A5080726390- EEG and Brain-Computer Interfaces
- Epilepsy research and treatment
- Functional Brain Connectivity Studies
- Neurological disorders and treatments
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Heart Rate Variability and Autonomic Control
- Neuroscience and Neuropharmacology Research
- Infant Health and Development
- Autism Spectrum Disorder Research
- Sleep and Wakefulness Research
- Stock Market Forecasting Methods
- Neonatal and fetal brain pathology
- ECG Monitoring and Analysis
- Non-Invasive Vital Sign Monitoring
- Phonocardiography and Auscultation Techniques
Mayo Clinic
2020-2023
BioElectronics (United States)
2020-2023
WinnMed
2020-2023
Mayo Clinic in Arizona
2022
Mayo Clinic in Florida
2018
Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, essential tremor have FDA indications electrical brain using intracranially implanted electrodes. Interfacing implantable devices with local cloud computing resources the potential to improve efficacy, disease tracking, management. Epilepsy, in particular, is that might benefit from integration implants off-the-body tracking therapy. Recent...
Noninvasive wearable devices have great potential to aid the management of epilepsy, but these must robust signal quality, and patients be willing wear them for long periods time. Automated machine learning classification biosensor signals requires quantitative measures quality automatically reject poor-quality or corrupt data segments. In this study, commercially available sensors were placed on with epilepsy undergoing in-hospital in-home electroencephalographic (EEG) monitoring, healthy...
The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated but need be feasible accessible in long-term. Wearable devices are perfect candidates develop non-invasive, forecasts yet investigated long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established...
Abstract The ability to forecast seizures minutes hours in advance of an event has been verified using invasive EEG devices, but not previously demonstrated noninvasive wearable devices over long durations ambulatory setting. In this study we developed a seizure forecasting system with short-term memory (LSTM) recurrent neural network (RNN) algorithm, wrist-worn research-grade physiological sensor device, and tested the patients epilepsy field, concurrent confirmation via implanted recording...
The factors that influence seizure timing are poorly understood, and unpredictability remains a major cause of disability. Work in chronobiology has shown cyclical physiological phenomena ubiquitous, with daily multiday cycles evident immune, endocrine, metabolic, neurological, cardiovascular function. Additionally, work chronic brain recordings identified risk is linked to activity. Here, we provide the first characterization relationships between modulation diverse set signals, activity,...
Abstract Objective . The detection of seizures using wearable devices would improve epilepsy management, but reliable in an ambulatory environment remains challenging, and current studies lack concurrent validation electroencephalography (EEG) data. Approach An adaptively trained long–short-term memory deep neural network was developed a modest number seizure data sets from wrist-worn devices. Transfer learning used to adapt classifier that initially on intracranial (iEEG) signals facilitate...
One of the most disabling aspects living with chronic epilepsy is unpredictability seizures. Cumulative research in past decades has advanced our understanding dynamics seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed assess whether patient-specific forecasting using remote, minimally invasive ultra-long-term subcutaneous EEG.
Abstract Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent provide sensing but have not yet developed analytics accurately tracking and quantifying behaviour seizures. Here we describe a distributed co-processor providing intuitive bi-directional between patient, implanted neural device, local computing resources. Automated analysis of continuous...
Epilepsy is one of the most common neurological disorders, and it affects almost 1% population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, neuromodulation therapy. The unpredictability disabling aspects epilepsy. Furthermore, associated sleep, cognitive, psychiatric comorbidities, which significantly impact quality life. Seizure predictions could potentially be used adjust therapy prevent onset a...
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 refractory epilepsy and monitored an sqEEG device were used to develop algorithm for seizure forecasting long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures identified by board-certified epileptologist. One-minute data segments labeled as preictal or interictal...
Stimulation-evoked signals are starting to be used as biomarkers indicate the state and health of brain networks. The human limbic network, often targeted for stimulation therapy, is involved in emotion memory processing. Previous anatomic, neurophysiological, functional studies suggest distinct subsystems within network (Rolls, 2015). Studies using intracranial electrical stimulation, however, have emphasized similarities evoked waveforms across network. We test whether these...
Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid disorders are very common in drug-resistant their added complexity warrants careful consideration. In this review, we first discuss comorbidities symptoms epilepsy. We describe how can potentially impact patient presentation these factors be addressed experimental designs studies focused on...
Abstract Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal (based on expert visual review) recorded from patient with epilepsy can cause delays care delivery. Here, we investigated whether EEGs might contain subtle electrophysiological clues Specifically, (i) there are indicators abnormal brain electrophysiology patients, (ii) such abnormalities modulated by side generating seizures focal We analysed awake recordings age-matched groups 144...
Abstract Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioral inputs from patients. Recent provide sensing but have not yet developed analytics accurately tracking and quantifying behavior seizures. Here we describe a distributed co-processor providing intuitive bi-directional between patient, implanted neural device, local computing resources. Automated analysis of continuous...
<h3>Objective:</h3> To develop the ability to forecast seizures in patients with epilepsy without intracranial devices. <h3>Background:</h3> Seizure forecasting has been established using EEG, but minimally invasive devices may permit seizure and provide accurate records. <h3>Design/Methods:</h3> Patients were recruited for ultra-long monitoring a wearable device (Empatica E4, Fitbit Charge HR, or Inspire) concurrent ambulatory EEG (UNEEG SubQ, EpiMinder, NeuroPace RNS) at three sites....
Abstract The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated but need be feasible accessible in long-term. Wearable devices are perfect candidates develop non-invasive, forecasts yet investigated long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for...
Seizure forecasting is a great research interest due to its potential in helping patients manage activities or facilitate targeted therapies, specifically with the emergence of new subcutaneous continuous EEG recording systems that have shown promise be helpful. In work presented here, we used one subject diagnosed refractory epilepsy 230 days monitoring evaluate seven architectures design seizure prediction algorithm using deep learning RNN classifier. The preliminary results suggest it...
Abstract Objective Seizure unpredictability is a major source of disability for people with epilepsy. Recent work using chronic brain recordings has established that many individuals epilepsy seizure risk not random, but corresponds to circadian and multiday (multidien) cycles in excitability. Here, we aimed evaluate whether multimodal wearable device can characterize risk, compare wearables performance concurrent recordings. Methods Fourteen subjects underwent long-term ambulatory...
Standardizing terminology to describe electrophysiological events can improve both clinical care and computational research. Sharing data enriched by such standardized support advances in neuroscientific exploration, from single-subject mega-analysis. Machine readability of event annotations is essential for performing analyses efficiently across software tools packages. Hierarchical Event Descriptors (HED) provide a framework describing neuroscience experiments. HED library schemas extend...
Abstract Stimulation-evoked signals are starting to be used as biomarkers indicate the state and health of brain networks. The human limbic network, often targeted for stimulation therapy, is involved in emotion memory processing. Previous anatomical, neurophysiological functional studies suggest distinct subsystems within network (Rolls, 2015). using intracranial electrical stimulation, however, have emphasized similarities evoked waveforms across network. We test whether these...
Sunday, April 26April 14, 2020Free AccessSleep Dependent Memory Consolidation in People With Epilepsy (5469)Fatemeh Khadjevand, Tal Pal Attia, Laura Miller, Eric S.T. Louis, and Gregory WorrellAuthors Info & AffiliationsApril 2020 issue94 (15_supplement)https://doi.org/10.1212/WNL.94.15_supplement.5469 Letters to the Editor
To develop the ability to forecast seizures in patients with epilepsy without invasive intracranial devices.