Ambulatory monitoring of vocal function to improve voice disorder assessment

描述（由申请人提供）：在美国，声音障碍影响了大约6.6%的工作年龄人口。许多常见的声音障碍是慢性或反复出现的情况，可能是由发声行为的错误和/或滥用模式引起的，通常被称为发声功能亢进。这种以行为为基础的障碍在临床环境中很难准确评估，在个体从事典型的日常活动时，通过长期动态监测声带功能，可能会有更好的表征。使用颈部微型加速度计（ACC）作为发声传感器的设备已经显示出对声音功能进行不显眼的长期监测的最佳潜力。然而，这项技术在临床实践中的应用非常有限，因为：1)当前设备、措施和分析算法的技术限制；2)商用系统的成本相对较高；3)缺乏统计上可靠的研究来确定基于acc的措施的真正诊断能力。该项目的总体目标（响应PAR-09-057）是将基于acc的声音功能动态监测发展成为一种有效、可靠且具有成本效益的临床工具，可用于准确识别和区分与功能亢进语音障碍相关的声音使用模式。实现这一目标将：1)大大改善对这些常见类型的语音障碍的临床评估，2)使语音治疗能够更准确地针对个体患者的特定功能障碍行为，3)为未来开发动态生物反馈方法提供基础，这些方法有可能促进这些障碍的更高效和有效的行为治疗。在这个项目的R21阶段，我们将开发和验证一个新的、通用的、具有成本效益的动态语音监控系统，该系统使用颈部放置的微型ACC作为发音传感器，并使用移动个人数字设备（例如智能手机）作为数据采集平台。通过设计与新一代个人数字设备架构基本兼容的系统软件和基本接口电路，将努力促进该技术在临床应用和开发中的持续可用性。该项目的R33阶段将侧重于使用新的动态监测系统，从大量（治疗前后）功能性语音障碍患者和匹配对照中收集数据。这些数据将受到三种类型的分析方法的影响，以努力确定区分功能亢进和正常发声行为模式的最佳措施：1)先前开发的发声功能动态测量，包括发声剂量；2)使用新的声音系统模型从ACC信号中提取的声门气流估计，以及3)基于已成功用于分析其他生理信号（如心电图）的长期记录的方法的测量，用于患者的风险分层。

DESCRIPTION (provided by applicant): Voice disorders affect approximately 6.6% of the working-age population in the United States. Many common voice disorders are chronic or recurring conditions that are likely to result from faulty and/or abusive patterns of vocal behavior referred to generically as vocal hyperfunction. Such behaviorally based disorders can be difficult to accurately assess in the clinical setting and could potentially be much better characterized by long-term ambulatory monitoring of vocal function as individuals engage in their typical daily activities. Devices that use a neck-placed miniature accelerometer (ACC) as a phonation sensor have shown the best potential for unobtrusive long-term monitoring of vocal function. The adoption, however, of this technology into clinical practice has been quite limited because of: 1) technical limitations of current devices, measures, and analysis algorithms; 2) the relatively high cost of commercially-available systems; and 3) the lack of statistically robust studies to determine the true diagnostic capabilities of ACC-based measures. The overall goal of the proposed project (in response to PAR-09-057) is to develop ACC-based ambulatory monitoring of vocal function into a valid, reliable, and cost-effective clinical tool that can be used to accurately identify and differentiate patterns of voice use that are associated with hyperfunctional voice disorders. Achieving this goal will: 1) greatly improve clinical assessment of these commonly-occurring types of voice disorders, 2) enable voice therapy to more accurately target specific hyperfunctional behaviors for individual patients, and 3) provide the basis for future efforts to develop ambulatory biofeedback approaches that have the potential to facilitate more efficient and effective behavioral treatment of these disorders. In the R21 phase of this project we will develop and validate a new, versatile, and cost-effective system for ambulatory voice monitoring that uses a neck-placed miniature ACC as the phonation sensor and a mobile personal digital device (e.g., a smartphone) as the data acquisition platform. An effort will be made to facilitate the continued availability of this technology for clinical use and development by designing system software and basic interface circuitry that is largely compatible with new generations of personal digital device architecture. The R33 phase of the project will focus on using the new ambulatory monitoring system to collect data from a large, statistically robust sample of patients with hyperfunctional voice disorders (before and after treatment) and matched controls. These data will be subjected to three types of analysis approaches in an effort to identify the best set of measures for differentiating among hyperfunctional and normal patterns of vocal behavior: 1) previously-developed ambulatory measures of vocal function that include vocal dosage; 2) measures based on estimates of glottal airflow that are extracted from the ACC signal using a new vocal system model, and 3) measures based on methods that have been used successfully in analyzing long-term recordings of other physiologic signals (e.g., electrocardiograms) for risk stratification of patients.