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Classification of speech intelligibility in Parkinson's disease
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
Högskolan Dalarna.
Högskolan Dalarna.
2014 (English)In: Biocybernetics and Biomedical Engineering BBE, ISSN 0208-5216, Vol. 34, no 1, 34-45 p.Article in journal (Refereed) Published
Abstract [en]

A problem in the clinical assessment of running speech in Parkinson's disease (PD) is to track underlying deficits in a number of speech components including respiration, phonation, articulation and prosody, each of which disturbs the speech intelligibility. A set of 13 features, including the cepstral separation difference and Mel-frequency cepstral coefficients were computed to represent deficits in each individual speech component. These features were then used in training a support vector machine (SVM) using n-fold cross validation. The dataset used for method development and evaluation consisted of 240 running speech samples recorded from 60 PD patients and 20 healthy controls. These speech samples were clinically rated using the Unified Parkinson's Disease Rating Scale Motor Examination of Speech (UPDRS-S). The classification accuracy of SVM was 85% in 3 levels of UPDRS-S scale and 92% in 2 levels with the average area under the ROC (receiver operating characteristic) curves of around 91%. The strong classification ability of selected features and the SVM model supports suitability of this scheme to monitor speech symptoms in PD.

Place, publisher, year, edition, pages
2014. Vol. 34, no 1, 34-45 p.
Keyword [en]
Parkinson's disease, Speech processing, Dysarthria, Support vector machine, Tele-monitoring
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-23607DOI: 10.1016/j.bbe.2013.10.003ISI: 000333226500006Scopus ID: 2-s2.0-84894547177OAI: oai:DiVA.org:mdh-23607DiVA: diva2:679575
Projects
E-MOTIONS
Available from: 2013-12-16 Created: 2013-12-16 Last updated: 2015-07-15Bibliographically approved
In thesis
1. First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
Open this publication in new window or tab >>First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic. 

The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait.

The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Place, publisher, year, edition, pages
Sweden: Mälardalen University, 2014. 102 p.
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 153
National Category
Engineering and Technology
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-24647 (URN)978-91-7485-142-7 (ISBN)
Public defence
2014-04-16, Clas Ohlson, Studenternas Hus Tenoren, Campus Borlänge, 13:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2014-03-17 Created: 2014-03-14 Last updated: 2015-07-15Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
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Output format
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