Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning

Thesis

This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total...

Expand abstract

Authors: Tsanas, A

• Publication date: 2012
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: Oxford University, UK
• Language: English
• Keywords: statistical machine learning; pattern recognition; Parkinson's disease; Unified Parkinson's Disease Rating Scale (UPDRS); feature selection; signal processing
• Subjects: Biomedical engineering; Artificial Intelligence; Pattern recognition (statistics); Mathematical biology; Bioinformatics (technology); Mathematical modeling (engineering); Machine learning; Signal processing