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University of Washington

1. Zhang, Qiushui. mPower Voice Activity Monitoring and Classification for Parkinson’s Diagnosis.

Degree: 2018, University of Washington

Background: 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. Advisors/Committee Members: Whipple, Mark E. (advisor).

Subjects/Keywords: Machine Learning; Parkinson’s disease; Voice; Bioinformatics; To Be Assigned

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, Q. (2018). mPower Voice Activity Monitoring and Classification for Parkinson’s Diagnosis. (Thesis). University of Washington. Retrieved from http://hdl.handle.net/1773/42108

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhang, Qiushui. “mPower Voice Activity Monitoring and Classification for Parkinson’s Diagnosis.” 2018. Thesis, University of Washington. Accessed October 21, 2018. http://hdl.handle.net/1773/42108.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhang, Qiushui. “mPower Voice Activity Monitoring and Classification for Parkinson’s Diagnosis.” 2018. Web. 21 Oct 2018.

Vancouver:

Zhang Q. mPower Voice Activity Monitoring and Classification for Parkinson’s Diagnosis. [Internet] [Thesis]. University of Washington; 2018. [cited 2018 Oct 21]. Available from: http://hdl.handle.net/1773/42108.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang Q. mPower Voice Activity Monitoring and Classification for Parkinson’s Diagnosis. [Thesis]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/42108

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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