mPower Voice Activity Monitoring and Classification for Parkinson’s Diagnosis

Thesis (Master's)--University of Washington, 2018Background: Parkinson’s disease patients’ voice data collected via the mPower application can be classified into three groups: Just after taking medication (at their best condition), Immediately before taking medication (at their worst condition), and somewhere in between medication doses (neither best nor worst condition). Objectives: Our goal for this investigation is to validate voice as an accurate classifier of medication status in patients with Parkinson’s Disease. Methods: After data pre-processing, logistic regression, support vector machines (SVM), decision trees, Gaussian Naïve Bayes and Multi-layer Perceptron (MLP) is applied for model training. Results: Comparing across the entire groups, the accuracy is relatively low at 0.51 on average for just best and worst condition and it increases to 0.55 for Gaussian Naïve Bayes if the condition between best and worst is also included. If we just consider the data for a single PD patient, the performance of the model can increase to 0.82. Conclusions: The result shows that there is connection between voice and Parkinson’s Disease conditions. However, the difference between the condition for the whole population might not be larger than the difference between each individual