한빛사 논문
Sung Hoon Lee1,2†, Yun-Soung Kim2,3†, Min-Kyung Yeo4†, Musa Mahmood2,3, Nathan Zavanelli2,5, Chaeuk Chung6, Jun Young Heo7, Yoonjoo Kim6, Sung-Soo Jung8*, Woon-Hong Yeo2,3,5,9*
1School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. 2Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA. 3George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. 4Department of Pathology, Chungnam National University School of Medicine, Daejeon 35015, Republic of Korea. 5Wallace H. Coulter Department of Biomedical Engineering, Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA 30332, USA. 6Division of Pulmonology, Department of Internal Medicine, Chungnam National University School of Medicine, Daejeon 35015, Republic of Korea. 7Department of Biochemistry, Chungnam National University School of Medicine, Daejeon 35015, Republic of Korea. 8Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Daejeon 35015, Republic of Korea. 9Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA.
*Corresponding author.
†These authors contributed equally to this work.
Abstract
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.
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