Author:

Keywords:

SISTA

Abstract:

Epilepsy is one of the most common neurological diseases that manifests in repetitive epileptic seizures as a result of an abnormal, synchronous activity of a large group of neurons. Depending on the affected brain regions, seizures produce various severe clinical symptoms. There is no cure for epilepsy and sometimes even medication and other therapies, like surgery, do not control the number of seizures. In that case, long-term (home) monitoring and automatic seizure detection would enable the tracking of the evolution of the disease and improve objective insight in any responses to medical interventions or changes in medical treatment. Especially during the night, supervision is reduced; hence a large number of seizures is missed. In addition, an alarm should be integrated into the automated seizure detection algorithm for severe seizures in order to help the patient during and after the seizure. Frontal lobe and tonic-clonic seizures are accompanied with violent movements which could lead to injuries; also there is the danger of suffocation caused by vomiting or the breathing can be obstructed. These situations require intervention during or after the seizures. Combined video/electroencephalography (EEG) monitoring remains the gold standard for epilepsy monitoring, whereas solely EEG is traditionally used for automated seizure detection in specialized hospitals. However, EEG electrodes have to be attached to the scalp by the trained nurse, and long-term wearing EEG can become uncomfortable, which makes EEG-based home monitoring not feasible. In this thesis, we investigate the application of less intrusive sensors, namely accelerometers (ACM) attached to the wrists and ankles within wrist-bands, and surface electromyography (sEMG) registering the muscle activity of the biceps at both arms, for the detection of epileptic seizures. This thesis aims at developing automated seizure detection algorithms using aforementioned modalities in pediatric patients. First, two feature selection methods are applied to identify the most relevant features for the distinction between each epileptic seizure class and all other nocturnal movements using ACM signals. For this purpose, a large number of features was collected from the literature. Feature selection methods were tested using least squares support vector machine classifiers. It is shown that a fast filter method, although significantly reducing the number of features, did not degrade the classification performance compared with the complete feature set. Next, this method is applied as part of an ACM-based automated seizure detection algorithm for the detection of (tonic-)clonic seizures. Patient-independent detectors were tested both on the data recorded with a wired system and data recorded in a home environment using a wireless system. In the last part of this thesis, ACM and sEMG-based automated tonic-clonic seizure detectors were compared. In addition, we examined whether an integrated approach could yield a better result. The ACM and sEMG classification outputs were combined using a late integration approach. The results showed that there was a need for a patient-specific measurement system for the detection of epileptic seizures based on prior knowledge on patient’s seizure characteristic and his/her typical non-epileptic behavior. The techniques proposed in this thesis pave the way to the development of home monitoring algorithms for pediatric patients.