IEEE J Biomed Health Inform. 2019 Jan;23(1):184-196. doi: 10.1109/JBHI.2018.2800741. Epub 2018 Feb 1.
Telehealth has shown potential to improve access to healthcare cost-effectively in respiratory illness. However, it has failed to live up to expectation, in part because of poor objective measures of symptoms such as cough events, which could lead to early diagnosis or prevention. Considering the burden that these conditions constitute for national health systems, an effort is needed to foster telehealth potential by developing low-cost technology for efficient monitoring and analysis of cough events. This paper proposes the use of local Hu moments as a robust feature set for automatic cough detection in smartphone-acquired audio signals. The final system feeds a k-nearest-neighbor classifier with the extracted features. To properly evaluate the system in a diversity of noisy backgrounds, we contaminated real cough audio data with a variety of sounds including noise from both indoor and outdoor environments and noncough events (sneeze, laugh, speech, etc.). The created database allows flexible settings of signal-to-noise ratio levels between background sounds and events (cough and noncough). This evaluation was complemented using real patient data from an outpatient clinic. The system is able to detect cough events with high sensitivity (up to 88.51%) and specificity (up to 99.77%) in a variety of noisy environments, overcoming other state-of-the-art audio features. Our proposal paves the way for ubiquitous cough monitoring with minimal disruption in daily activities.
远程医疗在改善呼吸疾病的医疗服务可及性和成本效益方面显示出了潜力。然而，它并没有达到预期效果，部分原因是缺乏对咳嗽等症状的客观测量，这可能导致早期诊断或预防。考虑到这些疾病对国家卫生系统构成的负担，需要通过开发低成本的技术来有效地监测和分析咳嗽事件，以充分发挥远程医疗的潜力。本文提出使用局部 Hu 矩作为智能手机采集的音频信号中自动咳嗽检测的稳健特征集。最终的系统使用提取的特征为 k-最近邻分类器提供输入。为了在各种嘈杂背景下对系统进行适当评估，我们用各种声音（包括室内和室外环境噪声以及非咳嗽事件（打喷嚏、大笑、说话等））污染了真实的咳嗽音频数据。创建的数据库允许灵活设置背景声音和事件（咳嗽和非咳嗽）之间的信噪比水平。使用来自门诊诊所的真实患者数据对该评估进行了补充。该系统能够在各种嘈杂环境中以高灵敏度（高达 88.51%）和特异性（高达 99.77%）检测咳嗽事件，优于其他最先进的音频特征。我们的提案为在不干扰日常活动的情况下进行无处不在的咳嗽监测铺平了道路。
