使用智能手机对帕金森病进行自动声学分析。

Gabriela T Acevedo T, Marc C Pappas, Jackson G Wolfe, Joshua Wong, Adolfo Ramirez-Zamora, Pamela R Zeilman, Diego L Guarin
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引用次数: 0

摘要

构音障碍是帕金森病(PD)中一种常见的语言障碍。构音障碍分析仪软件已成为PD自动语音分析和构音障碍严重程度量化的可行工具。然而,大多数研究使用dys构音分析仪在严格控制的条件下和高质量的麦克风下获得的录音,而dys构音分析仪在非理想条件下(如繁忙的临床环境)获得的录音时的效用仍未被探索。这项研究调查了构音障碍分析仪在更类似于使用智能手机的临床环境中的表现。我们获得了三组数据,包括健康对照组(HC)、开启深部脑刺激(on -DBS)的PD患者和关闭DBS (off -DBS)的PD患者。我们发现,与HC组相比,OFF-DBS组的音高可变性显著降低,言语率显著提高。此外,大多数语音标记的估计值落在文献报道的值之内。我们的研究结果表明,构音障碍分析仪有效地提取相关的语音标记,即使是在非理想条件下获得的录音,强调其潜在的广泛临床应用。临床相关性-我们的研究结果证明了在临床环境中使用智能手机录音进行自动客观语音分析的潜力。这些发现与开发一种临床工具相关,该工具可以在PD的常规临床就诊中广泛使用并易于实施,以改善PD的构音障碍评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Acoustic Analysis in Parkinson's Disease Using a Smartphone.

Dysarthria is a common speech disorder in Parkinson's Disease (PD). The Dysarthria Analyzer software has emerged as a viable tool for automatic speech analysis in PD and quantification of dysarthria severity. However, most studies use the Dysarthria Analyzer with recordings obtained under tightly controlled conditions and high-quality microphones, and the utility of the Dysarthria Analyzer when used with recordings acquired under non-ideal conditions, such as in busy clinical settings, remains unexplored. This study investigates the Dysarthria Analyzer's performance in a setting more akin to a clinical environment using a smartphone. We obtained data from three groups, including healthy controls (HC), PD patients with their deep brain stimulation on (ON-DBS), and PD patients with their DBS off (OFF-DBS). We found a significant decrease in pitch variability and an increase in speech rate for the OFF-DBS group compared to the HC. Furthermore, most of the estimated values for the speech markers fall within the reported values in the literature. Our findings demonstrate that the Dysarthria Analyzer effectively extracts relevant speech markers even when used with recordings obtained under non-ideal conditions, emphasizing its potential for widespread clinical adoption.Clinical Relevance- Our findings demonstrate the potential of using smartphone recordings obtained in clinical environments for automatic objective speech analysis. These findings are relevant for developing a clinical tool that can be widely accessible and easily implemented during routine clinical visits of PD to improve the assessment of dysarthria in PD.

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