Impact of periictal nurse interventions on postictal generalized EEG suppression in generalized convulsive seizures

A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection

Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.

A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy

Epilepsy affects ~2-3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.

Sudden unexpected death in epilepsy

PURPOSE OF REVIEW

Sudden unexpected death in epilepsy (SUDEP) is a major contributor to premature mortality in people with epilepsy. This review provides an update on recent findings on the epidemiology of SUDEP, clinical risk factors and potential mechanisms.

RECENT FINDINGS

The overall risk rate of SUDEP is approximately 1 per 1000 patients per year in the general epilepsy population and that children and older adults have a similar incidence. Generalized convulsive seizures (GCS), perhaps through their effects on brainstem cardiopulmonary networks, can cause significant postictal respiratory and autonomic dysfunction though other mechanisms likely exist as well. Work in animal models of SUDEP has identified multiple neurotransmitter systems, which may be future targets for pharmacological intervention. There are also chronic functional and structural changes in autonomic function in patients who subsequently die from SUDEP suggesting that some SUDEP risk is dynamic. Modifiable risks for SUDEP include GCS seizure frequency, medication adherence and nighttime supervision.

SUMMARY

Current knowledge of SUDEP risk factors has identified multiple targets for SUDEP prevention today as we await more specific therapeutic targets that are emerging from translational research studies.

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.

Oxygen for seizures, more questions than answers: A scoping review

BACKGROUND

Ictal hypoxaemia is a feature seen in epileptic seizures, characterized by low oxygen saturations, increasing seizure prolongation risk and possibly contributing to sudden unexpected death in epilepsy (SUDEP). High flow oxygen is recommended in the management of seizures by UK's National Institute of Health and Care excellence (NICE); however, the evidence supporting this recommendation is unclear.

AIMS

To identify the efficacy of oxygen in the seizure treatment.

METHOD

A scoping review was conducted using PRISMA-ScR guidance. PsycINFO, EMBASE and MEDLINE were searched along with the references section of identified literature. Articles were critically appraised for study, patient, seizure, oxygen therapy and outcome characteristics, summarized and quality-assessed using Sackett's criteria.

RESULTS

Literature search identified 623 articles of which five met the pre-criteria for full review. One animal study demonstrated favourable effects of oxygen administration. Three human studies also reported favourable effects of oxygen administration, while one reported outcomes that were not statistically significant. Study design concerns in all identified literature confounded the ability to assess efficacy. All five publications were assigned Sackett's score of 2b.

CONCLUSION

There is a significant lack of evidence to support the efficacy of oxygen administration in epileptic seizures. Future research is needed.

Commercially available seizure detection devices: A systematic review

IMPORTANCE

Epilepsy can be associated with significant morbidity and mortality. Seizure detection devices could be invaluable tools for both people with epilepsy, their caregivers, and clinicians as they could alert caretakers about seizures, reduce the risk of sudden unexpected death in epilepsy, and provide objective and more reliable seizure tracking to guide treatment decisions or monitor outcomes in clinical trials.

OBJECTIVE

To synthesize the characteristics of commercial seizure detection tools/devices currently available.

METHODS

We performed a systematic search utilizing a diverse set of resources to identify commercially available seizure detection products for consumer use. Performance data was obtained through a systematic review on commercially available products.

OBSERVATIONS

We identified 23 products marketed for seizure detection/alerting. Devices utilize a variety of mechanisms to detect seizures, including movement detectors, autonomic change detectors, electroencephalogram (EEG) based detectors, and other mechanisms (audio). The optimal device for a person with epilepsy depends on a variety of factors including the main purpose of the device, their age, seizure type and personal preferences. Only 8 devices have published peer-reviewed performance data and the majority for tonic-clonic seizures. An informed conversation between the clinician and the patient can help guide if a seizure detection device is appropriate.

