Seizures Detection in Real Life Setting (ECEME)

A Prospective, Multicenter, Exploratory Study for Epileptic Seizures Detection Through Multimodal Analysis of Cardiorespiratory and Actimetry Parameters

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).

All included patients will be provided with a wearable, non invasive sensor for vital signs recording.

The device consists of a chest strap and an electronics module that attaches to the strap. The device stores and transmits vital sign data including ECG, heart rate, respiration rate, body orientation and activity. This sensor will be worn every day (on a 24 hours basis) excepted during weekends for 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.

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.

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.

Changes in Number of true positive, true negative and false negative seizures throughout the study duration.

Changes in number of true positive, true negative and false negative seizures depending on patients' characteristics.

Number of true positive, true negative and false negative seizures will be analysed and compared between patients based on patients' clinical characteristics.

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".

A self-questionnaire including 5 questions will be used, ranging from 0 "I do not agree at all" up to 10 "I completely agree".

Electrocardiogram signal quality will be compared between data obtained from sensor (real life setting) and data obtained from video-EEG monitoring (hospital setting).

Contribution from ECG data will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.

Contribution of data from heart rate will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.

Contribution of data from respiration rate will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.

Contribution from body orientation data will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.

Contribution of activity data will be analysed as stand-alone parameter and as associated parameter in multimodal monitoring.

Inclusion Criteria: 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. Exclusion Criteria: Generalised tonic-clonic seizures Frequent psychogenic non-epileptic seizures Pregnant or breastfeeding patients Patients displaying sensor contraindications