Ataxic speech disorders and Parkinson’s disease diagnostics via stochastic embedding of empirical mode decomposition

Publisher:
Public Library of Science (PLoS)
Publication Type:
Journal Article
Citation:
PLOS ONE, 2023, 18, (4), pp. e0284667
Issue Date:
2023
Full metadata record
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
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