CONCLUSIONS AND RELEVANCE

Seizure detection devices have a potential to reduce morbidity and mortality for certain people with epilepsy. Clinicians should be familiar with the characteristics of commercially available devices to best counsel their patients on whether a seizure detection device may be beneficial and what the optimal devices may be.

Automated Analysis of Risk Factors for Postictal Generalized EEG Suppression

Rationale: Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES.

Methods: This is a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of SUDEP in adult patients with intractable epilepsy. We studied videoelectroencephalogram (vEEG) recordings of generalized convulsive seizures (GCS) in a cohort of patients in whom respiratory and vEEG recording were carried out during the evaluation in the epilepsy monitoring unit. A validated automated EEG suppression detection tool was used to determine presence and duration of PGES.

Results: We studied 148 GCS in 87 patients. PGES occurred in 106/148 (71.6%) seizures in 70/87 (80.5%) of patients. PGES mean duration was 38.7 ± 23.7 (37; 1–169) seconds. Presence of tonic phase during GCS, including decerebration, decortication and hemi-decerebration, were 8.29 (CI 2.6–26.39, p = 0.0003), 7.17 (CI 1.29–39.76, p = 0.02), and 4.77 (CI 1.25–18.20, p = 0.02) times more likely to have PGES, respectively. In addition, presence of decerebration (p = 0.004) and decortication (p = 0.02), older age (p = 0.009), and hypoxemia duration (p = 0.03) were associated with longer PGES durations.

Conclusions: In this study, we confirmed observations made with visual analysis, that presence of tonic phase during GCS, longer hypoxemia, and older age are reliably associated with PGES. We found that of the different types of tonic phase posturing, decerebration has the strongest association with PGES, followed by decortication, followed by hemi-decerebration. This suggests that these factors are likely indicative of seizure severity and may or may not be associated with SUDEP. An automated signal processing tool enables objective metrics, and may resolve apparent ambiguities in the role of PGES in SUDEP and seizure severity studies.

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.

Postictal stertor: Associations with focal and bilateral seizure types

OBJECTIVE

The objective of the present study was to determine the association between respiratory stertor and focal and bilateral seizure types.

METHODS

We characterized ictal and postictal behaviors during symmetric bilateral tonic-clonic (TC) and asymmetric TC seizures in the Johns Hopkins University (JHU) epilepsy monitoring unit, comparing these to focal unaware seizures. We measured the presence and duration of postictal stertorous respirations, postictal generalized electroencephalographic suppression (PGES), immobility/motor dysfunction, and encephalopathy and determined their associations and relationship to seizure types.

RESULTS

In initial seizures recorded in 80 consecutive patients, bilateral symmetric TC seizures (N = 35) were strongly associated with PGES (97%, p < 0.001) and postictal stertorous respirations (89%, p < 0.001). Only 10% of the 20 patients with asymmetric TC seizures had brief PGES; focal unaware seizures (N = 25) were not associated with PGES or stertorous breathing. Some patients (24%) with asymmetric or bilateral symmetric TC seizures had severe postictal encephalopathy with stertor that was separate or extended beyond periods of PGES.

CONCLUSION

Bilateral symmetric TC seizures, but not focal unaware seizures, have postictal stertor during PGES. Severe postictal encephalopathy, however, is also associated with motor dysfunction and stertor. Stertor appears to be a compensatory postictal respiratory pattern for ictal/postictal hypoxemia and occurs with PGES or postictal encephalopathy.

Detection of Postictal Generalized Electroencephalogram Suppression: Random Forest Approach

BACKGROUND

Sudden unexpected death in epilepsy (SUDEP) is second only to stroke in neurological events resulting in years of potential life lost. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a period of suppressed brain activity often occurring after generalized tonic-clonic seizure, a most significant risk factor for SUDEP. Therefore, PGES has been considered as a potential biomarker for SUDEP risk. Automatic PGES detection tools can address the limitations of labor-intensive, and sometimes inconsistent, visual analysis. A successful approach to automatic PGES detection must overcome computational challenges involved in the detection of subtle amplitude changes in EEG recordings, which may contain physiological and acquisition artifacts.

