A5024832606- EEG and Brain-Computer Interfaces
- Neurological disorders and treatments
- Epilepsy research and treatment
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Neuroscience and Neural Engineering
- Sleep and Wakefulness Research
- Neonatal and fetal brain pathology
- Infant Health and Development
- Autism Spectrum Disorder Research
- Muscle activation and electromyography studies
- Advanced Control Systems Design
- Machine Learning in Bioinformatics
- Neuroscience and Neuropharmacology Research
- Neuroscience of respiration and sleep
- Advanced Memory and Neural Computing
- Blind Source Separation Techniques
- Cerebrospinal fluid and hydrocephalus
WinnMed
2019-2024
St. Anne's University Hospital Brno
2018-2024
University Hospital Brno
2018-2024
Mayo Clinic
2019-2024
Czech Technical University in Prague
2021-2024
BioElectronics (United States)
2021-2024
International Clinical Research Center, St. Anne's University Hospital Brno
2024
Mayo Clinic in Arizona
2022-2023
Mayo Clinic in Florida
2022
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...
This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The was tested on hand-held device (Mayo Epilepsy Assist Device) in pseudo-prospective mode iEEG from four canines with naturally occurring epilepsy.The trained and 75 collected over 1608 d utilizing genetic algorithm to optimize hyper-parameters (prediction horizon (PH), median filter window length, probability...
Abstract Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing stimulation devices. Two pet the subject received concurrent thalamic deep (DBS) over multiple months. All subjects circadian cycles in rate of interictal epileptiform spikes (IES)....
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...
Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people epilepsy, and DBS may actually further disturb normal sleep patterns quality. Novel implantable devices capable of streaming continuous intracranial electroencephalography (iEEG) signals enable detailed assessments therapy efficacy tracking related comorbidities. Here, we investigate the feasibility automated classification using iEEG data...
Abstract There is a paucity of data to guide anterior nucleus the thalamus (ANT) deep brain stimulation (DBS) with sensing. The clinical Medtronic Percept DBS device provides constrained sensing power within frequency band (power‐in‐band [PIB]), recorded in 10‐min averaged increments. Here, four patients temporal lobe epilepsy were implanted an investigational providing full bandwidth chronic intracranial electroencephalogram (cEEG) from bilateral ANT and hippocampus (Hc). PIB‐based seizure...
Abstract Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that often associated with human bias requires trained electrophysiology experts specific domain knowledge. This challenge now compounded by development measurement technologies devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool...
Abstract Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures. These seizures often originate from limbic networks and people also experience chronic comorbidities related to memory, mood, sleep (MMS). Deep brain stimulation targeting the anterior nucleus of thalamus (ANT-DBS) proven therapy, but optimal parameters remain unclear. We developed neurotechnology platform for tracking MMS enable data streaming between an investigational sensing-stimulation...
Bidirectional interactions between sleep, seizures, and epilepsy remain incompletely understood. Evidence from animal models people with focal suggest that seizures may engage mechanisms of memory consolidation to reinforce strengthen synaptic connections within the pathological networks generates termed seizure-related (SRC). Human studies SRC, however, are limited by small sample size restricted observations post-ictal sleep. Using continuous local field potential (LFP) recordings novel...
Abstract Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures that often originate within limbic networks involving amygdala and hippocampus. The network involved in crucial physiologic functions memory, emotion sleep. frequently drug-resistant, people experience comorbidities related to mood Deep brain stimulation targeting the anterior nucleus of thalamus (ANT-DBS) an established therapy for temporal epilepsy. However, optimal parameters their impact...
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...
The impedance is a fundamental electrical property of brain tissue, playing crucial role in shaping the characteristics local field potentials, extent ephaptic coupling, and volume tissue activated by externally applied stimulation. We tracked impedance, sleep–wake behavioral state, epileptiform activity five people with epilepsy living their natural environment using an investigational device. study identified oscillations that span hours to weeks amygdala, hippocampus, anterior nucleus...
Abstract Objective. This study aims to characterize the time course of impedance, a crucial electrophysiological property brain tissue, in human thalamus (THL), amygdala-hippocampus, and posterior hippocampus over an extended period. Approach. Impedance was periodically sampled every 5–15 min several months five subjects with drug-resistant epilepsy using investigational neuromodulation device. Initially, we employed descriptive piecewise continuous mathematical models impedance response for...
Abstract EEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of into physiological, pathological, or artifacts has been performed by expert visual review recordings. However, size data recordings rapidly increasing with trend higher channel counts, greater sampling frequency, longer recording duration complete reliance on not sustainable. In this study, we publicly share annotated intracranial clips from...
Epilepsy patients often experience acute repetitive seizures, known as seizure clusters, which can progress to prolonged seizures or status epilepticus if left untreated. Predicting the onset of clusters is crucial enable receive preventative treatments. Additionally, studying patterns help predict type (isolated cluster) after observing a just occurred seizure. This paper presents machine learning models that use bivariate intracranial EEG (iEEG) features clustering. Specifically, we...
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 High frequency anterior nucleus of the thalamus deep brain stimulation (ANT DBS) is an established therapy for treatment resistant focal epilepsies. Although high frequency-ANT DBS well tolerated, patients are rarely seizure free and efficacy other parameters their impact on comorbidities epilepsy such as depression memory dysfunction remain unclear. The purpose this study was to assess low vs ANT verbal self-reported anxiety symptoms. Five with temporal lobe were implanted...
The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification EEG as showing normal physiological or abnormal pathological activity has been performed by expert visual review. potential value unbiased, automated long recognized, in recent years application machine learning methods received significant attention. A variety solutions using convolutional neural networks (CNN) for have emerged with impressive results....
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...
Abstract Objective. Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function. Approach. Here we develop validate an automated iEEG-based sleep–wake classifier canines using expert sleep labels derived from simultaneous video, accelerometry, scalp (EEG) iEEG monitoring. The EEG, accelerometry recordings were manually scored by a...
Verbal memory impairment is a common symptom of temporal lobe epilepsy (TLE). The ability to encode and recall verbal memories can be probed with the classic free task. We hypothesized that increased seizure frequency will lead decreased memory. investigated this hypothesis in patients receiving chronic stimulation bilateral anterior nuclei thalamus (ANT) deep brain (DBS). Recordings were obtained from individuals drug resistant mesial TLE implanted an investigational Medtronic Summit RC+S™...
Abstract Objective Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people epilepsy, and DBS may actually further disturb normal sleep patterns quality. Novel devices capable of continuous intracranial EEG (iEEG) telemetry enable detailed assessments therapy efficacy tracking related comorbidities. Here, we investigate the feasibility automated classification using iEEG data recorded from Papez’s...