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Project

Multimodal profiling of people with epilepsy to determine which signals are clinically useful for long-term home monitoring

Epilepsy is a serious and common neurological disorder marked by recurrent epileptic seizures, caused by abnormal, excessive and synchronous electrical discharges in the brain. It has a prevalence of around 0.5% and affects nearly 50 million people worldwide. As many as 30% of patients with epilepsy are refractory, which means that epilepsy is not controlled with two or more well used anti-epileptic drugs. In order to develop and evaluate new treatment strategies, seizure identification must be prompt and accurate, but currently, many seizures are not reported. Less than half of the epilepsy patients can accurately document their seizures. The best way to detect and count seizures is during long-term video-electroencephalographic (vEEG) monitoring, but at high economic cost for the healthcare system. This kind of monitoring also helps to elucidate interictal epileptiform discharges (IED), which are in-between seizures manifestation of the abnormal brain activity. This IED are related to short-term cognitive changes during task-related activities and still, there’s discussion if they should be treated. Due to the high economic costs, vEEG is not available to many patients with epilepsy throughout the world, and in low-income countries, there are higher accessibility barriers. That is the main reason for using an accurate, non-obtrusive, portable devices for seizure detection and monitoring in the home setting. The currently available wearables are only able to detect one seizure type, namely tonic-clonic seizures. These are based on EMG- and motion measurement and do not allow measurement of other biosignals, which may be important to avoid SUDEP. In this study, the investigators will make use of a small, discrete and unobtrusive wearable, the Sensor-Dot (https://www.byteflies.com/) and newly developed electrode patches (Plug ‘n Patch system). The aim is multimodal profiling of people with epilepsy to determine which signals are clinically useful for long-term home monitoring. Biosignals that will be registered include EEG, EMG, and ECG, respiration, oxygen saturation, skin temperature and motion. The first part of the study is hospital-based and will last 5 days. The investigators will compare the biosignals of the Sensor-Dot and the Plug ‘n Patch system with those measured with hospital equipment. Participants are 15 patients with refractory focal epilepsy who will be admitted to the hospital for long-term vEEG registration of epileptic seizures as part of a presurgical evaluation. The second part of the study is home-based and will last for a maximum of 1 year. Sixty participants will be selected with refractory idiopathic generalized epilepsy (n=15), refractory focal epilepsy (n=30) and frequent nocturnal tonic-clonic seizures (n=15). The aim is to determine and improve usability of the Sensor-Dot and Plug ‘n Patch system upon long-term use in the home environment. The investigators will determine the number of patients with side effects and adverse events of the Sensor-Dot and Plug ‘n Patch system, e.g. contact allergic eczema. The investigators will determine the total time that participants wear the Sensor-Dot and Plug ‘n Patch system, and the reason why participants do not wear it. The investigators further aim to determine whether epileptic seizures and IED occur in cycles, and will study interactions between epilepsy and sleep. The investigators will also study whether skin temperature occurs in recurring cycles and is associated with the occurrence of epileptic seizures. The investigators will study changes in EEG, respiration, heart rate, skin temperature and oxygen saturation during tonic-clonic seizures with the aim to better understand and prevent SUDEP. The investigators will determine whether it is possible that the Sensor-Dot and Plug ‘n Patch system can be used as a seizure forecaster.

Date:3 Nov 2020 →  Today
Keywords:Epilepsy, Clinical Neurophysiology, Health technology, Wearable devices, Seizure forecasting, Epilepsie, Klinische Neurofysiologie, Gezondheidstechnologie, Voorspellen van epileptische aanvallen
Disciplines:Neurological and neuromuscular diseases, Neurophysiology
Project type:PhD project