OBJECTIVE

This study aimed to present a random forest approach for automatic PGES detection using multichannel human EEG recordings acquired in epilepsy monitoring units.

METHODS

We used a combination of temporal, frequency, wavelet, and interchannel correlation features derived from EEG signals to train a random forest classifier. We also constructed and applied confidence-based correction rules based on PGES state changes. Motivated by practical utility, we introduced a new, time distance-based evaluation method for assessing the performance of PGES detection algorithms.

RESULTS

The time distance-based evaluation showed that our approach achieved a 5-second tolerance-based positive prediction rate of 0.95 for artifact-free signals. For signals with different artifact levels, our prediction rates varied from 0.68 to 0.81.

CONCLUSIONS

We introduced a feature-based, random forest approach for automatic PGES detection using multichannel EEG recordings. Our approach achieved increasingly better time distance-based performance with reduced signal artifact levels. Further study is needed for PGES detection algorithms to perform well irrespective of the levels of signal artifacts.

Signs and symptoms of the postictal period in epilepsy: A systematic review and meta-analysis

OBJECTIVE

The postictal period has many physical, behavioral, and cognitive manifestations associated with it. These signs and symptoms are common, can be quite debilitating, and can have a continued impact long after the seizure has ended. The purpose of this systematic review was to quantify the occurrence of postictal signs and symptoms, along with their frequency and duration in persons with epilepsy.

METHODS

Cochrane Database of Systematic Reviews, CINAHL, EMBASE, MEDLINE, PsycINFO, Web of Science, and Scopus were searched from inception to November 29, 2017. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting standards were followed. Search terms included subject headings and text words such as convulsion, epilepsy, seizure, postictal, post seizure, seizure recovery, seizure end, Todd's paresis, and Todd's paralysis. Standardized forms were used to collect various study variables. Abstract and full-text review, data abstraction, and quality assessment were all done in duplicate. Study heterogeneity was assessed using the I-squared test, and a random effects model was used to determine estimates. Publication bias was evaluated using funnel plots.

RESULTS

From 7811 abstracts reviewed, 78 articles met eligibility criteria, with 31 postictal manifestations (signs and/or symptoms) described and 45 studies included in the meta-analysis. The majority of studies described postictal headaches, migraines, and psychoses, with mean weighted frequency of 33.0% [95% confidence interval (CI) 26.0-40.0], 16.0% [95% CI 10.0-22.0], and 4.0% [95% CI 2.0-5.0], respectively. The mean weighted proportions of manifestations ranged from 0.5% (subacute postictal aggression) to 96.2% (postictal unresponsiveness) with symptom duration usually lasting <24 h but up to 2 months for physical and cognitive/behavioral symptoms respectively.

SIGNIFICANCE

Examining data on the various signs and symptoms of the postictal period will have practical applications for physicians by raising their awareness about these manifestations and informing them about the importance of optimizing their prevention and treatment in epilepsy.

Don’t Just Stand There: Do Something! The Case for Peri-Ictal Intervention

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Nocturnal supervision and SUDEP risk at different epilepsy care settings

Objective

To estimate the incidence of sudden unexpected death in epilepsy (SUDEP) in people with intellectual disabilities in residential care settings and to ascertain the effects of nocturnal seizures and nocturnal supervision on SUDEP risk.

Methods

We conducted a nested case-control study reviewing records of all people who died at 2 residential care settings over 25 years. Four controls per case were selected from the same population, matched on age (±5 years) and residential unit. Nocturnal supervision was graded in 3 categories: (1) no supervision; (2) a listening device or a roommate or physical checks at least every 15 minutes; and (3) 2 of the following: a listening device, roommate, additional device (bed motion sensor/video monitoring), or physical checks every 15 minutes. Outcome measures were compared using Mann-Whitney U tests and Fisher exact tests.

