Characterization of Stethoscope and Machine LearningAlgorithm for Respiratory Sound Classification

• Main Authors: Nai-Yun Tung, 董乃昀
• Other Authors: Chii-Wann Lin
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/vqw5ks

碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 107 === Stethoscope is an indispensable tool for medical diagnosis. Electronic stethoscope solves the problem with traditional stethoscope, auscultation cannot be recorded and stored, and that diagnosis can only rely on medical professionals. By using electronic stethoscopes, sound signals are now transmittable to computers for further analysis. Therefore, quantification of the recording quality of electronic stethoscopes has become an important indicator. The aim of this study is to establish an acoustic testing system which quantifies the recording quality of electronic stethoscopes, and to develop a clinical respiratory sound classification algorithm. Currently, there are no complete and scientific standards for evaluating stethoscopes. In this study, monotonic sound is played using the frequency sweep method and recorded by 3M electronic stethoscopes and continuous auscultation patches in a soundless room. After calculating frequency response and harmonic distortion rate to quantify acoustical properties, an acoustic testing system is developed. Through clinical trials in ICU and RCW in hospitals, patients’ respiratory sound is collected. Machine learning algorithm is then used to build up a respiratory sound recognition and classification system, including MFCC feature extraction and selecting the best out of 3 types of neural networks models, to automatically recognize normal and abnormal respiratory sound. In this study, the acoustic testing system works effectively on evaluating an electronic stethoscope, enabling it to differentiate on the quality of recording between 3M electronic stethoscopes and continuous auscultation patches. The result shows that because of 3M stethoscope’s structure and recoding restrictions, sound recorded by it has unstable frequency response which leads to the classification model unable to extract the specific frequency features and lower the accuracy of recognition. This difference in recording quality of stethoscope correlates to the classification performance. Finally, our classification model has an overall accuracy of 93%, which means that it can be used in hospitals to automatically recognize different respiratory sound and help doctors for diagnosis. In the future, the acoustic testing system will be used for developing and evaluating electronic stethoscope. In addition, respiratory sound recognition algorithm will be trained and will include more clinical abnormal sound types; furthermore, this will build up a continuous monitoring and real-time alerting respiratory caring system.