Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study

Abstract

Background: Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases-fueled mostly by environmental pollution and an aging population-can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD.

Objective: In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD.

Methods: We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, "the quick brown fox jumps over the lazy dog." We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning-based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model's output.

Results: We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost-a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing "ahh") influence the model's decision the most.

Conclusions: Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.

Keywords: Parkinson’s disease; improving access and equity in health care; mobile phone; speech analysis.

Figure 1

An outline of our approach for solving the speech task of uttering “The quick brown fox...”.

Figure 2

An overview of our data collection, storage, and analysis pipeline.

Figure 3

Some screenshots of our subjects while providing the data. All the subjects except B provided data without any supervision. B, D, E, and F have been diagnosed with Parkinson disease. Electronic informed consent was taken from the participants to use their photos for publication.

Figure 4

A bar plot showing the age distribution of participants with PD and those without PD in our data set. PD: Parkinson disease.

Figure 5

Shapley additive explanations analysis of our best performing models on 3 data sets: (A) main model (ie, entire data set), (B) female model (ie, female only), and (C) age-trimmed model (all subjects are older than 50 years).

Figure 6

Changes in fundamental frequency F0 of voice as a function of gender and age (collected from Tsanas et al [13]).