Development of Android-Based Pulmonary Monitoring System for Automated Lung Auscultation Using Long Short-Term Memory (LSTM) Network with Post-Processing from Edge Impulse

• Main Authors: Avila, Kaye Antoinette V., Cabrera, Beatrice Corine R., Reyes, Rosula, Oppus, Carlos M
• Format: text
• Published: Archīum Ateneo 2023
• Subjects: automated lung auscultation; edge impulse; LSTM; lung sound classification; pulmonary monitoring; recurrent neural network; Biomedical; Biomedical Engineering and Bioengineering; Electrical and Computer Engineering; Engineering
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/150; https://doi.org/10.1109/ECBIOS57802.2023.10218442
• Institution: Ateneo De Manila University

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.