Do patients need to stay in bed all day in the Epilepsy Monitoring Unit? Safety data from a non-restrictive setting

Automated detection of tonic seizures using wearable movement sensor and artificial neural network

Although several validated wearable devices are available for detection of generalized tonic-clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report development and validation of an artificial neural network (ANN) model for automated detection of tonic seizures with visible clinical manifestation using a wearable wristband movement sensor (accelerometer and gyroscope). The dataset prospectively recorded for this study included 70 tonic seizures from 15 patients (seven males, age 3-46 years, median = 19 years). We trained an ANN model to detect tonic seizures. The independent test dataset comprised nocturnal recordings, including 10 tonic seizures from three patients and additional (distractor) data from three subjects without seizures. The ANN model detected nocturnal tonic seizures with visible clinical manifestation with a sensitivity of 100% (95% confidence interval = 69%-100%) and with an average false alarm rate of .16/night. The mean detection latency was 14.1 s (median = 10 s), with a maximum of 47 s. These data suggest that nocturnal tonic seizures can be reliably detected with movement sensors using ANN. Large-scale, multicenter prospective (phase 3) trials are needed to provide compelling evidence for the clinical utility of this device and detection algorithm.

Epilepsy monitoring unit practices and safety among NAEC epilepsy centers: A census survey

OBJECTIVE

An epilepsy monitoring unit (EMU) is a specialized unit designed for capturing and characterizing seizures and other paroxysmal events with continuous video electroencephalography (vEEG). Nearly 260 epilepsy centers in the United States are accredited by the National Association of Epilepsy Centers (NAEC) based on adherence to specific clinical standards to improve epilepsy care, safety, and quality. This study examines EMU staffing, safety practices, and reported outcomes.

METHOD

We analyzed NAEC annual report data and results from a supplemental survey specific to EMU practices reported in 2019 from 341 pediatric or adult center directors. Data on staffing, resources, safety practices and complications were collated with epilepsy center characteristics. We summarized using frequency (percentage) for categorical variables and median (inter-quartile range) for continuous variables. We used chi-square or Fisher's exact tests to compare staff responsibilities.

RESULTS

The supplemental survey response rate was 100%. Spell classification (39%) and phase 1 testing (28%) were the most common goals of the 91,069 reported admissions. The goal ratio of EEG technologist to beds of 1:4 was the most common during the day (68%) and off-hours (43%). Compared to residents and fellows, advanced practice providers served more roles in the EMU at level 3 or pediatric-only centers. Status epilepticus (SE) was the most common reported complication (1.6% of admissions), while cardiac arrest occurred in 0.1% of admissions.

SIGNIFICANCE

EMU staffing and safety practices vary across US epilepsy centers. Reported complications in EMUs are rare but could be further reduced, such as with more effective treatment or prevention of SE. These findings have potential implications for improving EMU safety and quality care.

Progressive slowing of clonic phase predicts postictal generalized EEG suppression

OBJECTIVE

Postictal generalized electroencephalography (EEG) suppression (PGES) is a surrogate marker of sudden unexpected death in epilepsy (SUDEP). It is still unclear which ictal phenomena lead to prolonged PGES and increased risk of SUDEP. Semiology features of generalized convulsive seizure (GCS type 1) have been reported as a predictor of prolonged PGES. Progressive slowing of clonic phase (PSCP) has been observed in GCSs, with gradually increasing inhibitory periods interrupting the tonic contractions. We hypothesized that PSCP is associated with prolonged PGES.

METHODS

We analyzed 90 bilateral convulsive seizures in 50 consecutive patients (21 female; age: 11-62 years, median: 31 years) recruited to video-EEG monitoring. Five raters, blinded to all other data, independently assessed the presence of PSCP. PGES and seizure semiology were evaluated independently. We determined inter-rater agreement (IRA) for the presence of PSCP, and we evaluated its association, as well as that of other ictal features, with the occurrence of PGES, prolonged PGES (≥20 s) and very prolonged PGES (≥50 s) using multivariate logistic regression analysis.

RESULTS

We found substantial IRA for the presence of PSCP (percent agreement: 80%; beyond-chance agreement coefficient: .655). PSCP was an independent predictor of the occurrence of PGES and prolonged PGES (p < .001). All seizures with very prolonged PGES had PSCP. GCS type 1 was an independent predictor of occurrence of PGES (p = .02) and prolonged PGES (p = .03) but not of very prolonged PGES. Only half of the seizures with very prolonged PGES were GCS type 1.

SIGNIFICANCE

PSCP predicts prolonged PGES, emphasizing the importance of gradually increasing inhibitory phenomena at the end of the seizures. Our findings shed more light on the ictal phenomena leading to increased risk of SUDEP. These phenomena may provide basis for algorithms implemented into wearable devices for identifying GCS with increased risk of SUDEP.

Effectiveness of Home Care Interventions in Patients with Moderate to Severe Craniocerebral Injury Combined with Epilepsy

Objective

To investigate the effect of a home care model on coping style and quality of life of patients with postcranial surgery complicated by epilepsy.

Methods

One hundred and forty-four patients with postcranial surgery complicated by epilepsy admitted to our hospital from July 2017 to April 2018 were selected as study subjects and randomly divided into 63 cases each in the observation group and the control group. The control group was given nursing interventions including health education, psychological intervention, medication guidance, complication prevention, and follow-up management, while the observation group was jointly given collaborative family nursing interventions. At a follow-up of 6 months, indicators such as coping style, treatment compliance, and quality of life were compared between the two groups.

Results

Patients in the observation group had significantly higher problem solving, help seeking, and rationalization scores and significantly lower self-blame scores than the control group (P < 0.01); significantly higher treatment compliance than the control group (P < 0.01); and significantly higher social functioning, emotional well-being, and energy/fatigue scores than the control group (P < 0.01).

Conclusion

The home care model helps to promote the development of positive coping styles, improve treatment compliance, and improve the quality of life of patients after cranial surgery.

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.

Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.

Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.

Do all patients in the epilepsy monitoring unit experience the same level of comfort? A quantitative exploratory secondary analysis

Aims

To find out which variables may be associated with comfort of patients in an epilepsy monitoring unit.

Design

Exploratory, quantitative study design.

Methods

Data were collected from October 2018 to November 2019 in Austria and Southern Germany. A total of 267 patients of 10 epilepsy centres completed the Epilepsy Monitoring Unit Comfort Questionnaire which is based on Kolcaba's General Comfort Questionnaire. Secondary data analysis were conducted by using descriptive statistics and an exploratory model building approach, including different linear regression models and several sensitivity analyses.

Results

Total comfort scores ranged from 83 to 235 points. Gender, occupation and centre turned out to be possible influential variables. On average, women had a total comfort score 4.69 points higher than men, and retired persons 28.2 points higher than high school students ≥18 years. Comfort scores of younger patients were lower than those of older patients. However, age did not show a statistically significant effect. The same could be observed in marital status and educational levels.

Conclusion

When implementing comfort measures, nurses must be aware of variables which could influence the intervention negatively. Especially, high school students ≥18 years should be supported by epilepsy specialist nurses, in order to reduce uncertainty, anxiety and discomfort. But, since the identified variables account only for a small proportion of the inter‐individual variability in comfort scores, further studies are needed to find out additional relevant aspects and to examine centre‐specific effects more closely.

Impact

Nurses ensure patient comfort during a hospital stay. However, there are variables that may impair the effectiveness of the nursing measures. Our study showed that the experience of comfort was highly individual and could be explained by sociodemographic variables only to a limited extent. Nurses must be aware that additional factors, such as the situation in the individual setting, may be relevant.

Intervention Time and Adverse Events in a Canadian Epilepsy Monitoring Unit

BACKGROUND

Intervention time (IT) in response to seizures and adverse events (AEs) have emerged as key elements in epilepsy monitoring unit (EMU) management. We performed an audit of our EMU, focusing on IT and AEs.

METHODS

We performed a retrospective study on all clinical seizures of admissions over a 1-year period at our Canadian academic tertiary care center's EMU. This EMU was divided in two subunits: a daytime three-bed epilepsy department subunit (EDU) supervised by EEG technicians and a three-bed neurology ward subunit (NWU) equipped with video-EEG where patients were transferred to for nights and weekends, under nursing supervision. Among 124 admissions, 58 were analyzed. A total of 1293 seizures were reviewed to determine intervention occurrence, IT, and AE occurrence. Seizures occurring when the staff was present at bedside at seizure onset were analyzed separately.

RESULTS

Median IT was 21.0 (11.0-40.8) s. The EDU, bilateral tonic-clonic seizures (BTCS), and the presence of a warning signal were associated with increased odds of an intervention taking place. The NWU, BTCS, and seizure rank (seizures were chronologically ordered by the patient for each subunit) were associated with longer ITs. Bedside staff presence rate was higher in the EDU than in the NWU (p < 0.001). AEs occurred in 19% of admissions, with no difference between subunits. AEs were more frequent in BTCS than in other seizure types (p = 0.001).

