Critique of the 2017 epileptic seizure and epilepsy classifications.

This article critiques the International League Against Epilepsy (ILAE) 2015-2017 classifications of epilepsy, epileptic seizures, and status epilepticus. It points out the following shortcomings of the ILAE classifications: (1) they mix semiological terms with epileptogenic zone terminology; (2) simple and widely accepted terminology has been replaced by complex terminology containing less information; (3) seizure evolution cannot be described in any detail; (4) in the four-level epilepsy classification, level two (epilepsy category) overlaps almost 100% with diagnostic level one (seizure type); and (5) the design of different classifications with distinct frameworks for newborns, adults, and patients in status epilepticus is confusing. The authors stress the importance of validating the new ILAE classifications and feel that the decision of Epilepsia to accept only manuscripts that use the ILAE classifications is premature and regrettable.

Complex epilepsy phenotype extraction from narrative clinical discharge summaries.

Epilepsy is a common serious neurological disorder with a complex set of possible phenotypes ranging from pathologic abnormalities to variations in electroencephalogram. This paper presents a system called Phenotype Exaction in Epilepsy (PEEP) for extracting complex epilepsy phenotypes and their correlated anatomical locations from clinical discharge summaries, a primary data source for this purpose. PEEP generates candidate phenotype and anatomical location pairs by embedding a named entity recognition method, based on the Epilepsy and Seizure Ontology, into the National Library of Medicine's MetaMap program. Such candidate pairs are further processed using a correlation algorithm. The derived phenotypes and correlated locations have been used for cohort identification with an integrated ontology-driven visual query interface. To evaluate the performance of PEEP, 400 de-identified discharge summaries were used for development and an additional 262 were used as test data. PEEP achieved a micro-averaged precision of 0.924, recall of 0.931, and F1-measure of 0.927 for extracting epilepsy phenotypes. The performance on the extraction of correlated phenotypes and anatomical locations shows a micro-averaged F1-measure of 0.856 (Precision: 0.852, Recall: 0.859). The evaluation demonstrates that PEEP is an effective approach to extracting complex epilepsy phenotypes for cohort identification.

Significant postictal hypotension: expanding the spectrum of seizure-induced autonomic dysregulation.

Periictal autonomic dysregulation is best studied using a "polygraphic" approach: electroencephalography ([EEG]), 3-channel electrocardiography [ECG], pulse oximetry, respiration, and continuous noninvasive blood pressure [BP]), which may help elucidate agonal pathophysiologic mechanisms leading to sudden unexpected death in epilepsy (SUDEP). A number of autonomic phenomena have been described in generalized tonic-clonic seizures (GTCS), the most common seizure type associated with SUDEP, including decreased heart rate variability, cardiac arrhythmias, and changes in skin conductance. Postictal generalized EEG suppression (PGES) has been identified as a potential risk marker of SUDEP, and PGES has been found to correlate with post-GTCS autonomic dysregulation in some patients. Herein, we describe a patient with a GTCS in whom polygraphic measurements were obtained, including continuous noninvasive blood pressure recordings. Significant postictal hypotension lasting >60 s was found, which closely correlated with PGES duration. Similar EEG changes are well described in hypotensive patients with vasovagal syncope and a similar vasodepressor phenomenon, and consequent cerebral hypoperfusion may account for the PGES observed in some patients after a GTCS. This further raises the possibility that profound, prolonged, and irrecoverable hypotension may comprise one potential SUDEP mechanism.

How do overweight people dropout of a weight loss diet? A qualitative study.

BACKGROUND: The growing trend of overweight and obesity in many developed and developing countries in recent years has made obesity one of the most significant health problems in the world. The treatment of overweight and obese people is challenging, as patients have difficulty adhering to a weight-loss diet. Thus, the present study aimed to identify the reasons for the dropout of weight-loss diets. METHODS: This qualitative study using content analysis was conducted in a comprehensive health center in Shiraz, southern Iran, between April and October 2020. The study was performed on 27 participants with a history of obesity and diet dropout selected via purposive and theoretical sampling. The data were gathered through semi-structured interviews and were thematically analyzed. RESULTS: The participants included 25 females (92.6%) and two males (7.4%) with a mean age of 33.4 ± 8.4 years. Data analysis resulted in the emergence of three themes and 14 sub-themes. The first theme was personal reasons for diet dropout, which included six sub-themes; i.e., misunderstanding of diet, not having enough motivation, stress and hormonal disorder, having the feel of "being harmful to health", lack of mental and psychological preparation, and personal taste. The second theme was familial and social reasons for diet dropout, including two sub-themes, i.e., social and familial problems. Finally, the third theme was the reasons related to diet characteristics, including six sub-themes: ineffectiveness of diet, expensiveness of diet food and dietary supplements, family problems, unavailability of food, unscientific and unconventional diets feeling bad about the diet, and unpalatable diet food. All the concepts were related to each other and resulted in a pattern revealing the experiences of overweight people and who had dropped out of weight-loss diets. CONCLUSION: The reasons for diet dropout were divided into three levels: personal reasons, familial and social reasons, and diet characteristics. Overall, clinicians should pay attention to the complexity of diets to increase the success rate of weight management. Based on the current study findings, a guideline is recommended to guide patients who dropout of weight-loss diets.

