Lee Sung Hoon, Kim Yun-Soung, Yeo Min-Kyung, Mahmood Musa, Zavanelli Nathan, Chung Chaeuk, Heo Jun Young, Kim Yoonjoo, Jung Sung-Soo, Yeo Woon-Hong
School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Sci Adv. 2022 May 27;8(21):eabo5867. doi: 10.1126/sciadv.abo5867. Epub 2022 May 25.
Modern auscultation, using digital stethoscopes, provides a better solution than conventional methods in sound recording and visualization. However, current digital stethoscopes are too bulky and nonconformal to the skin for continuous auscultation. Moreover, motion artifacts from the rigidity cause friction noise, leading to inaccurate diagnoses. Here, we report a class of technologies that offers real-time, wireless, continuous auscultation using a soft wearable system as a quantitative disease diagnosis tool for various diseases. The soft device can detect continuous cardiopulmonary sounds with minimal noise and classify real-time signal abnormalities. A clinical study with multiple patients and control subjects captures the unique advantage of the wearable auscultation method with embedded machine learning for automated diagnoses of four types of lung diseases: crackle, wheeze, stridor, and rhonchi, with a 95% accuracy. The soft system also demonstrates the potential for a sleep study by detecting disordered breathing for home sleep and apnea detection.
现代听诊技术利用数字听诊器,在声音记录和可视化方面比传统方法提供了更好的解决方案。然而,目前的数字听诊器体积太大,与皮肤贴合度不佳,无法进行连续听诊。此外,其刚性产生的运动伪影会导致摩擦噪声,从而导致诊断不准确。在此,我们报告了一类技术,该技术使用柔软的可穿戴系统作为各种疾病的定量疾病诊断工具,提供实时、无线、连续听诊。这种柔软的设备能够以最小的噪声检测连续的心肺声音,并对实时信号异常进行分类。一项针对多名患者和对照受试者的临床研究体现了可穿戴听诊方法结合嵌入式机器学习对四种类型的肺部疾病(湿啰音、哮鸣音、喘鸣音和鼾音)进行自动诊断的独特优势,准确率达95%。该柔软系统还通过检测家庭睡眠中的呼吸紊乱和呼吸暂停,展示了其在睡眠研究方面的潜力。