CONCLUSION

This study suggests that close monitoring by trained staff members dedicated to EMU patients is key to optimize safety. AE rate was high, warranting corrective measures.

Risk incidence of fractures and injuries: a multicenter video-EEG study of 626 generalized convulsive seizures

OBJECTIVE

To evaluate the incidence and risk factors of generalized convulsive seizure (GCS)-related fractures and injuries during video-EEG monitoring.

METHODS

We analyzed all GCSs in patients undergoing video-EEG-monitoring between 2007 and 2019 at epilepsy centers in Frankfurt and Marburg in relation to injuries, falls and accidents associated with GCSs. Data were gathered using video material, EEG material, and a standardized reporting form.

RESULTS

A total of 626 GCSs from 411 patients (mean age: 33.6 years; range 3-74 years; 45.0% female) were analyzed. Severe adverse events (SAEs) such as fractures, joint luxation, corneal erosion, and teeth loosening were observed in 13 patients resulting in a risk of 2.1% per GCS (95% CI 1.2-3.4%) and 3.2% per patient (95% CI 1.8-5.2%). Except for a nasal fracture due to a fall onto the face, no SAEs were caused by falls, and all occurred in patients lying in bed without evidence of external trauma. In seven patients, vertebral body compression fractures were confirmed by imaging. This resulted in a risk of 1.1% per GCS (95% CI 0.5-2.2%) and 1.7% per patient (95% CI 0.8-3.3%). These fractures occurred within the tonic phase of a GCS and were accompanied by a characteristic cracking noise. All affected patients reported back pain spontaneously, and an increase in pain on percussion of the affected spine section.

CONCLUSIONS

GCSs are associated with a substantial risk of fractures and shoulder dislocations that are not associated with falls. GCSs accompanied by audible cracking, and resulting in back pain, should prompt clinical and imaging evaluations.

Ictal quantitative surface electromyography correlates with postictal EEG suppression

OBJECTIVE

To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment.

METHODS

Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES.

RESULTS

We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%.

CONCLUSIONS

Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.

Epilepsy monitoring units can be safe places; a prospective study in a large cohort

OBJECTIVE

No international guideline is available for minimum safety measures at epilepsy monitoring units (EMUs), although recommendations for preferred practices exist. These are mostly based on expert opinion, without evidence of effectiveness. We do not apply all of these preferred practices at our EMU setting. We audited adverse events and diagnostic utility at our EMU over one year.

METHODS

From May 2018 to May 2019, we prospectively collected data concerning adverse events and diagnostic utility of all EMU admissions (noninvasive video-electroencephalogram (EEG) recordings); during these admissions, individuals can be ambulant within their EMU room.

RESULTS

There were 1062 admissions comprising 1518 EMU days. In 2% of the admissions, a complication occurred, mostly a fall without injury (n = 6). In almost half of the falls, this was from the bed. Complications occurred most often during admissions for presurgical evaluation. Antiseizure medication (ASM) was tapered in 86% of presurgical cases, but no serious injury occurred, and occurring seizures were effectively treated with intranasal midazolam if needed.

CONCLUSIONS

The overall adverse event rate was low. Falls are the most common adverse event comparable with previously published fall rates at other EMUs where people are restricted to their bed. We showed that restricted ambulation at a well-monitored EMU is not necessary and possibly unwanted. No serious injury due to tapering of ASM occurred, and intranasal midazolam was shown to be effective as acute seizure treatment.

Drug withdrawal in the epilepsy monitoring unit - The patsalos table

Investigation of possible candidates for epilepsy surgery will usually require inpatient EEG to capture seizures and allow full operative planning. Withdrawal of antiepileptic drugs increases the yield of this valuable diagnostic information and the benefits of this should justify any increase in the risk of harm associated with these seizures This paper outlines our opinion on what would constitute proposed best practice for management of antiepileptic drug (AED) dosing when patients are admitted for monitoring of seizures to an epilepsy monitoring unit (EMU). In the vast majority of cases EMU admissions are safe and, even if seizures occur, will pass off without complication. Previous guidance has concentrated on ensuring practice around technical aspects of EEG monitoring itself and staffing within the unit. In this guidance we aim to outline optimally safe ways of ensuring that EMUs ensure the minimisation of risk to the patients admitted under their care. We propose an algorithm for enhancing the safety of AED withdrawal in VT admissions while ensuring adequate seizure yields. Risk minimisation requires planned management of drug dosing (with reduction if appropriate), provision of adequate rescue medication, and adequate supervision to allow rapid response to generalised seizures. This algorithm is accompanied by a table which uses knowledge of the clinical and pharmacological properties of each AED to ensure dose withdrawal and reduction is timely and safe taking into account the severity and frequency of the individual's seizures.

