Acoustic Indicators of Parkinson’s Disease: A Systematic Literature Review of Clinical Practices
Objective: The present study aims to determine, from the existing literature, the use of commonly employed acoustic voice analysis techniques and voice measurements in assessing and diagnosing Parkinson's disease (PD).
Methods: This is a systematic literature review that included journal research papers, written in English, Portuguese, and Spanish that reported using of voice acoustic parameters for assessment and diagnosis of patients with PD. The search included seven major databases (Web of Science, EBSCO, Pubmed, Science Direct, Scopus, Scielo, and BVS). Any paper on this topic available online before 2024 was considered, without any time limits. The papers were screened by two raters according to the criteria: population (normal adults and patients with PD), intervention (any kind of intervention), comparison (intra- or inter-comparison between normal controls and PD patients), and outcome (acoustic voice metrics). Following this, a quality check was conducted to filter out lower-quality studies. Key information was obtained from the final selected studies, focusing on the sample size, the specific acoustic metrics employed, the speech material used for analysis, and whether there is statistical significance of using voice acoustic metrics for assessment and diagnosis of PD.
Results and Conclusions: This review underscored the growing interest in acoustic voice analysis as a valuable biomarker for predicting, diagnosing, and tracking PD, as evidenced by the increasing number of studies over time. In general, PD has been more extensively researched in literature compared to other conditions. The findings identified commonly used acoustic measures for PD assessment, with the meta-analysis highlighting specific acoustic features as reliable indicators for PD detection. The review also addresses gaps, limitations, and overlooked acoustic metrics in research, and suggests potential future directions. Additionally, it provides insights into advancing the assessment and screening of PD utilizing voice as a promising biomarker.