Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory.

ABSTRACT: In this paper, a study is conducted to explore the ability of deep learning in recognizing pulmonary diseases from electronically recorded lung sounds. The selected data-set included a total of 103 patients obtained from locally recorded stethoscope lung sounds acquired at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan. In addition, 110 patients data were added to the data-set from the Int. Conf. on Biomedical Health Informatics publicly available challenge database. Initially, all signals were checked to have a sampling frequency of 4 kHz and segmented into 5 s segments. Then, several preprocessing steps were undertaken to ensure smoother and less noisy signals. These steps included wavelet smoothing, displacement artifact removal, and z-scor

SUBMITTER: Fraiwan M 

PROVIDER: S-EPMC8019351 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature