Seizures Detection in Real Life Setting
- Conditions
- Epilepsy; SeizureFocal Epilepsy
- Interventions
- Device: Wearable, non invasive sensor for seizure detection
- Registration Number
- NCT05635396
- Lead Sponsor
- Reliev Technologies
- Brief Summary
Epilepsy is a disabling neurological disease that affects tens of millions of people worldwide. Despite therapeutic advances, about a third of these patients suffer from treatment-resistant forms of epilepsy and still experience regular seizures.All seizures can last and lead to status epilepticus, which is a major neurological emergency. Epilepsy can also be accompanied with cognitive or psychiatric comorbidities.
Reliable seizures count is an essential indicator for estimating the care quality and for optimizing treatment. Several studies have highlighted the difficulty for patients to keep a reliable seizure diary due for example to memory loss or perception alterations during crisis. Whatever the reasons, it has been observed that at least 50% of seizures are on average missed by patients.
Seizure detection has been widely developed in recent decades and are generally based on physiological signs monitoring associated with biomarkers search and coupled with detection algorithms. Multimodal approaches, i.e. combining several sensors at the same time, are considered the most promising.
Mobile or wearable non invasive devices, allowing an objective seizures documentation in daily life activities, appear to be of major interest for patients and care givers, in detecting and anticipating seizures occurence.
This single-arm exploratory, multicenter study aims at assessing whether the use of such a non-invasive, wearable device can be useful in a real life setting in detecting seizures occurence through multimodal analysis of various parameters (heart rate, respiratory and accelerometry).
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 12
- Patients aged 7 years old or more
- Patients with drug-resistant focal epilepsy
- Patients with high frequency seizures according to investigator's judgement
- Patients that can be followed 4 weeks after inclusion
- Informed consent form signed.
- Generalised tonic-clonic seizures
- Frequent psychogenic non-epileptic seizures
- Pregnant or breastfeeding patients
- Patients displaying sensor contraindications
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description Wearable, non invasive sensor for vital signs recording. Wearable, non invasive sensor for seizure detection All included patients will be provided with a wearable, non invasive sensor for vital signs recording.
- Primary Outcome Measures
Name Time Method Number of false positive seizures. From baseline up to 4 weeks. The number of false positive seizures will be measured, ie. seizures detected through the sensor but not reported in the seizures diary completed in real time by care giver.
Number of true positive seizures. From baseline up to 4 weeks. The number of true positive seizures will be measured, ie. seizures detected through the sensor and reported in a seizures diary completed in real time by care giver.
Number of false negative seizures. From baseline up to 4 weeks. The number of false negative seizures will be measured, ie. seizures not detected through the sensor but reported in the seizures diary completed in real time by care giver.
- Secondary Outcome Measures
Name Time Method Sensor tolerability from care givers' perspective. At 4 weeks after baseline. A self-questionnaire including 5 questions will be used, ranging from 0 "I do not agree at all" up to 10 "I completely agree".
Respiration rate impact on seizures detection. From baseline up to 4 weeks. Contribution of data from respiration rate will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.
Changes in Number of true positive, true negative and false negative seizures throughout the study duration. From baseline up to 4 weeks. Data from sensor will be analysed and compared to seizures diary.
Electrocardiogram signal quality in real life setting. From baseline up to 4 weeks. Electrocardiogram signal quality will be compared between data obtained from sensor (real life setting) and data obtained from video-EEG monitoring (hospital setting).
Activity impact on seizures detection. From baseline up to 4 weeks. Contribution of activity data will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.
Body orientation impact on seizures detection. From baseline up to 4 weeks. Contribution from body orientation data will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.
Changes in number of true positive, true negative and false negative seizures depending on patients' characteristics. From baseline up to 4 weeks. Number of true positive, true negative and false negative seizures will be analysed and compared between patients based on patients' clinical characteristics.
Sensor tolerability from patients' perspective. At 4 weeks after baseline. The French Version of the System Usability Scale (F-SUS) will be used. It is a self-questionnaire including 10 questions, ranging from 0 "I do not agree at all" up to 10 "I completely agree".
ECG data impact (ECG characteristics) on seizures detection. From baseline up to 4 weeks. Contribution from ECG data will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.
Heart rate impact on seizures detection. From baseline up to 4 weeks. Contribution of data from heart rate will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.