MACHINE LEARNING USING SPEECH UTTERANCES FOR PARKINSON DISEASE DETECTION

Authors

  • Ondřej Klempíř Czech Technical University in Prague
  • Radim Krupička Czech Technical University in Prague

Keywords:

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

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.

Author Biographies

Ondřej Klempíř, Czech Technical University in Prague

Department of Biomedical Informatics.

Radim Krupička, Czech Technical University in Prague

Department of Biomedical Informatics.

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Published

2018-06-30

Issue

Vol. 48 No. 2 (2018)

Section

Original Research

License

Copyright (c) 2018 Ondřej Klempíř, Radim Krupička

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