A5057668265- EEG and Brain-Computer Interfaces
- Epilepsy research and treatment
- Blind Source Separation Techniques
- Cardiovascular Syncope and Autonomic Disorders
- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Bacterial Infections and Vaccines
- Atomic and Subatomic Physics Research
- Muscle activation and electromyography studies
- Advanced MRI Techniques and Applications
- ECG Monitoring and Analysis
- Transcranial Magnetic Stimulation Studies
- Cardiac electrophysiology and arrhythmias
- Psychosomatic Disorders and Their Treatments
- Neuroscience and Neuropharmacology Research
- Tactile and Sensory Interactions
- Cardiac Arrhythmias and Treatments
- Humor Studies and Applications
- Pharmacological Effects and Toxicity Studies
- Respiratory and Cough-Related Research
- Infant Health and Development
- Neurological disorders and treatments
Thomas Jefferson University Hospital
2006-2025
Jefferson Hospital for Neuroscience
2024
Thomas Jefferson University
2016-2017
Epilepsy Foundation
2007
In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System builds mathematical models from time series signal and uses small number of parameters to represent the entirety domain epochs. Such were used as features classifiers in our study. We analyzed 69 55 non-seizure recordings an additional 10 continuous Thomas Jefferson University Hospital, alongside larger dataset CHB-MIT database. By dividing...
Summary Objective: To establish the efficacy and safety of low‐frequency electrical stimulation for cortical brain mapping. Methods: Cortical function was mapped using in epilepsy patients with chronically implanted intracranial subdural electrodes. Contacts overlying motor, sensory, visual, language cortex were stimulated at frequencies 5, 10, 50 Hz, current levels ranging from 1 to 17.5 mA 3–5 s. The intensity incidence which functional alterations afterdischarges (ADs) occurred recorded....
Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, single routine EEG limited in its diagnostic value. Only small percentage of EEGs show interictal epileptiform discharges (IEDs) and overall misdiagnosis rates epilepsy are 20% to 30%. We aim demonstrate how network properties recordings can be used improve the speed accuracy differentiating from mimics, such as functional seizures - even absence IEDs. In this multicenter study, we analyzed 218 patients with...
Abstract Objective While scalp EEG is important for diagnosing epilepsy, a single routine limited in its diagnostic value. Only small percentage of EEGs show interictal epileptiform discharges (IEDs) and overall misdiagnosis rates epilepsy are 20-30%. We aim to demonstrate how analyzing network properties recordings can be used improve the speed accuracy diagnosis - even absence IEDs. Methods In this multicenter study, we analyzed from 198 patients with suspected normal initial EEGs. The...
<ns4:p><ns4:italic>Objective:</ns4:italic> To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this on the basis its performance in comparison to commercially available (AR1) accurately depict seizure-onset location.</ns4:p><ns4:p> <ns4:italic>Methods:</ns4:italic> A blinded investigation used 23 EEG recordings seizures from 8 patients. Each recording was uninterpretable with digital filtering because artifact...
<ns4:p><ns4:italic>Objective:</ns4:italic> To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this on the basis its performance in comparison to commercially available (AR1) accurately depict seizure-onset location.</ns4:p><ns4:p> <ns4:italic>Methods:</ns4:italic> A blinded investigation used 23 EEG recordings seizures from 8 patients. Each recording was uninterpretable with digital filtering because artifact...