Autoimmune musicogenic epilepsy associated with anti-glutamic acid decarboxylase antibodies and Stiff-person syndrome.

Epilepsy should be suspected in patients with Stiff-person syndrome and new onset paroxysmal episodes. Musicogenic epilepsy may be a manifestation of anti-GAD-Ab spectrum, supporting an autoimmune workup in these patients. Appropriate treatment is not well established, and immunotherapy should be considered in patients with only partial response to antiepileptic drugs.

Could the 2017 ILAE and the four-dimensional epilepsy classifications be merged to a new "Integrated Epilepsy Classification"?

Over the last few decades the ILAE classifications for seizures and epilepsies (ILAE-EC) have been updated repeatedly to reflect the substantial progress that has been made in diagnosis and understanding of the etiology of epilepsies and seizures and to correct some of the shortcomings of the terminology used by the original taxonomy from the 1980s. However, these proposals have not been universally accepted or used in routine clinical practice. During the same period, a separate classification known as the "Four-dimensional epilepsy classification" (4D-EC) was developed which includes a seizure classification based exclusively on ictal symptomatology, which has been tested and adapted over the years. The extensive arguments for and against these two classification systems made in the past have mainly focused on the shortcomings of each system, presuming that they are incompatible. As a further more detailed discussion of the differences seemed relatively unproductive, we here review and assess the concordance between these two approaches that has evolved over time, to consider whether a classification incorporating the best aspects of the two approaches is feasible. To facilitate further discussion in this direction we outline a concrete proposal showing how such a compromise could be accomplished, the "Integrated Epilepsy Classification". This consists of five categories derived to different degrees from both of the classification systems: 1) a "Headline" summarizing localization and etiology for the less specialized users, 2) "Seizure type(s)", 3) "Epilepsy type" (focal, generalized or unknown allowing to add the epilepsy syndrome if available), 4) "Etiology", and 5) "Comorbidities & patient preferences".

Classification of paroxysmal events and the four-dimensional epilepsy classification system.

This educational review describes the classification of paroxysmal events and a four-dimensional epilepsy classification system. Paroxysmal events are classified as epileptic and non-epileptic paroxysmal events. Non-epileptic events are, in turn, classified as psychogenic and organic paroxysmal events. The following four dimensions are used to classify epileptic paroxysmal events: ictal semiology, the epileptogenic zone, etiology, and comorbidities. Efforts are made to keep these four dimensions as independent as possible. The review also includes 12 educational vignettes and three more detailed case reports classified using the 2017 classification of the ILAE and the four-dimensional epilepsy classification. In addition, a case is described which is classified using the four-dimensional epilepsy classification with different degrees of precision by an emergency department physician, a neurologist, and an epileptologist. [Published with video sequences on www.epilepticdisorders.com].

Bedside optical coherence tomography for Terson's syndrome screening in acute subarachnoid hemorrhage: a pilot study.

