Robust Detection of Parkinson's Disease Using Harvested Smartphone Voice Data: A Telemedicine Approach.

Introduction

Parkinson's disease affects over 10 million people globally, and ∼20% of patients with Parkinson's disease have not been diagnosed as such. The clinical diagnosis is costly: there are no specific tests or biomarkers and it can take days to diagnose as it relies on a holistic evaluation of the individual's symptoms. Existing research either predicts a Unified Parkinson Disease Rating Scale rating, uses other key Parkinsonian features such as tapping, gait, and tremor to diagnose an individual, or focuses on different audio features.

Methods

In this article, we present a classification approach implemented as an iOS App to detect whether an individual has Parkinson's using 10-s audio clips of the individual saying "aaah" into a smartphone.

Results

The 1,000 voice samples analyzed were obtained from the mPower (mobile Parkinson Disease) study, which collected 65,022 voice samples from 5,826 unique participants.

Conclusions

The experimental results comparing 12 different methods indicate that our approach achieves 99.0% accuracy in under a second, which significantly outperforms both prior diagnosis methods in the accuracy achieved and the efficiency of clinical diagnoses.