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Objective
Clinical decisions on managing epilepsy patients rely on patient accuracy regarding seizure reporting. Studies have noted disparities between patient‐reported seizures and electroencephalographic (EEG) findings during video‐EEG monitoring periods, chiefly highlighting underreporting of seizures, a well‐recognized phenomenon. However, seizure overreporting is a significant problem discussed...
This study describes a generalized cross‐patient seizure‐forecasting approach using recurrent neural networks with ultra‐long‐term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short‐term memory (LSTM) deep‐learning classifiers. Electrographic...
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration,...
Objective
One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient‐specific...
Objective
The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified...
Objective
Emerging evidence has shown that ambient air pollution affects brain health, but little is known about its effect on epileptic seizures. This work aimed to assess the association between daily exposure to ambient air pollution and the risk of epileptic seizures.
Methods
This study used epileptic seizure data from two independent data sources (NeuroVista and Seer App seizure diary). In...
Objective
Ultra long‐term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long‐term quality and consistency of the sqEEG signal, which is the objective of this study.
Methods
The largest multicenter cohort of sqEEG was analyzed, including...
Objective
Video‐electroencephalography (vEEG) is an important component of epilepsy diagnosis and management. Nevertheless, inpatient vEEG monitoring fails to capture seizures in up to one third of patients. We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG.
Methods
We used a database of ambulatory vEEG studies to select a cohort with linked electronic...
Objective
Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure...
Brain-computer interfaces are commonly proposed to assist individuals with locked-in syndrome to interact with the world around them. In this paper, we present a pipeline to move from recorded brain signals to real-time classification on a low-power platform, such as IBM's TrueNorth Neurosynaptic System. Our results on a EEG-based hand squeeze task show that using a convolutional neural network and...
Objective
We report on temporally clustered seizures detected from continuous long‐term ambulatory human electroencephalographic data. The objective was to investigate short‐term seizure clustering, which we have termed bursting, and consider implications for patient care, seizure prediction, and evaluating therapies.
Methods
Chronic ambulatory intracranial electroencephalography (EEG) data collected...
Directional tuning is the tendency for cortical neurons to exhibit a peak firing rate when a limb is moved in a preferred direction. This phenomenon has been used to underpin decoding strategies in many brain-machine interface (BMI) systems. Although it is well established that individual motor neurons can be decoded using directional tuning, this is not as well understood at the scale of cortical...
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