Background Despite the efforts of research groups to develop and implement at least partial automation, cough counting remains impractical. Analysis of 24-h cough frequency is an established regulatory endpoint which, if addressed in an automated manner, has the potential to ease cough symptom evaluation over multiple 24-h periods in a patient-centric way, supporting the development of novel treatments for chronic cough, an unmet clinical need. Objectives In light of recent technological advancements, we propose a system based on the use of smartphones for objective continuous sound collection, suitable for automated cough detection and analysis. Two capabilities were identified as necessary for naturalistic cough assessment: (1) recording sound in a continuous manner (sound collection), and (2) detection of coughs from the recorded sound (cough detection). Methods This work did not involve any human subject testing or trials. For sound collection, we designed, built, and verified technical parameters of a smartphone application for sound collection. Our cough detection work describes the development of a mathematical model for sound analysis and cough identification. Performance of the model was compared to previously published results of commercially available solutions and to human raters. The compared solutions use the following methods to automatically or semi-automatically assess cough: 24-h sound recording with an ambulatory device with multiple microphones, automatic silence removal, and manual recording review for cough count. Results Sound collection: the application demonstrated the ability to continuously record sounds using the phone's internal microphone; the technical verification informed the configuration of the technical and user experience parameters. Cough detection: our cough recognition sensitivity to cough as determined by human listeners was 90 at 99.5% specificity preset and 75 at 99.9% specificity preset for a dataset created from publicly available data. Conclusions Sound collection: the application reliably collects sound data and uploads them securely to a remote server for subsequent analysis; the developed sound data collection application is a critical first step toward future incorporation in clinical trials. Cough detection: initial experiments with cough detection techniques yielded encouraging results for application to patient-collected data from future studies.

中文翻译：

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使用智能手机和机器学习连续收集声音以测量咳嗽

背景 尽管研究小组努力开发和实施至少部分自动化，但咳嗽计数仍然不切实际。对 24 小时咳嗽频率的分析是一个既定的监管终点，如果以自动方式解决，有可能以患者为中心的方式在多个 24 小时期间缓解咳嗽症状评估，支持开发新的慢性慢性病治疗方法咳嗽，一种未满足的临床需求。目标鉴于最近的技术进步，我们提出了一种基于智能手机的系统，用于客观的连续声音采集，适用于自动咳嗽检测和分析。两种能力被确定为自然咳嗽评估所必需的：（1）以连续方式记录声音（声音收集），(2) 从记录的声音中检测咳嗽（咳嗽检测）。方法 这项工作不涉及任何人类受试者测试或试验。对于声音采集，我们设计、构建并验证了一个用于声音采集的智能手机应用程序的技术参数。我们的咳嗽检测工作描述了声音分析和咳嗽识别数学模型的开发。该模型的性能与先前发布的商用解决方案的结果和人类评估者进行了比较。比较的解决方案使用以下方法来自动或半自动评估咳嗽：使用带有多个麦克风的流动设备进行 24 小时录音、自动消除静音以及手动记录咳嗽计数。结果声音收集：该应用程序展示了使用手机的内置麦克风连续录制声音的能力；技术验证通知了技术和用户体验参数的配置。咳嗽检测：由人类听众确定的咳嗽识别灵敏度为 90（预设为 99.5% 特异性）和 75（预设为 99.9% 特异性）。结论声音收集：应用程序可靠地收集声音数据并将其安全地上传到远程服务器以供后续分析；开发的声音数据收集应用程序是未来纳入临床试验的关键第一步。咳嗽检测：咳嗽检测技术的初步实验产生了令人鼓舞的结果，可应用于未来研究中收集的患者数据。