Fraiwan Mohammad, Fraiwan Luay, Khassawneh Basheer, Ibnian Ali
Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
Department of Biomedical Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
Data Brief. 2021 Feb 26;35:106913. doi: 10.1016/j.dib.2021.106913. eCollection 2021 Apr.
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.
听诊器技术的进步使得能够高质量地记录患者的声音。我们使用电子听诊器记录健康和不健康受试者的肺部声音。该数据集包括来自七种疾病（即哮喘、心力衰竭、肺炎、支气管炎、胸腔积液、肺纤维化和慢性阻塞性肺疾病（COPD））的声音以及正常呼吸声。本文呈现的数据集包含在不同有利位置对胸壁检查的音频记录。听诊器在受试者身上的放置位置由进行诊断的专科医生确定。每个记录对应于强调某些身体声音的各种频率滤波器被复制了三次。该数据集可用于开发从肺部声音检测肺部疾病或识别正确肺部声音类型的自动化方法。同样的方法也可应用于心音研究。
