Digital assessment of speech in Huntington disease.

Autor: Nunes AS; BioSensics LLC, Newton, MA, United States., Pawlik M; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States., Mishra RK; BioSensics LLC, Newton, MA, United States., Waddell E; Warren Alpert Medical School of Brown University, Providence, RI, United States., Coffey M; Donald and Barbara Zucker School of Medicine, Uniondale, NY, United States., Tarolli CG; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States.; Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States., Schneider RB; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States.; Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States., Dorsey ER; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States.; Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States., Vaziri A; BioSensics LLC, Newton, MA, United States., Adams JL; Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States.; Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States.
Jazyk: angličtina
Zdroj: Frontiers in neurology [Front Neurol] 2024 Jan 23; Vol. 15, pp. 1310548. Date of Electronic Publication: 2024 Jan 23 (Print Publication: 2024).
DOI: 10.3389/fneur.2024.1310548
Abstrakt: Background: Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration.
Methods: We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features.
Results: Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p  < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%.
Conclusion: Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.
Competing Interests: AN, RM, and AV were employed by BioSensics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
(Copyright © 2024 Nunes, Pawlik, Mishra, Waddell, Coffey, Tarolli, Schneider, Dorsey, Vaziri and Adams.)
Databáze: MEDLINE