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Thesis

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

Abstract:

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...

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Institution:
University of Oxford
Research group:
"Oxford Centre for Industrial and Applied Mathematics (OCIAM)"; "Systems Analysis Modelling and Prediction (SAMP)"; "Intelligent Patient Monitoring (IPM)"
Oxford college:
St Cross College
Department:
Mathematical,Physical & Life Sciences Division - Mathematical Institute
Role:
Author

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Role:
Supervisor
Role:
Supervisor
Role:
Supervisor
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Funding agency for:
Athanasios Tsanas
Publication date:
2012
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK
URN:
uuid:2a43b92a-9cd5-4646-8f0f-81dbe2ba9d74
Local pid:
ora:6565

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