OBJECTIVE: Approximately 10% of patients with subarachnoid hemorrhage (SAH) become permanently, legally blind. The average cost of lifetime support and unpaid taxes for each blind person amounts to approximately $900,000. This study evaluates the feasibility and potential role of bedside optical coherence tomography (OCT) in Terson's syndrome (TS) in patients with acute SAH (aSAH) and its potential role in blindness prevention. METHODS: The authors conducted an open-label pilot study, in which 31 patients with an angiographic diagnosis of aSAH were first screened for TS with dilated funduscopy and then with OCT in the acute phase and at 6-week followup visits. Outpatient mood assessments (Patient Health Questionnaire­depression module, Hamilton Depression Scale), and quality of life general (NIH Patient-Reported Outcomes Measurement Information System) and visual scales (25-item National Eye Institute Visual Functioning Questionnaire) were measured at 1 and 6 weeks after discharge. Exclusion criteria included current or previous history of severe cataracts, severe diabetic retinopathy, severe macular degeneration, or glaucoma. RESULTS: OCT identified 7 patients with TS, i.e., a 22.6% incidence in our aSAH sample: 7 in the acute phase, including a large retinal detachment that was initially missed by funduscopy and diagnosed by OCT in follow-up clinic. Dilated retinal funduscopy significantly failed to detect TS in 4 (57.1%) of these 7 cases. Intraventricular hemorrhage was significantly more common in TS cases (85.7% vs 25%). None of the participants experienced any complications from OCT examinations. Neither decreased quality of life visual scale scores nor a depressed mood correlated with objective OCT pathological findings at the 6-week follow-up after discharge. There were no significant mood differences between TS cases and controls. CONCLUSIONS: OCT is the gold standard in retinal disease diagnosis. This pilot study shows that bedside OCT examination is feasible in aSAH. In this series, OCT was a safe procedure that enhanced TS detection by decreasing false-negative/inconclusive funduscopic examinations. It allows early diagnosis of macular holes and severe retinal detachments, which require acute surgical therapy to prevent legal blindness. In addition, OCT aids in ruling out potential false-positive visual deficits in individuals with a depressed mood at follow-up.

Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care.

OBJECTIVE: Epilepsy encompasses an extensive array of clinical and research subdomains, many of which emphasize multi-modal physiological measurements such as electroencephalography and neuroimaging. The integration of structured, unstructured, and signal data into a coherent structure for patient care as well as clinical research requires an effective informatics infrastructure that is underpinned by a formal domain ontology. METHODS: We have developed an epilepsy and seizure ontology (EpSO) using a four-dimensional epilepsy classification system that integrates the latest International League Against Epilepsy terminology recommendations and National Institute of Neurological Disorders and Stroke (NINDS) common data elements. It imports concepts from existing ontologies, including the Neural ElectroMagnetic Ontologies, and uses formal concept analysis to create a taxonomy of epilepsy syndromes based on their seizure semiology and anatomical location. RESULTS: EpSO is used in a suite of informatics tools for (a) patient data entry, (b) epilepsy focused clinical free text processing, and (c) patient cohort identification as part of the multi-center NINDS-funded study on sudden unexpected death in epilepsy. EpSO is available for download at http://prism.case.edu/prism/index.php/EpilepsyOntology. DISCUSSION: An epilepsy ontology consortium is being created for community-driven extension, review, and adoption of EpSO. We are in the process of submitting EpSO to the BioPortal repository. CONCLUSIONS: EpSO plays a critical role in informatics tools for epilepsy patient care and multi-center clinical research.

Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research.

OBJECTIVE: The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. MATERIALS AND METHODS: We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. RESULTS: Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. DISCUSSION: Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. CONCLUSION: The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.

Seizures, Cerebral Shutdown, and SUDEP.

Several potential pathophysiologic phenomena, including "cerebral shutdown," are postulated to be responsible for SUDEP. Since the evidence for a seizure-related mechanism is strong, a poor understanding of the physiology of human seizure termination is a major handicap. However, rather than a failure of a single homeostatic mechanism, such as postictal arousal, it may be a "perfect storm" created by the lining up of a several factors that lead to death.

Electrophysiological signal analysis and visualization using Cloudwave for epilepsy clinical research.

Epilepsy is the most common serious neurological disorder affecting 50-60 million persons worldwide. Electrophysiological data recordings, such as electroencephalogram (EEG), are the gold standard for diagnosis and pre-surgical evaluation in epilepsy patients. The increasing trend towards multi-center clinical studies require signal visualization and analysis tools to support real time interaction with signal data in a collaborative environment, which cannot be supported by traditional desktop-based standalone applications. As part of the Prevention and Risk Identification of SUDEP Mortality (PRISM) project, we have developed a Web-based electrophysiology data visualization and analysis platform called Cloudwave using highly scalable open source cloud computing infrastructure. Cloudwave is integrated with the PRISM patient cohort identification tool called MEDCIS (Multi-modality Epilepsy Data Capture and Integration System). The Epilepsy and Seizure Ontology (EpSO) underpins both Cloudwave and MEDCIS to support query composition and result retrieval. Cloudwave is being used by clinicians and research staff at the University Hospital - Case Medical Center (UH-CMC) Epilepsy Monitoring Unit (EMU) and will be progressively deployed at four EMUs in the United States and the United Kingdomas part of the PRISM project.

