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

Abstract

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.