Seizure detection based on heart rate variability using a wearable electrocardiography device

OBJECTIVE

To assess the feasibility and accuracy of seizure detection based on heart rate variability (HRV) using a wearable electrocardiography (ECG) device. Noninvasive devices for detection of convulsive seizures (generalized tonic-clonic and focal to bilateral tonic-clonic seizures) have been validated in phase 2 and 3 studies. However, detection of nonconvulsive seizures still needs further research, since currently available methods have either low sensitivity or an extremely high false alarm rate (FAR).

METHODS

In this phase 2 study, we prospectively recruited patients admitted to long-term video-EEG monitoring (LTM). ECG was recorded using a dedicated wearable device. Seizures were automatically detected using HRV parameters computed off-line, blinded to all other data. We compared the performance of 26 automated algorithms with the seizure time-points marked by experts who reviewed the LTM recording. Patients were classified as responders if >66% of their seizures were detected.

RESULTS

We recruited 100 consecutive patients and analyzed 126 seizures (108 nonconvulsive and 18 convulsive) from 43 patients who had seizures during monitoring. The best-performing HRV algorithm combined a measure of sympathetic activity with a measure of how quickly HR changes occurred. The algorithm identified 53.5% of the patients with seizures as responders. Among responders, detection sensitivity was 93.1% (95% CI: 86.6%-99.6%) for all seizures and 90.5% (95% CI: 77.4%-97.3%) for nonconvulsive seizures. FAR was 1.0/24 h (0.11/night). Median seizure detection latency was 30 s. Typically, patients with prominent autonomic nervous system changes were responders: An ictal change of >50 heartbeats per minute predicted who would be responder with a positive predictive value of 87% and a negative predictive value of 90%.

SIGNIFICANCE

The automated HRV algorithm, using ECG recorded with a wearable device, has high sensitivity for detecting seizures, including the nonconvulsive ones. FAR was low during the night. This approach is feasible in patients with prominent ictal autonomic changes.

Diagnostic yield of high-density versus low-density EEG: The effect of spatial sampling, timing and duration of recording

OBJECTIVE

To investigate the effect of spatial sampling and of recording duration on the diagnostic yield of EEG for identification of interictal epileptiform discharges (IEDs). Previous studies demonstrated that high-density (HD) recordings increased accuracy of localization compared to low-density (LD) recordings.

METHODS

We have prospectively evaluated the effect of spatial sampling and of recording duration in patients who had short-term (ST) recordings with a HD array of 256 electrodes following long-term (LT) recordings with a LD array consisting of the standard IFCN array of 25 electrodes. IED clusters were identified in four datasets: LT-LD, ST-LD (spatially down-sampled to the standard IFCN array), ST-HD and a shortened (90 minutes) epoch of LT-LD.

RESULTS

Sixty consecutive patients were recruited. We identified 89 IED clusters totally. Two clusters were found by increasing spatial sampling from 25 to 256 electrodes. This modest increase was not statistically significant. Eight clusters were missed by reducing the recording duration to 90 minutes, as compared with the LT recordings (p = 0.003).

CONCLUSIONS

Recording duration is more important for the diagnostic yield of EEGs than increasing spatial sampling beyond the standard IFCN electrode array.

SIGNIFICANCE

The standard IFCN electrode array provides sufficient spatial sampling for identification of the IEDs.

Automated real-time detection of tonic-clonic seizures using a wearable EMG device

Objective

To determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device.

Methods

We prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device. The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings by trained experts, who were blinded to data from the device. Reading the seizure logs from the device was done blinded to all other data.

Results

The mean recording time per patient was 53.18 hours. Total recording time was 3735.5 hours, and device deficiency time was 193 hours (4.9% of the total time the device was turned on). No adverse events occurred. The sensitivity of the wearable device was 93.8% (30 out of 32 GTCS were detected). Median seizure detection latency was 9 seconds (range −4 to 48 seconds). False alarm rate was 0.67/d.

Conclusions

The performance of the wearable EMG device fulfilled the requirements of patients: it detected GTCS with a sensitivity exceeding 90% and detection latency within 30 seconds.

Classification of evidence

This study provides Class II evidence that for people with a history of GTCS, a wearable EMG device accurately detects GTCS (sensitivity 93.8%, false alarm rate 0.67/d).