Cloudwave: distributed processing of "big data" from electrophysiological recordings for epilepsy clinical research using Hadoop.

Epilepsy is the most common serious neurological disorder affecting 50-60 million persons worldwide. Multi-modal electrophysiological data, such as electroencephalography (EEG) and electrocardiography (EKG), are central to effective patient care and clinical research in epilepsy. Electrophysiological data is an example of clinical "big data" consisting of more than 100 multi-channel signals with recordings from each patient generating 5-10GB of data. Current approaches to store and analyze signal data using standalone tools, such as Nihon Kohden neurology software, are inadequate to meet the growing volume of data and the need for supporting multi-center collaborative studies with real time and interactive access. We introduce the Cloudwave platform in this paper that features a Web-based intuitive signal analysis interface integrated with a Hadoop-based data processing module implemented on clinical data stored in a "private cloud". Cloudwave has been developed as part of the National Institute of Neurological Disorders and Strokes (NINDS) funded multi-center Prevention and Risk Identification of SUDEP Mortality (PRISM) project. The Cloudwave visualization interface provides real-time rendering of multi-modal signals with "montages" for EEG feature characterization over 2TB of patient data generated at the Case University Hospital Epilepsy Monitoring Unit. Results from performance evaluation of the Cloudwave Hadoop data processing module demonstrate one order of magnitude improvement in performance over 77GB of patient data. (Cloudwave project: http://prism.case.edu/prism/index.php/Cloudwave).

EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification.

Sudden Unexpected Death in Epilepsy (SUDEP) is a poorly understood phenomenon. Patient cohorts to power statistical studies in SUDEP need to be drawn from multiple centers due to the low rate of reported SUDEP incidences. But the current practice of manual chart review of Epilepsy Monitoring Units (EMU) patient discharge summaries is time-consuming, tedious, and not scalable for large studies. To address this challenge in the multi-center NIH-funded Prevention and Risk Identification of SUDEP Mortality (PRISM) Project, we have developed the Epilepsy Data Extraction and Annotation (EpiDEA) system for effective processing of discharge summaries. EpiDEA uses a novel Epilepsy and Seizure Ontology (EpSO), which has been developed based on the International League Against Epilepsy (ILAE) classification system, as the core knowledge resource. By extending the cTAKES natural language processing tool developed at the Mayo Clinic, EpiDEA implements specialized functions to address the unique challenges of processing epilepsy and seizure-related clinical free text in discharge summaries. The EpiDEA system was evaluated on a corpus of 104 discharge summaries from the University Hospitals Case Medical Center EMU and achieved an overall precision of 93.59% and recall of 84.01% with an F-measure of 88.53%. The results were compared against a gold standard created by two epileptologists. We demonstrate the use of EpiDEA for cohort identification through use of an intuitive visual query interface that can be directly used by clinical researchers.

OPIC: Ontology-driven Patient Information Capturing system for epilepsy.

The widespread use of paper or document-based forms for capturing patient information in various clinical settings, for example in epilepsy centers, is a critical barrier for large-scale, multi-center research studies that require interoperable, consistent, and error-free data collection. This challenge can be addressed by a web-accessible and flexible patient data capture system that is supported by a common terminological system to facilitate data re-usability, sharing, and integration. We present OPIC, an Ontology-driven Patient Information Capture (OPIC) system that uses a domain-specific epilepsy and seizure ontology (EpSO) to (1) support structured entry of multi-modal epilepsy data, (2) proactively ensure quality of data through use of ontology terms in drop-down menus, and (3) identify and index clinically relevant ontology terms in free-text fields to improve accuracy of subsequent analytical queries (e.g. cohort identification). EpSO, modeled using the Web Ontology Language (OWL), conforms to the recommendations of the International League Against Epilepsy (ILAE) classification and terminological commission. OPIC has been developed using agile software engineering methodology for rapid development cycles in close collaboration with domain expert and end users. We report the result from the initial deployment of OPIC at the University Hospitals Case Medical Center (UH CMC) epilepsy monitoring unit (EMU) as part of the NIH-funded project on Sudden Unexpected Death in Epilepsy (SUDEP). Preliminary user evaluation shows that OPIC has achieved its design objectives to be an intuitive patient information capturing system that also reduces the potential for data entry errors and variability in use of epilepsy terms.