Deep-learning-based breathing pattern classification method for real-time monitoring of patients with infectious respiratory disease

Jinho Park; Thien Nguyen; Amir Gandjbakhche

doi:10.1117/12.3003602

13 March 2024 Deep-learning-based breathing pattern classification method for real-time monitoring of patients with infectious respiratory disease

Jinho Park, Thien Nguyen, Amir Gandjbakhche

Author Affiliations +

Proceedings Volume PC12838, Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables V; PC1283803 (2024) https://doi.org/10.1117/12.3003602
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

The global outbreak of novel Coronavirus Disease (COVID-19) in 2019 required a method for detecting and continuously monitoring patients with an infectious respiratory disease. Patients infected with acute respiratory disease show symptoms of shallow, rapid breathing and dyspnea due to hypoxia-hypercapnia.

In this paper, we develop a system for monitoring of patients with infectious respiratory disease in real time using NIRS sensors and classifies breathing patterns using deep learning algorithm.

Conference Presentation

Citation Download Citation

Jinho Park, Thien Nguyen, and Amir Gandjbakhche "Deep-learning-based breathing pattern classification method for real-time monitoring of patients with infectious respiratory disease", Proc. SPIE PC12838, Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables V, PC1283803 (13 March 2024); https://doi.org/10.1117/12.3003602

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