Results

We identified 60 SUDEP cases and 198 matched controls. People who died of SUDEP were more likely to have nocturnal convulsive seizures in general (77% of cases vs 33% of controls, p < 0.001) and a higher frequency of nocturnal convulsive seizures. Total SUDEP incidence was 3.53/1,000 patient-years (95% confidence interval [CI] 2.73–4.53). The incidence differed among centers: 2.21/1,000 patient-years (95% CI 1.49–3.27) vs 6.12/1,000 patient-years (95% CI 4.40–8.52). There was no significant difference in nocturnal supervision among cases and controls, but there was a difference among centers: the center with a lowest grade of supervision had the highest incidence of SUDEP.

Conclusions

Having nocturnal seizures, in particular convulsions, may increase SUDEP risk. Different levels of nocturnal supervision may account for some of the difference in incidence.

Unravelling the mysteries of sudden unexpected death in epilepsy

INTRODUCTION

Sudden unexpected death in epilepsy (SUDEP) is the most frequent cause of premature death in epileptic patients. Most SUDEP events occur at night and frequently go unnoticed; the exact pathophysiological mechanisms of this phenomenon therefore remain undetermined. Nevertheless, most cases of SUDEP are attributed to an infrequent yet extremely severe complication of epileptic seizures.

DEVELOPMENT

We conducted a systematic literature search on PubMed. Our review article summarises scientific evidence on the classification, pathophysiological mechanisms, risk factors, biomarkers, and prevention of SUDEP. Likewise, we propose new lines of research and critically analyse findings that are relevant to clinical practice.

CONCLUSIONS

Current knowledge suggests that SUDEP is a heterogeneous phenomenon caused by multiple factors. In most cases, however, SUDEP is thought to be due to postictal cardiorespiratory failure triggered by generalised tonic-clonic seizures and ultimately leading to cardiac arrest. The underlying pathophysiological mechanism involves multiple factors, ranging from genetic predisposition to environmental factors. Risk of SUDEP is higher in young adults with uncontrolled generalised tonic-clonic seizures. However, patients apparently at lower risk may also experience SUDEP. Current research focuses on identifying genetic and neuroimaging biomarkers that may help determine which patients are at high risk for SUDEP. Antiepileptic treatment is the only preventive measure proven effective to date. Night-time monitoring together with early resuscitation may reduce the risk of SUDEP.

Equivocal significance of post-ictal generalized EEG suppression as a marker of SUDEP risk

PURPOSE

Our objective was to determine the significance of PGES as a possible EEG marker of increased risk for SUDEP and explore factors that influence PGES.

METHODS

We identified 17 patients who died due to definite or probable SUDEP and 52 living control patients with drug resistant focal epilepsy who underwent EEG monitoring and least one seizure recorded on EEG. We reviewed 305 seizures on EEG and when available, on video, for presence or absence of PGES, the duration of PGES immediately after seizure end, seizure type, state seizure occurred (sleep vs. wake), tonic duration and time from seizure onset to initial nursing intervention. We noted that majority (93% in SUDEP group and 83% living controls) with PGES had additional brief bursts of suppression. We measured the time from the end of seizure to end of last brief suppression to determine the time to final PGES.

RESULTS

SUDEP patients had statistically significant shorter PGES duration compared to living controls (unadjusted: -32.8s, 95%CI[-54.5, -11.2], adjusted: -39.5s, 95% CI[-59.4, -19.6]). SUDEP status was associated with longer time to final PGES compare to living controls, but this was not statistically significant. Earlier nursing intervention was associated with shorter seizure duration. PGES occurred only after GCS. Time to nursing intervention, tonic duration or state did not have a statistically significant effect on PGES.

CONCLUSIONS

PGES is an equivocal marker of increased SUDEP risk. Earlier nursing intervention is associated with shorter seizure duration and may play a role in reducing risk of SUDEP.