Predicting Parkinson's Disease with Voice Analysis on a Smartphone

Andrej Zupanc (2017) Predicting Parkinson's Disease with Voice Analysis on a Smartphone. EngD thesis.

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Abstract

Early diagnosis can have significant effect on disease progression, its treatment and patient's quality of life. However, some diseases are incurable, and doctors can only help relieve symptoms. One of such is Parkinson's disease, a neurodegenerative disease marked by tremor, slowness of movement, muscular rigidity and difficulty with speaking. The aim of this paper was to develop a system for early diagnosis of Parkinson's disease which could recognize signs of Parkinson's disease in a person's voice. For this purpose, a mobile application, an API interface and a classifier were developed. The API interface saves voice recordings made by the mobile application, then analyses and classifies them with the classifier. After the classification is done, the API interface sends the result back to the mobile application which informs its user about the outcome of their voice analysis. The application was developed for Android operating system. The API interface is based on the Flask library. Different classifiers using libraries Scikit-learn and Keras were developed. Then, the most appropriate classifier was chosen and implemented into the API interface. An example of how the application can be used is also described.

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