensitivity of .84, precision of .93, and F1 score of .89. Visual review of the multimodal algorithm-labeled segments resulted in a similar F1 score and shorter review time due to fewer false positive labels. Participants reported that the device was comfortable and that they would be willing to wear it on demand of their neurologist, for a maximum of 1 week or with intermediate breaks.

SIGNIFICANCE

The Sensor Dot improved seizure documentation at home, relative to patient self-reporting. Additional benefits were the short review time and the patients' device acceptance due to user-friendliness and comfortability.

Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy

BACKGROUND

Recent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home.

PURPOSE

The study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems.

MATERIALS AND METHODS

Adults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed.

RESULTS

Twelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems.

CONCLUSIONS

EEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.

Artificial intelligence-enhanced epileptic seizure detection by wearables

OBJECTIVE

Wrist- or ankle-worn devices are less intrusive than the widely used electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom-developed deep-learning seizure detection models, we demonstrate the detection of a broad range of seizure types by wearable signals.

METHODS

Patients admitted to the epilepsy monitoring unit were enrolled and asked to wear wearable sensors on either wrists or ankles. We collected patients' electrodermal activity, accelerometry (ACC), and photoplethysmography, from which blood volume pulse (BVP) is derived. Board-certified epileptologists determined seizure onset, offset, and types using video and EEG recordings per the International League Against Epilepsy 2017 classification. We applied three neural network models-a convolutional neural network (CNN) and a CNN-long short-term memory (LSTM)-based generalized detection model and an autoencoder-based personalized detection model-to the raw time-series sensor data to detect seizures and utilized performance measures, including sensitivity, false positive rate (the number of false alarms divided by the total number of nonseizure segments), number of false alarms per day, and detection delay. We applied a 10-fold patientwise cross-validation scheme to the multisignal biosensor data and evaluated model performance on 28 seizure types.

RESULTS

We analyzed 166 patients (47.6% female, median age = 10.0 years) and 900 seizures (13 254 h of sensor data) for 28 seizure types. With a CNN-LSTM-based seizure detection model, ACC, BVP, and their fusion performed better than chance; ACC and BVP data fusion reached the best detection performance of 83.9% sensitivity and 35.3% false positive rate. Nineteen of 28 seizure types could be detected by at least one data modality with area under receiver operating characteristic curve > .8 performance.

SIGNIFICANCE

Results from this in-hospital study contribute to a paradigm shift in epilepsy care that entails noninvasive seizure detection, provides time-sensitive and accurate data on additional clinical seizure types, and proposes a novel combination of an out-of-the-box monitoring algorithm with an individualized person-oriented seizure detection approach.

Experience of the new seizure diary in the Free State and Northern Cape

BACKGROUND

Epilepsy is a neurological disease affecting adults and children globally. A seizure diary is one of the self-management tools for tracking seizures. This study aims to ascertain the experience of a new seizure diary by persons completing the diary in the Free State and Northern Cape of South Africa.

METHODS

Adult patients with epilepsy attending Universitas Academic Hospital epilepsy clinic in Bloemfontein, clinics in Kimberley and the casualty department of Kimberley hospital (Robert Mangaliso Sobukwe hospital) received a new seizure diary. After using the diary for 6 months, participants (patients, relatives or caregivers) completed a questionnaire.

RESULTS

A total of 139 epilepsy patients received a new seizure diary; 67 previously diary-unexposed participants and 33 participants who had previous exposure to a seizure diary. The majority of participants, namely 91% of previously diary-unexposed and 84.9% of participants who had previous exposure to the seizure diary, understood the new seizure diary. Participants who had previous exposure to a seizure diary were predominantly very positive about the new diary because it had more information. However, 21.2% indicated that they preferred the old one because it was easier to complete.

CONCLUSION

Patients, caregivers or relatives from both groups used the new seizure diary and provided important information about their experience with the new diary. Despite a few complaints about using the new diary, most participants who had previous exposure to a seizure diary preferred the new seizure diary.Contribution: This study explored participants' opinions of the new seizure diary.

Feasibility of video/audio monitoring in the analysis of motion and treatment effects on night-time seizures - Interventional study

THE AIM OF THE STUDY

This pilot study assessed the ability of a video/audio-based seizure monitoring system to evaluate (I) baseline frequency and severity of nocturnal seizures with motor features in patients with drug-resistant epilepsy (DRE) and (II) the individual effect of brivaracetam (BRV) treatment on number, duration and movement intensity of these seizure types. Algorithmic feature analysis was developed for assessment of qualitative changes in movement intensity measurements within seizure types before and after BRV intervention.

MATERIALS AND METHODS

Night-time motor seizures of recruited patients were recorded in two separate four-week monitoring periods. The first period defined a prescreening phase (n = 13 patients) to establish a baseline, and the second period defined the intervention phase (n = 9 patients), with BRV initiated during the second week of the second monitoring period. All recorded nights were analyzed by an expert video reviewer, and all unequivocal seizures were classified by an epileptologist. Seizure frequencies using both seizure diaries and video monitoring were compared. The effect of BRV on both seizure duration and movement intensity was assessed by numerical comparison of visual features calculated from motion characteristics of the video, as well as spectral features from the recorded audio. The statistical significance of changes in seizure duration and intensity before and after the intervention were investigated by Wilcoxon rank-sum test and visual inspection of Kernel density estimation.

RESULTS

8 patients marked seizures in their seizure diaries during the prescreening phase. During the three-week follow-up, three patients achieved > 50% seizure decrease, four patients did not respond to treatment, and two patients experienced worsening of seizures. Five patients were able to document 40-70% of their seizures compared to the video/audio monitoring system. According to the signal feature analysis the intervention decreased movement intensity with clear clinical significance in three patients, whereas statistically significant differences in features appeared in 8 out of 9 patients.

CONCLUSIONS

The novel video/audio monitoring system improved the evaluation of treatment effect compared to the seizure diaries and succeeded in providing a comparative intra-patient assessment of the movement intensity and duration of the recorded seizures.