MACHINE LEARNING USING SPEECH UTTERANCES FOR PARKINSON DISEASE DETECTION

Ondřej Klempíř, Radim Krupička

Abstract


Pathophysiological recordings of patients measured from various testing methods are frequently used in the medical field for determining symptoms as well as for probability prediction for selected diseases. There are numerous symptoms among the Parkinson’s disease (PD) population, however changes in speech and articulation – is potentially the most significant biomarker. This article is focused on PD diagnosis classification based on their speech signals using pattern recognition methods (AdaBoost, Bagged trees, Quadratic SVM and k-NN). The dataset investigated in the article consists of 30 PD and 30 HC individuals’ voice measurements, with each individual being represented with 2 recordings within the dataset. Training signals for PD and HC underwent an extraction of relatively well-discriminating features relating to energy and spectral speech properties. Model implementations included a 5-fold cross validation. The accuracy of the values obtained employing the models was calculated using the confusion matrix. The average value of the overall accuracy = 82.3 % and averaged AUC = 0.88 (min. AUC = 0.86) on the available data.

Keywords


Parkinson's disease; speech; machine learning; digital biomarker; classification

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN 0301-5491 (Print)
ISSN 2336-5552 (Online)
Published by the Czech Society for Biomedical Engineering and Medical Informatics and the Faculty of Biomedical Engineering, Czech Technical University in Prague.