A dataset of lung sounds recorded from the chest wall using an electronic stethoscope

Mohammad Fraiwan; Luay Fraiwan; Basheer Khassawneh; Ali Ibnian

doi:10.1016/j.dib.2021.106913

A dataset of lung sounds recorded from the chest wall using an electronic stethoscope

Data Brief. 2021 Feb 26:35:106913. doi: 10.1016/j.dib.2021.106913. eCollection 2021 Apr.

Authors

Mohammad Fraiwan¹, Luay Fraiwan², Basheer Khassawneh³, Ali Ibnian³

Affiliations

¹ Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
² Department of Biomedical Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
³ Department of Internal Medicine, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.

Abstract

The advancement of stethoscope technology has enabled high quality recording of patient sounds. We used an electronic stethoscope to record lung sounds from healthy and unhealthy subjects. The dataset includes sounds from seven ailments (i.e., asthma, heart failure, pneumonia, bronchitis, pleural effusion, lung fibrosis, and chronic obstructive pulmonary disease (COPD)) as well as normal breathing sounds. The dataset presented in this article contains the audio recordings from the examination of the chest wall at various vantage points. The stethoscope placement on the subject was determined by the specialist physician performing the diagnosis. Each recording was replicated three times corresponding to various frequency filters that emphasize certain bodily sounds. The dataset can be used for the development of automated methods that detect pulmonary diseases from lung sounds or identify the correct type of lung sound. The same methods can also be applied to the study of heart sounds.

Keywords: Artificial intelligence; Deep learning; Electronic stethoscope; Lung sounds; Pulmonary diseases.