Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention.

ABSTRACT: This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, d

SUBMITTER: Im S 

PROVIDER: S-EPMC10659186 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature