Epilepsy Monitoring Unit Research Articles

Differential electrographic seizure patterns in malformations of cortical development, early life brain injury, and later life brain injury

Structural epilepsy is a chronic neurologic condition that may be caused by in utero malformations of cortical development (MCD) or post-natal brain injuries resulting in encephalomalacia. We hypothesized that the timing of epileptogenic insult would lead to distinct electrographic seizure patterns. Specifically, we predicted that later life insults would lead to longer duration seizures with higher proportion of focal: focal to bilateral tonic-clonic (FBTC) seizures and low rates of bihemispheric onset seizures, as compared to early life insults. We performed a retrospective chart review of 70 adult patients – 33 with epilepsy secondary to brain injury (9 with injury occurring before 16 years and 24 with injury occurring at or after 16 years) with resultant encephalomalacia on MR imaging and 37 with epilepsy secondary to MCD – admitted to the University of Pittsburgh Epilepsy Monitoring Unit for presurgical evaluation. There were no significant differences in duration of epilepsy or number of trialed seizure medications between the groups. We examined scalp EEG data for all patients, as well as intracranial EEG data in a subset. We analyzed seizure duration, seizure frequency, and seizure type (focal, FBTC, and bihemispheric onset) in three cohorts: MCD patients, patients with brain injury occurring in early life (<16 years old), and patients with brain injury occurring in later life (≥16 years old). Patients with later life brain injury had significantly longer and less frequent seizures as compared to MCD cohorts. There were no differences between MCD and early life brain injury cohorts. Seizure duration findings were corroborated in a subset of patients who additionally underwent intracranial EEG monitoring. Additionally, later life brain injury patients had significantly different seizure types as compared to MCD cohorts, with high rates of FBTC and low rates of bihemispheric onset. Again, there was no significant differences in seizure type between early life brain injury and MCD cohorts. These novel findings indicate the relevance of timing of epileptogenic insult on the electrophysiological characteristics of structural epilepsies.

Effects of epileptic seizures on the quality of biosignals recorded from wearables.

Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri-ictal periods in performing a feature-based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. This study assesses the signal quality of wearable data during peri-ictal phases of generalized tonic-clonic and focal to bilateral tonic-clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri-ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre- and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist-worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.

Clinical utility of ultra long-term subcutaneous electroencephalographic monitoring in drug-resistant epilepsies: a "real world" pilot study.

This study was undertaken to assess the clinical utility, safety, and tolerability in epilepsy patients of ultra long-term monitoring with a novel subcutaneous electroencephalographic (EEG) device (sqEEG). Five patients with drug-resistant focal epilepsy were implanted (one patient bilaterally) with sqEEG. In phase 1, we assessed sqEEG sensitivity for seizure recording by recording seizures simultaneously with scalp EEG in the epilepsy monitoring unit (EMU). sqEEG was scored either visually (v-sqEEG) or by using a semiautomatic algorithm (EpiSight; E-sqEEG). In phase 2, the patients were monitored as outpatients for 3-6 months. sqEEG data were analyzed monthly, evaluating concordance of data obtained by v-sqEEG, E-sqEEG, and patients' diaries. v-sqEEG data were used to guide treatment adjustments. sqEEG-related side effects were assessed throughout the study. In phase 1, v-sqEEG detected all seizures recorded in the EMU in all patients, whereas E-sqEEG was as effective in three patients. In the other two patients, E-sqEEG detected only a proportion or none of the seizures, respectively. Sensitivity of E-sqEEG depended on the ictal EEG features. In phase 2, a 100% concordance between E-sqEEG and v-sqEEG in seizure detection was observed for the same three patients as in phase 1. In the other two patients (one implanted bilaterally), effectiveness of E-sqEEG in detecting seizure as compared to v-sqEEG ranged from 0% to 83%. v-sqEEG showed that all patients reported in their diaries fewer seizures than they actually suffered. In four of five patients, v-sqEEG showed that the treatment adjustments had been ineffective or associated with a seizure increment. The only side effect was an infection at the implantation site in one patient. The sqEEG system could collect reliable information on seizure activity, thus providing clinically relevant information. Sensitivity of EpiSight in detecting seizures varied across patients, depending on the ictal EEG features. sqEEG ultra long-term monitoring was feasible and well tolerated.

Functional seizure semiology and classification in a public and private hospital

PurposeOur understanding of potential differences in seizure semiology among patients with functional seizures (FS), also known as psychogenic non-epileptic seizures (PNES), across socioeconomic contexts is currently limited. By examining the differences in seizure manifestations between different socioeconomic groups, we aim to enhance the understanding of how socioeconomic factors may influence FS presentation. This study aimed to describe FS semiology in patients from a private and public epilepsy monitoring units (EMUs) in Cape Town, South Africa. MethodsThe study included patients with FS confirmed through video-electroencephalography (video-EEG) and without comorbid epilepsy. For this retrospective case-control study, data on seizure semiology was gathered from digital patient records, beginning with the earliest available record for each hospital. ResultsA total of 305 patients from a private hospital and 67 patients from a public hospital were eligible for the study (N = 372). The private hospital tended to report more akinetic and subjective seizure types when compared to the public hospital. Additionally, patients at the public hospital had higher odds of reporting emotional seizure triggers (aOR=2.57, 95% CI [1.03, 6.37]), loss of consciousness or awareness (aOR=2.58, 95% CI [1.07, 6.24]), and rapid post-event recovery (aOR=6.01, 95% CI [2.52, 14.34]). At the same time, they were less likely to report both short (<30 s) (aOR=0.21, 95% CI [0.08, 0.55]) and long (>5 min) seizures (aOR=0.73, 95% CI [0.13, 0.56]), amnesia for the event (aOR=0.19, 95% CI [0.09, 0.43]), ictal aphasia (aOR=0.33, 95% CI [0.14, 0.76]) or falls and drop attacks (aOR=0.43, 95% CI [0.18, 0.996]), when compared to the private hospital patients. ConclusionWhile the seizure manifestations were largely consistent across the two socioeconomic cohorts of patients with FS, some subtle differences were observed and warrant further investigation.

Ex-SPECTing success: Predictors of successful SPECT radiotracer injection during presurgical video-EEG admissions.

To determine predictors of successful ictal single photon emission computed tomography (SPECT) injections during Epilepsy Monitoring Unit (EMU) admissions for patients undergoing presurgical evaluation for drug-resistant focal epilepsy. In this retrospective study, consecutive EMU admissions were analyzed at a single center between 2019 and 2021. All seizures that occurred during the admission were reviewed. "Injectable seizures" occurred during hours when the radiotracer was available. EMU-level data were analyzed to identify factors predictive of an EMU admission with a successful SPECT injection (successful admission). Seizure-level data were analyzed to identify factors predictive of an injectable seizure receiving a SPECT injection during the ictal phase (successful injection). A multivariate generalized linear model was used to identify predictive variables. 125 EMU admissions involving 103 patients (median 37 years, IQR 27.0-45.5) were analyzed. 38.8% of seizures that were eligible for SPECT (n = 134) were successfully injected; this represented 17.4% of all seizures (n = 298) that occurred during admission. Unsuccessful admissions were most commonly due to a lack of seizures during EMU-SPECT (19.3%) or no injectable seizures (62.3%). Successful EMU-SPECT was associated with baseline seizure frequency >1 per week (95% CI 2.1-3.0, P < 0.001) and focal PET hypometabolism (95% CI 2.0-3.7, P < 0.001). On multivariate analysis, the only factor associated with successful injection was patients being able to indicate they were having a seizure to staff (95% CI 1.0-4.4, P = 0.038). Completing a successful ictal SPECT study remains challenging. A baseline seizure frequency of >1 per week, a PET hypometabolic focus, and a patient's ability to indicate seizure onset were identified as predictors of success. These findings may assist EMUs in optimizing their SPECT protocols, patient selection, and resource allocation.

Intraoperative Electromyogram Analysis of Postural Musculature With Three-dimensional Screen Display Versus Operating Microscope in Ophthalmic Surgery: A Clinical Methods Study.

To objectively analyze the effect of three-dimensional screen-based surgery (3D SBS) versus traditional operating microscope (TOM) on operating surgeon posterior chain postural musculature during ophthalmic surgery. We hypothesized an increase in median amplitude of electromyography (EMG) signals when using a TOM compared to 3D SBS. The goal was to assess surgical ergonomics that may contribute to cervical and lumbar spine pathology. EMG analysis was conducted at the University of Cincinnati Medical Center Epilepsy Monitoring Unit. Data were collected in the private practice setting at Cincinnati Eye Institute. This was an institutional review board exempt, nonrandomized, prospective, single center, n = 1 clinical methods study. EMG surface electrodes were affixed to the bilateral splenius capitis, sternocleidomastoid, upper trapezius, anterior deltoid, and quadratus lumborum musculature. EMG data were collected across four sessions consisting of one day each using 3D SBS or a TOM, and two cross-over days. A survey regarding surgeon comfort was administered before, during, and after each surgery, and at the end of each day. EMG cross-over data demonstrated an increase in muscle activation in all measured muscles except the anterior deltoids and right quadratus lumborum with use of a TOM. Survey data showed increased fatigue when using a TOM relative to 3D SBS. EMG demonstrated increased postural muscle activation when utilizing TOM relative to 3D SBS. Similar to previous studies, our survey data suggest inferior ergonomics of TOM relative to 3D SBS. [Ophthalmic Surg Lasers Imaging Retina 2024;55:628-636.].

An essay on the Charcot and Richer hysteria: from charcoal drawings to cell phones.

Hysteria, previously also known as the disease of the womb, has moved from being a woman's illness through the medieval times' stigma of demonic possession, to the modern concept of a functional neurological disorder. Interestingly to the present assay, Charcot (1825-1893) and Richer (1849-1933) described, in their 1887 work Les Démoniaques dans l'art, by means of iconography, semiological aspects of the so-called Grande Attaque Hystérique, which resembles features of psychogenic nonepileptic seizures emulating grand mal epileptic seizures. The aim of the present assay is to describe how those charcoal iconographic representations evolved through history and are nowadays portrayed in videos recorded at epilepsy monitoring units and patients' cell phones.

Dreams interrupted: characteristics of REM sleep-associated seizures and status epilepticus.

Seizures are rare in rapid eye movement sleep (REM). However; seizures sometimes occur in REM, and a small number of focal epilepsy patients display their maximum rate of interictal epileptiform discharges in REM. We sought to systematically identify and characterize seizures in REM. We reviewed all admissions to the Epilepsy Monitoring Unit (EMU) at the Winnipeg Health Sciences Centre over 12-months in 2014-2015. American Academy of Sleep Medicine sleep-stage scoring was initially applied in the standard 30-second epochs. Then, to capture sudden changes in sleep-wake state on shorter timescales that are associated with seizure formation and propagation, we re-scored ictal and peri-ictal EEG epochs every 1 second. Patients found to have seizures in REM were subject to chart review spanning three years pre- and post-admission. REM seizures occurred in 3/63 EMU patients. Notably, one patient exhibited continuous epileptiform activity, consistent with focal nonconvulsive electrographic status epilepticus, throughout REM cycles for each night of her admission. Otherwise, discrete REM seizures constituted a small fraction of the other patients' total seizures (range 5.0-8.3%), occurred shortly after REM onset from non-REM 2, and were manifest as minor epileptic arousals. Our results confirm that REM seizures are rare, while highlighting outliers who widen the known spectrum of heterogeneous sleep effects on seizures/epilepsy. We also report the first case of paradoxical status epilepticus in REM.

Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability.

Introduction: For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific in silico dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. Methods: We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. Results: Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. Discussion: This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.

Intervention time and adverse events in a canadian epilepsy monitoring unit: An updated audit

BackgroundOptimizing patient safety in the epilepsy monitoring unit (EMU) has become a topic of increasing interest. We performed an audit of our center's new single-floor EMU, assessing intervention rate (IR), intervention time (IT), and adverse events (AEs). MethodsA prospective study was conducted on all clinical seizures of patients admitted over a one-year period at our Canadian academic tertiary care center's new single-floor EMU. This single-floor EMU was supervised by EEG technologists during daytime (similar to the old set-up) and beneficiary attendants during nighttime/weekends (versus live video feed to the central nursing station on the neurology ward previously). Among 153 admissions, 79 were analyzed, and a total of 537 seizures were reviewed to assess IR, IT, and AEs. Univariate comparisons were performed with our double-floor EMU, which we reported in a previous publication. ResultsIn our new single-floor EMU, the IR was 61.1 % and overall median IT was 29.0s (19.0s–45.9s). The AE rate was 4.8 %. Compared to previously reported numbers for our old double-floor EMU (IR = 27.8 %; IT = 21.0s; AE = 1.2 %), the IR was significantly higher ((p < 0.001) but unexpectedly, the median IT was higher (p < 0.001) as well as the AE rate (p < 0.001). ConclusionThis prospective evaluation revealed a small but non-negligible rate of complications in our EMU, higher than our prior retrospective audit. Heightened levels of supervision in our new single-floor EMU led to higher IR. This may have led to artificially longer ITs.

High precision in epileptic seizure self-reporting with an app diary

People with epilepsy frequently under- or inaccurately report their seizures, which poses a challenge for evaluating their treatment. The introduction of epilepsy health apps provides a novel approach that could improve seizure documentation. This study assessed the documentation performance of an app-based seizure diary and a conventional paper seizure diary. At two tertiary epilepsy centers patients were asked to use one of two offered methods to report their seizures (paper or app diary) during their stay in the epilepsy monitoring unit. The performances of both methods were assessed based on the gold standard of video-EEG annotations. In total 89 adults (54 paper and 35 app users) with focal epilepsy were included in the analysis, of which 58 (33 paper and 25 app users) experienced at least one seizure and made at least one seizure diary entry. We observed a high precision of 85.7% for the app group, whereas the paper group’s precision was lower due to overreporting (66.9%). Sensitivity was similar for both methods. Our findings imply that performance of seizure self-reporting is patient-dependent but is more precise for patients who are willing to use digital apps. This may be relevant for treatment decisions and future clinical trial design.

Epileptic seizure forecasting with wearable-based nocturnal sleep features.

Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach. Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon. Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.

Back to Basics: Care of the Stereotactic EEG Implanted Patient

Purpose: Stereotactic EEG (SEEG) is gaining increasing popularity in the United States. Patients undergoing SEEG have unique challenges, and their needs are different compared with noninvasive cases. We aim to describe the medical, nursing, and other institutional practices of SEEG evaluations among tertiary referral (level IV) epilepsy centers accredited by the National Association of Epilepsy Centers. Methods: We analyzed data obtained from a Research Electronic Data Capture (REDCap) survey we formulated and distributed to directors of all level IV epilepsy centers listed by the National Association of Epilepsy Center. Most questions were addressed to the adult and pediatric SEEG programs separately. Results: Among 199 epilepsy center directors invited to complete the survey, 90 (45%) responded. Eighty-three centers (92%) reported they perform SEEG evaluations. Of the 83 respondents, 56 perform SEEG in adult and 47 in pediatric patients. Twenty-two centers evaluate both pediatric and adult subjects. The highest concordance of SEEG workflow was in (1) epilepsy monitoring unit stay duration (1–2 weeks, 79% adult and 85% pediatric programs), (2) use of sleep deprivation (94% both adult and pediatric) and photic stimulation (79% adult and 70% pediatric) for seizure activation, (3) performing electrical cortical stimulation at the end of SEEG evaluation after spontaneous seizures are captured (84% adult and 88% pediatric), and (4) daily head-wrap inspection (76% adult and 80% pediatric). Significant intercenter variabilities were noted in the other aspects of SEEG workflow. Conclusions: Results showed significant variability in SEEG workflow across polled centers. Prospective, multicenter protocols will help the future development and harmonization of optimal practice patterns.

Medication Reconciliation Errors on Discharge for Epilepsy Monitoring Unit Patients.

Medication errors are common in the inpatient setting. Epilepsy patients who miss doses of their antiseizure medications are at risk for breakthrough seizures and subsequent complications. The purpose of this study was to quantify and characterize anti-seizure medications reconciliation errors on discharge from the epilepsy monitoring unit (EMU). Consecutive admissions to an academic medical center EMU were retrospectively reviewed. Medication reconciliation errors on discharge, including drug errors, dosing errors, and dose timing errors, were recorded. Associations between medication errors and clinical and demographic variables were analyzed using binary logistic regression for continuous variables and Fisher exact tests for categorical variables. One hundred and eleven admissions between January 1, 2021 and December 31, 2021 were identified. Fourteen anti-seizure medication reconciliation errors were recorded during 11 unique admissions (9.9% of admissions). The most common error type was dosing error (10/14 errors; 71.4%). Number of antiseizure medications on admission (p=0.004), total number of medications on admission (p=0.013), number of medication changes during admission (p=0.0007), and length of stay (p=0.0001) were associated with increased likelihood of errors. Medication reconciliation errors upon discharge from the EMU occur during approximately 10% of admissions. A higher number of preadmission antiseizure medications, higher total number of preadmission medications, higher number of medication changes during admission, and longer length of stay are associated with increased risk of discharge medication reconciliation errors. Careful attention should be paid to patients with these risk factors.

Patient Handoff Practices at the Epilepsy Centers in the United States: A Survey of the Medical Directors.

