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

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.

Contrastive learning of heart and lung sounds for label-efficient diagnosis

“… 16 We use another dataset for external validation: lung sounds recorded at a different clinical site. 36 …”

“…16 We use another dataset for external validation: lung sounds recorded at a different clinical site. 36 For the PhysioNet Heart Challenge, we consider the task of classifying normal sounds versus abnormal sounds. The dataset consists of 3,240 samples (2,575 normal and 665 abnormal), which were center-padded and cropped to a consistent size.…”

Lung Sound Classification Using Co-Tuning and Stochastic Normalization

“…[13] or multi channel lung sound data [14] (ours) and public datasets i.e. the ICBHI 2017 dataset [5] or the Abdullah University Hospital 2020 dataset [15]. Due to limitations in the amount and quality of available data, the performance and generalization of the lung sound classification system may suffer.…”

Lung Sound Classification Using Co-tuning and Stochastic Normalization

“…[8] or multi channel lung sound data [9] (ours) and public datasets i.e. the ICBHI 2017 dataset [5] or the Abdullah University Hospital 2020 dataset [10]. Due to limitations in the amount and quality of available data, the performance and generalization of the lung sound classification system may suffer over-estimated results.…”