View clinical trials related to Seizures.
Filter by:The proper period of anti-seizure medication (ASM) treamtment is important for decreasing side effect of ASM and recurrence of seizure. We evaluate reliable risk factor analysis for safe withdrawal of ASM in children with epilepsy. Futhermore, we develop the scoring system for prediction of seizure recurrence to set the standard for safe withdrawal of ASM.
The goal of this clinical trial is to validate a wearable seizure monitoring device, EpiCare@Home, as an objective seizure monitoring tool for people with focal onset epileptic seizures. The device continuously records brain, cardiorespiratory, and physical activity data. The study aims to 1) collect benchmark data for seizure detection algorithm development and validation, and 2) evaluate the performance of the device in clinical and at home workflows. Participants will wear the device during a routine Epilepsy Monitoring Unit (EMU) admission. Additionally, they can continue wearing the device at home after the EMU admission.
Breath and sweat samples will be collected from people who have been admitted to hospital after a potential seizure and analysed by the team. The researchers then hope to identify a pattern of small molecules that can distinguish seizures from other events, and perhaps determine the severity of the seizure.
One in 10 people have a seizure during their life. Usually no cause is identified. Seizures without an identified cause are called unprovoked first seizure (UFS). Most people with UFS do not have further seizures. Being able to predict the risk of more seizures as soon as possible would help doctors decide whether to suggest treatment after UFS. Studies show that seizures are associated with changes in brain structure and function that are difficult to detect with standard assessments but can be detected with advanced techniques. Changes in connections between brain regions are also linked to subtle problems in thinking and mood. The investigators will examine brain connections using detailed brain scans, thinking, and mood in people with UFS and develop an accurate method for calculating the risk of further seizures. 200 adult patients and 75 matched healthy controls from the Halifax and Kingston First Seizure Clinics will undergo cognitive screening assessment of major cognitive domains, MRI imaging including structural scans, resting-state functional MRI (rsfMRI) and diffusion-weighted imaging (DWI), and EEG. Seizure recurrence will be assessed prospectively and a multimodal machine learning model will be trained to predict seizure recurrence at 12 months.
The X-TOLE3 Phase 3 clinical trial is a randomized, double-blind, placebo-controlled study that will evaluate the clinical efficacy, safety and tolerability of XEN1101 administered as adjunctive therapy in focal-onset seizures.
This study aims to collect data in newly implanted cochlear implant-recipients to inform future development of fitting methods to optimally and efficiently program a cochlear implant.
The present study investigates CI users' potential differences in speech tests, other performance measures (i.e. pitch-matching, perception of timbre and melodic intervals, consonance perception), and patient-reported outcome (i.e. questionnaires) between the clinical fitting map and anatomy-based fitting in two groups of CI users (one with standard fitting and one with anatomy-based fitting).
This is a Phase 3, multicenter, randomized, double-blind, placebo-controlled study to evaluate the clinical efficacy, safety, and tolerability of XEN1101 administered as adjunctive treatment in primary generalized tonic-clonic seizures (PGTCS).
Epilepsy is the 3rd neurological pathology after migraines and dementia syndromes with a high estimate of nearly 600,000 people affected in France. The disease is characterized by the repetition of epileptic seizures on the one hand, but also by the cognitive, behavioral, psychological and social consequences of this condition, especially when the epileptic disease is not stabilized. Epileptic patients feel a great deal of stress due to the unpredictability of the occurrence of seizures. Seizure detection is of great interest to bioinformatics researchers and to people with epilepsy and their caregivers. Recent advances in physiological sensor technologies and artificial intelligence have opened the possibility of developing systems capable of closely monitoring the frequency of epileptic seizures with a direct impact on therapeutic adaptations. This may eventually allow for seizure prediction and/or "seizure weather" (i.e., seizure forecasting) if there is a particular chronotype of seizure occurrence for a given individual. Currently, few devices have a sufficient level of evidence regarding their effectiveness to be recommended. Those that seem to be the most advanced are those that allow the identification of hypermotor seizures, including tonic-clonic generalized seizures and tonic-clonic secondary generalized focal seizures, mostly occurring at night. The latter represent only a small part of epileptic seizures. The objective of the present study is to build a real life database in order to develop a seizure detection algorithm. The recorded data will be heart rate via ECG and movement data via 9 variables measured on 3 axes x, y, z, with 3 sensors: accelerometer, gyroscope, magnetometer. These data will be collected using a connected patch available on the market (CE marking). At the same time, the patients will benefit from a long term video-EEG examination which will be annotated by the doctors and will be used as a gold standard for the identification of seizures in order to train the algorithm. This more complete base will be used to develop an algorithm previously developed from retrospective data.
Adjusting hearing aid user's real ear performance by using probe-microphone technology (real ear measurement, REM) has been a well-known procedure that verifies whether the output of the hearing aid at the eardrum matches the desired prescribed target. Still less than half of audiologists verify hearing aid fitting to match the prescribed target amplification with this technology. Recent studies have demonstrated failures to match the prescribed amplification targets, using exclusively the predictions of the proprietary software. American Speech-Language-Hearing Association (ASHA) and American Academy of Audiology (AAA) have created Best Practice Guidelines that recommend using real-ear measurement (REM) over initial fit approach and also the recent ISO 21388:2020 on hearing aid fitting management recommends the routine use of REM. Still audiologists prefer to rely on the manufacturer's default "first-fit" settings because of the lack of proof over cost-effectiveness and patient outcome in using REM. There are only few publications of varying levels of evidence indicating benefits of REM-fitted hearing aids with respect to patient outcomes that include self-reported listening ability, speech intelligibility in quiet and noise and patients' preference. Our main research question is whether REM-based fitting improves the patient reported outcome measures - PROMs (SSQ, HERE) and performance-based outcome measures (speech-reception threshold in noise) over initial fit approach. An additional research question is whether REM-based fitting improves hearing aid usage (self-reported & log-data report). Eventually, the investigators will calculate the cost-effectiveness of REM-based fitting.