Communication failure is one of the most significant causes of medical errors. Providing care to patients with seizures at comprehensive epilepsy centers requires uninterrupted coverage and a multidisciplinary approach. However, handoff practices in these settings have not been comprehensively assessed, and recommendations for their standardization are currently lacking. The aim of this observational study was to define the scope of existing practices for patient handoffs across epilepsy centers in the United States and provide relevant recommendations. A 79-question survey was developed to establish the patterns of transition of care for patients undergoing continuous EEG recording, including the periodicity of handoffs and specifics of the relevant workflow. With permission from the National Association of Epilepsy Centers (NAEC), the survey was distributed to the medical directors of all Level 3 and 4 NAEC-accredited epilepsy centers in the United States. The responses were obtained from 70 institutions yielding a survey response rate of 26%. Of these, more than 77% had established weekly handoff processes for both the epilepsy monitoring unit and continuous EEG (cEEG) monitoring services. However, only 53% and 43% of centers had procedures for daily service transfers for the patients admitted to the epilepsy monitoring unit or the patients undergoing cEEG, respectively. The patterns of handoffs were complex and utilized group handoffs in < 50% of institutions. In most centers (>70%), patient data transmitted through handoffs included history, clinical information, and EEG findings. However, templates were not applied to standardize this information. All participants agreed or strongly agreed that a culture of patient safety was maintained in their place of practice; however, 12% of participants felt that insufficient time was allowed to discuss these patients or carry out the handoffs without interruptions. Existing handoff practices are not uniform or fully established across epilepsy centers in the United States. This study recommends that guidelines for formal handoff procedures be developed and introduced as a quality metric for all NAEC-accredited epilepsy centers.

Heartbeat evoked potentials and autonomic arousal during dissociative seizures: insights from electrophysiology and neuroimaging

IntroductionDissociative seizures often occur in the context of dysregulated affective arousal and entail dissociative symptoms such as a disintegration of bodily awareness. However, the interplay between affective arousal and changes...

P.003 Outcome of psychogenic nonepileptic seizures following diagnosis in the epilepsy monitoring unit

Background: study patients with PNES’ outcome after their diagnosis in the EMU. Methods: Comparative analyses were carried out on several variables before and after diagnosis: number of participants with daily PNES, number of visits to the emergency department, number of participants who took ASMs or psychotropic drugs, and employment status. Results: 61/103 patients (79% female) participated. The median age at PNES onset was 35 years. 62% were receiving ASMs and 40% psychotropic drugs. The mean stay at the EMU was five days. PNES diagnosis was explained to almost all patients (97%) by the end of their EMU stay and was well accepted by most (89%). When contacted, 46% of participants no longer had PNES; 32% mentioned that their PNES had ceased immediately upon communication of the diagnosis. Fewer patients had daily seizures after the diagnosis. Similarly, the median number of emergency department visits was significantly lower. Only 17 patients consulted their general practitioner and 20 a neurologist after a PNES attack. The use of ASMs was also significantly reduced from 70% to 33%, with only one still taking an ASM for its antiseizure properties. Conclusions: significant reductions in PNES frequency, health care utilization and ASM use.

Detection of focal to bilateral tonic-clonic seizures using a connected shirt.

This study was undertaken to develop and evaluate a machine learning-based algorithm for the detection of focal to bilateral tonic-clonic seizures (FBTCS) using a novel multimodal connected shirt. We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video-electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board-certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross-validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC-AUC). We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of .55/24 h and a median TiW of 10 s/alarm. ROC-AUC was .90 (95% confidence interval = .88-.91). Median detection latency from the time of progression to the bilateral tonic-clonic phase was 25.5 s. The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real-time and online seizure detection algorithm are required to validate the performance and usability of this device.

Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Challenges and Prospects in Epilepsy Monitoring Units: A Comprehensive Review of Logistic Barriers.

Epilepsy is one of the most common neurological diseases with a prevalence ranging from 0.5% to 2% in different sittings. The World Health Organization (WHO) estimated that nearly 80% of this burden is borne by resource-poor countries where even conventional electroencephalogram (EEG) coverage is dramatically short. Video EEG monitoring applied for days as conducted in epilepsy monitoring units (EMUs) is aimed at seizure localization, anti-seizure medication (ASM) adjustment, or epilepsy surgery evaluation and planning.However, the EEG approach in EMUs has its obstacles. The present article is aimed to concentrate on the logistic challenges of EMUs, discussing existing data and limitations and offering suggestions for future planning to enhance the utilization of existing technology. Shortages of adult and pediatric epileptologists, qualified nurses, as well as EEG technologists have been reported in different countries. Moreover, injuries and falls, psychosis, status epilepticus, and unexpected death have been stated to be the most frequent safety issues in EMUs. Enhancements to mitigate logistical and healthcare system-related barriers in EMUs include the implementation of large cohort studies and the utilization of artificial intelligence (AI) for the identification and categorization of specific risks among EMU admissions. The establishment of EMUs andtheir associated challenges and barriers are best acknowledged through discussions and dialogue with various stakeholders.