Assessment of Parkinson's Disease Medication State through Automatic Speech Analysis

TL;DR

This paper presents a speech analysis system using deep learning to accurately classify Parkinson's disease medication states, enabling remote monitoring of patient condition with high accuracy.

Contribution

It introduces a novel speaker-dependent speech-based biomarker system for classifying PD medication states with high accuracy, advancing remote patient monitoring.

Findings

Achieved 90.54% accuracy with mixed speech

Achieved 95.27% accuracy with semi-spontaneous speech

Demonstrated potential for reliable remote monitoring of PD medication states

Abstract

Parkinson's disease (PD) is a progressive degenerative disorder of the central nervous system characterized by motor and non-motor symptoms. As the disease progresses, patients alternate periods in which motor symptoms are mitigated due to medication intake (ON state) and periods with motor complications (OFF state). The time that patients spend in the OFF condition is currently the main parameter employed to assess pharmacological interventions and to evaluate the efficacy of different active principles. In this work, we present a system that combines automatic speech processing and deep learning techniques to classify the medication state of PD patients by leveraging personal speech-based bio-markers. We devise a speaker-dependent approach and investigate the relevance of different acoustic-prosodic feature sets. Results show an accuracy of 90.54% in a test task with mixed speech and an accuracy of 95.27% in a semi-spontaneous speech task. Overall, the experimental assessment shows the potentials of this approach towards the development of reliable, remote daily monitoring and scheduling of medication intake of PD patients.