Development of Android-Based Pulmonary Monitoring System for Automated Lung Auscultation Using Long Short-Term Memory (LSTM) Network with Post-Processing from Edge Impulse
Document Type
Conference Proceeding
Publication Date
1-1-2023
Abstract
Chronic pulmonary diseases remain a prevalent threat globally. With the emergence of COVID-19 and its transmission, there has been a rapid increase in the number of deaths due to respiratory illnesses. In this study, lung sound classifications were performed using a Thinklabs One digital stethoscope and through the utilization of Long Short-Term Memory (LSTM) in the classification of a person's lung auscultation record into either the normal, crackle, wheeze, or stridor categories with a 92.50% accuracy. Performance evaluation of this system was also done to cross-check for the validity of the algorithm modeled through Edge Impulse, which provided a 92.77% accuracy. The integration of the system adopted an Android-based mobile application as the pulmonary monitoring platform that records a person's general respiratory health data. The inputs from the mobile application were anonymously stored in a centralized database system correspondingly for post-processing and analysis.
Recommended Citation
K. A. V. Avila, B. C. R. Cabrera, R. S. J. Reyes and C. M. Oppus, "Development of Android-Based Pulmonary Monitoring System for Automated Lung Auscultation Using Long Short-Term Memory (LSTM) Network with Post-Processing from Edge Impulse," 2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), Tainan, Taiwan, 2023, pp. 21-26, doi: 10.1109/ECBIOS57802.2023.10218442.
