Ambulatory monitoring of vocal function to improve voice disorder assessment

描述（由申请人提供）：声音障碍影响美国约6.6%的工作年龄人口。许多常见的嗓音障碍是慢性或复发性疾病，可能是由错误和/或滥用的发声行为模式引起的，一般称为发声功能亢进。这种基于行为的疾病可能难以在临床环境中准确评估，并且当个体参与其典型的日常活动时，通过长期动态监测发声功能可能会更好地表征。使用颈部放置的微型加速度计（ACC）作为发声传感器的设备已显示出最好的潜力，用于不引人注目的长期监测发声功能。然而，由于以下原因，该技术在临床实践中的应用非常有限：1）当前器械、测量和分析算法的技术限制; 2）市售系统的成本相对较高; 3）缺乏统计学上可靠的研究来确定基于ACC的测量的真正诊断能力。 拟议项目的总体目标（响应PAR-09-057）是将基于ACC的发声功能动态监测开发为有效、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性嗓音障碍相关的发声模式。实现这一目标将：1）极大地改善了这些常见类型的嗓音障碍的临床评估，2）使嗓音治疗能够更准确地针对个体患者的特定功能亢进行为，以及3）为未来努力开发具有促进这些障碍的更高效和有效的行为治疗的潜力的动态生物反馈方法提供基础。 在该项目的R21阶段，我们将开发和验证一种新的、多功能的、具有成本效益的动态语音监测系统，该系统使用颈部放置的微型ACC作为发声传感器和移动的个人数字设备（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使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，从大量具有统计学稳健性的多功能语音障碍患者（治疗前后）和匹配对照组样本中收集数据。这些数据将经过三种类型的分析方法，以确定区分发声行为的功能亢进和正常模式的最佳测量方法：1）先前开发的发声功能的动态测量方法，包括发声剂量; 2）基于使用新的发声系统模型从ACC信号提取的声门气流的估计的测量，以及3）基于已成功用于分析其它生理信号的长期记录的方法的测量（例如，心电图）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.