Study of Techniques and Methods for Building a Database of Lung Auscultation Sounds

Abstract

Currently, the results of lung sound auscultation with either physical or electronic stethoscopes still rely mainly on the doctor's professional auscultation identification ability, which has not yet been able to realise intelligent diagnosis and interpretation. When patients are affected by lung diseases at home, they are unable to detect lung abnormalities on their own and delay treatment; when they are in the process of rescue and treatment of respiratory infectious diseases, in-ear stethoscopes are easily contaminated and cause nosocomial infections. Although stethoscopic sounds contain a wealth of information about health status, the lack of standardised collection methods, classification criteria and analysis tools has limited the objective analysis and application of stethoscopic sounds in practice. In this study, the data collection, arrangement and database design of the lung auscultation sound were carried out by using the unified auscultation sound collection equipment and process. The study used the software MetlabR2017a for data management and analysis to create a database of lung auscultation sounds in a healthy group and a group of patients with lung disease. A database of lung auscultation sounds was established for healthy groups and groups of patients with lung diseases. A standard set of classification of auscultatory tones, labelling specifications, audio characteristic signal parameters were developed. Building a system for storing, managing and analysing lung auscultation sound data to provide important data support for research related to the screening and monitoring of lung diseases and the translation of medical artificial intelligence applications. The study accumulated the experience of building an audio database of lung auscultation sounds, provided a useful reference for the management and analysis of the audio database, and laied the foundation for supporting the subsequent application of medical artificial intelligence-assisted auscultation in the screening and monitoring of lung diseases, which was of great medical value and practical application.

Keywords

• lung diseases|lung auscultation sound|audio database|support vector machine|feature recognition|data analysis