Ataxic Speech Disorders and Parkinson's Disease Diagnostics via Stochastic Embedding of Empirical Mode Decomposition

42 Pages Posted: 3 Aug 2022 Last revised: 13 Jan 2023

See all articles by Marta Campi

Marta Campi

Institut Pasteur - Hearing Institute

Gareth Peters

University of California Santa Barbara; University of California, Santa Barbara

Dorota Toczydlowska

School of Mathematical and Physical Sciences, University of Technology Sydney; The Department of Statistical Science, University College London

Date Written: July 26, 2022

Abstract

Medical diagnostic methods that utilise modalities of patient symptoms such as speech are increasingly being used for initial diagnostic purposes and monitoring disease state progression. Speech disorders are particularly prevalent in neurological degenerative diseases such as Parkinson's disease, the focus of the study undertaken in this work. We will demonstrate state-of-the-art statistical time-series methods that combine elements of statistical time series modelling and signal processing with modern machine learning methods based on Gaussian process models to develop methods to accurately detect a core symptom of speech disorder in individuals who have Parkinson's disease. We will show that the proposed methods out-perform standard best practices of speech diagnostics in detecting ataxic speech disorders, and we will focus the study, particularly on a detailed analysis of a well regarded Parkinson's data speech study publicly available making all our results reproducible. The methodology developed is based on a specialised technique not widely adopted in medical statistics that found great success in other domains such as signal processing, seismology, speech analysis and ecology. In this work, we will present this method from a statistical perspective and generalise it to a stochastic model, which will be used to design a test for speech disorders when applied to speech time series signals. As such, this work is making contributions both of a practical and statistical methodological nature.


The Supplementary Appendix for this paper is given at http://ssrn.com/abstract=4173858.

The repository for the code is available at https://github.com/mcampi111/Ataxic-Speech-Disorders-and-Parkinson-s-Disease-Diagnostics-via-Stochastic-Embedding-of-Empirical-Mo

Note:
Funding Information: None.

Conflict of Interests: None.

Keywords: Empirical Mode Decomposition, Time-Frequency, Cross-Entropy, Gaussian Processes, Multi-Kernel Learning, Speech Recognition, Parkinson's Disease

Suggested Citation

Campi, Marta and Peters, Gareth and Toczydlowska, Dorota, Ataxic Speech Disorders and Parkinson's Disease Diagnostics via Stochastic Embedding of Empirical Mode Decomposition (July 26, 2022). Available at SSRN: https://ssrn.com/abstract=4173535 or http://dx.doi.org/10.2139/ssrn.4173535

Marta Campi (Contact Author)

Institut Pasteur - Hearing Institute ( email )

France

Gareth Peters

University of California Santa Barbara ( email )

Santa Barbara, CA 93106
United States

University of California, Santa Barbara ( email )

Dorota Toczydlowska

School of Mathematical and Physical Sciences, University of Technology Sydney ( email )

15 Broadway, Ultimo
PO Box 123
Sydney, NSW 2007
Australia

The Department of Statistical Science, University College London ( email )

London

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