Doctoral Dissertation
Mobile systems for monitoring
Parkinson’s disease
MEVLUDIN MEMEDI
Information Technology
Örebro Studies in Technology 57 I ÖREBRO 2014
2014ME
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mevludin memedi was born in Macedonia in 1983. He received the Bachelor degree in Computer Science from South East European University, Tetovo, Macedonia, in 2006, the MSc degree in Computer Engineering from Dalarna University, Borlänge, Sweden, in 2008, and the Licentiate of Engineering (Teknologie licentiat) degree in Information Technology from Örebro University, Örebro, Sweden, in 2011. He has been a PhD student at the Computer Engineering department (Complex Systems – Microdata Analysis research profile) and the School of Science and Technology, Örebro University. His main areas of research interest are time series analysis, statistical and machine learning methods and their applications in medical data.
The overall aim of this thesis is to investigate the development, evaluation and application of methods and systems for supporting the clinical management of Parkinson’s disease by using repeated measures data collected by means of a telemetry touch screen device. The data consisted of subjective assessments of symptoms and objective assessments of motor condition through fine motor tests (spirography and tapping). One aim is to develop methods for objective quantification of the severity of symptoms being represented in fine motor tests results. Another aim is to develop a method for providing comparable information content as the clinical scales by combining simultaneous subjective and objective measures into composite scores for representing different symptoms severities and a global health condition of the patient. In addition, the thesis describes a prototype web-based system for providing an objective and visual representation of symptoms over time to clinicians for supporting them during decision making concerning evaluation of symptoms and treatments. The thesis demonstrates good quality of the methods and systems in terms of user satisfaction, validity, reliability, sensitivity to treatment interventions and natural disease progression, and ability to discriminate between healthy elderly subjects and patients in different disease stages.
issn 1650-8580 isbn 978-91-7668-988-2
