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Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer‐aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both substantially higher sensitivity and negative predictive...
Objective
Elderly onset epilepsy represents a distinct subpopulation that has received considerable attention due to the unique features of the disease in this age group. Research into this particular patient group has been limited by a lack of a standardized definition and understanding of the attributes associated with elderly onset epilepsy.
Methods
We used a prospective cohort database to examine...
Objective
New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist‐worn convulsive seizure detectors.
Methods
Hand‐annotated video‐electroencephalographic seizure...
Objective
Low‐cost evidence‐based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities...
Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30–40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging,...
Objective
Among children with epilepsy, to develop and evaluate a model to predict emergency department (ED) use, an indicator of poor disease control and/or poor access to care.
Methods
We used electronic health record data from 2013 to predict ED use in 2014 at 2 centers, benchmarking predictive performance against machine learning algorithms. We evaluated algorithms by calculating the expected...
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of...
Objective
Interictal epileptiform anomalies such as epileptiform discharges or high‐frequency oscillations show marked variations across the sleep‐wake cycle. This study investigates which state of vigilance is the best to localize the epileptogenic zone (EZ) in interictal intracranial electroencephalography (EEG).
Methods
Thirty patients with drug‐resistant epilepsy undergoing stereo‐EEG (SEEG)/sleep...
Objective
To find the covert patterns of abnormality in patients with unilateral temporal lobe epilepsy (TLE) and visually normal brain magnetic resonance images (MRI‐negative), comparing them to those with visible abnormalities (MRI‐positive).
Methods
We used multimodal brain MRI from patients with unilateral TLE and employed contemporary machine learning methods to predict the known laterality...
Objective
Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores.
Methods
The application was trained on notes from (1) patients with a diagnosis of epilepsy and a history of resective epilepsy...
Objective
Digital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal clinical settings and help generate useful information for treatment planning. Our study aimed to explore the big data from open‐source digital conversations among PWE with regard to suicidality, specifically comparing teenagers and adults, using machine...
Objective
To apply unsupervised machine learning to patient‐reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics.
Methods
Consecutive outpatients seen at the Calgary Comprehensive Epilepsy Program outpatient clinics with complete patient‐reported outcome measures on quality of life, health state valuation, depression, and epilepsy severity and...
Objective
Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed‐loop stimulation or optogenetic control of seizures. It is also of increased importance in high‐throughput, robust, and reproducible pre‐clinical research. However, seizure detectors are not widely relied...
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy,...
Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various...
Precision medicine can be distilled into a concept of accounting for an individual’s unique collection of clinical, physiologic, genetic, and sociodemographic characteristics to provide patient‐level predictions of disease course and response to therapy. Abundant evidence now allows us to determine how an average person with epilepsy will respond to specific medical and surgical treatments. This is...
Objective
To use clinically informed machine learning to derive prediction models for early and late premature death in epilepsy.
Methods
This was a population‐based primary care observational cohort study. All patients meeting a case definition for incident epilepsy in the Health Improvement Network database for inclusive years 2000‐2012 were included. A modified Delphi process identified 30 potential...
A reliable identification of a high‐risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty‐four patients with drug‐resistant epilepsy were admitted for continuous video‐electroencephalographic monitoring and filled out a daily...
Objective
Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long‐term ambulatory monitoring. This study evaluates the seizure detection performance of custom‐developed machine learning (ML) algorithms...
Objective
The 19‐item Epilepsy Surgery Satisfaction Questionnaire (ESSQ‐19) is a validated and reliable post hoc means of assessing patient satisfaction with epilepsy surgery. Prediction models building on these data can be used to counsel patients.
Methods
The ESSQ‐19 was derived and validated on 229 patients recruited from Canada and Sweden. We isolated 201 (88%) patients with complete clinical...
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