Thesis defense Ying Wang (Donders series 480)

20 January 2021

E-health in Epilepsy and Parkinson's Disease

The burden of our health care system is heavy and may increase in an aging population. Any public health crisis, such as the COVID-19 crisis, could test the capacity of our health care system and worsen the situation. E-health monitoring systems can help decrease the burden of health care systems and assist timely treatments for individuals. However, accurate e-health systems are still challenging. In this thesis, we stated our research about e-health monitoring systems for individuals with epilepsy or Parkinson’s disease. We developed the systems from the perspective of clinical, basic science, and engineering research. For the application of epilepsy, we developed an online seizure detection system for individuals with nonconvulsive seizures. We proposed a 4-class classifier based on our clinical findings for seizure detection in individuals with non-convulsive status epilepticus. The precision in the detection was relatively improved. For the Parkinson’s disease application, we collected multimodal physiological data from 30 individuals with frequent freezing of gait episodes. An online freezing of gait detection system was developed using multimodal features, and the detection performance was improved. In addition, a significant relationship between eye movements and freezing episodes was found through a fundamental study. Features about eye movements could further contribute to the detection performance in the following studies. Hereby, we would strongly suggest a close collaboration among researchers in the clinical, basic science, and engineering field in future ehealth monitoring studies. Inspired by problems during clinical practices, supported by the evidence in basic science, and customizing engineering techniques, we could obtain accurate ehealth monitoring systems for people around the world in the future.