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MRC Precision Medicine DTP: Developing clinical decision support tools to characterise neurodegenerative disorders using biomedical speech signal processing and statistical machine learning


Project Description

Background
The population is aging globally, presenting important societal challenges and straining national health systems to meet increasing demand for healthcare delivery. The rise of new technologies, including smartphones and smartwatches, provides a unique opportunity to revolutionise contemporary healthcare delivery through the collection of additional signal modalities, without requiring frequent physical visits of people into clinics.

Speech is a signal modality which is easy to collect, requires minimal equipment, and has been shown to convey clinically important information on neurodegenerative conditions such as Parkinson’s Disease (PD). We have previously shown that speech can be used to differentiate healthy controls from people with Parkinson’s disease and replicate the standard symptom severity metric Universal Parkinson’s Disease Rating Scale (UPDRS) [1]. We have also demonstrated the use of speech to assess remote PD rehabilitation [2]. Furthermore, we have reported initial explorations towards understanding phonation biomechanics of speech signal degradation, thus gaining a more mechanistic insight into PD symptom severity progression [3]. More recently, we have shown that speech could be used as an early biomarker of PD associated with genetic information [4]. Overall, the field of biomedical speech signal processing is rapidly expanding and has generated considerable research interest over the past few years.

Aims
The framework of this project is to further investigate the potential of speech signals, with the goal of studying and monitoring the manifestation and progression of neurodegenerative diseases, such as Parkinson’s disease and Alzheimer’s disease. The student will apply algorithms we have previously developed and extend approaches towards improving the characterisation of neurodegenerative diseases. Previous studies have only contrasted a group with a neurodegenerative disorder and healthy controls; they have not developed tools towards differential diagnosis (i.e. tackling the problem of differentiating diseases, and taking into account potential co-morbidities). This project will extend previous work to investigate imprints of different neurodegenerative disorders on speech signals towards improving understanding of disease progression and treatment planning.

We have rich data resources from clinical collaborators based in the US, Australia, and Spain, which is readily available and which has been previously used in publications in our group. Moreover, our clinical colleagues in Madrid, Spain (led by co-supervisor Victor Nieto-Lluis) have already started data collection across a range of neurodegenerative disorders, including speech signals and disease-specific clinical markers.
The recruited student will be primarily working on developing novel time-series, signal processing, and pattern recognition algorithms, and extending statistical machine learning algorithms to develop a robust user-friendly clinical decision support tool to characterise neurodegenerative disorders.

Training outcomes
• Practical understanding of the problems at the interface of clinical practice and data analytics, including the language barrier with niche terminology on both ends
• Developing expertise in time-series analysis, signal processing, and statistical machine learning to tackle large-scale challenging problems
• Programming skills: transforming algorithmic concepts to software tools, and developing interfaces which can be used by experts
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This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you are encouraged to contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2019

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.

Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £14,777 (RCUK rate 2018/19) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

References

1. A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity, Journal of the Royal Society Interface, Vol. 8, pp. 842-855, 2011
2. A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, 2014
3. P. Gómez-Vilda, A. Álvarez-Marquina, A. Tsanas, C.A. Lázaro-Carrascosa, V. Rodellar-Biarge, V. Nieto-Lluis, R. Martínez-Olalla: Phonation biomechanics in quantifying Parkinson's disease symptom severity, Recent Advances in Nonlinear Speech Processing, Vol. 48 of the series Smart Innovation, Systems and Technologies, Springer, pp. 93-102, 2016
4. S. Arora, N.P. Visanji, T.A. Mestre, A. Tsanas, A. Al Dakheel, B.S. Connolly, C. Gasca-Salas, D.S. Kern, J. Jain, E.J. Slow, A. Faust-Socher, A.E. Lang, M.A. Little, C. Marras: Investigating voice as a biomarker for leucine-rich repeat kinase 2-associated Parkinson’s disease: a pilot study, Journal of Parkinson’s Disease (in press)

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