Enhancing Respiratory Disease Diagnosis through FMCW Radar and Machine Learning Techniques
DOI:
https://doi.org/10.33379/gtech.v8i1.3693Keywords:
machine learning, fmcw radar, radar, respiratory, respiratory diseaseAbstract
This study addresses the urgent need for early diagnosis and continuous monitoring of respiratory diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and infectious diseases. We have created a system that combines state-of-the-art machine learning algorithms with frequency-modulated continuous wave (FMCW) radar technology. FMCW radar technology is sensitive to small respiratory movements, allowing real-time monitoring without physical contact. Machine learning algorithms, including Decision Trees, Random Forest, Naïve Bayes, Gradient Boosting, and Support Vector Machines, are employed to classify these waveforms. The Random Forest classifier achieved the highest accuracy score of 94.6%, with Naïve Bayes exhibiting the shortest processing time at 0.055 seconds. We explored various cross-validation methods such as Shuffle Split, K-fold, and Stratified K-fold, with the Shuffle Split method performing best overall in terms of accuracy and time. Our study introduces an integrated system that could revolutionize the early detection, response, and tracking of respiratory diseases and emergencies over time.
References
Ahmad, A., Roh, J. C., Wang, D., & Dubey, A. (2018). Vital signs monitoring of multiple people using a FMCW millimeter-wave sensor. In 2018 IEEE Radar Conference (RadarConf18), 1450-1455.
Baker, N., Thevasagayam, R., Ugonna, K., & Kirkby, J. (2020). Pediatric dysfunctional breathing: proposed components, mechanisms, diagnosis, and management. Frontiers in Pediatrics, 8, 379.
Connett, G. J., & Thomas, M. (2018). Dysfunctional breathing in children and adults with asthma. Frontiers in pediatrics, 6, 406.
Da Costa, C. A., Pasluosta, C. F., Eskofier, B., Da Silva, D. B., & da Rosa Righi, R. (2018). Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards. Artificial intelligence in medicine, 89, 61-69.
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756.
Halpin, D. M., Criner, G. J., Papi, A., Singh, D., Anzueto, A., Martinez, F. J., ... & Vogelmeier, C. F. (2021). Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. The 2020 GOLD science committee report on COVID-19 and chronic obstructive pulmonary disease. American journal of respiratory and critical care medicine, 203(1), 24-36.
Kavsaoğlu, A. R., & Sehirli, E. (2023). A novel study to classify breath inhalation and breath exhalation using audio signals from heart and trachea. Biomedical Signal Processing and Control, 80, 104220.
Khan, F., Ghaffar, A., Khan, N., & Cho, S. H. (2020). An overview of signal processing techniques for remote health monitoring using impulse radio UWB transceiver. Sensors, 20(9), 2479.
Lee, J. W., Chun, W., Lee, H. J., Min, J. H., Kim, S. M., Seo, J. Y., Ahn, K. S., & Oh, S. R. (2021). The role of macrophages in the development of acute and chronic inflammatory lung diseases. Cells, 10(4), 897.
Newson, T. P., & Elias, A. (2020). Breathing pattern disorders (dysfunctional breathing) characteristics and outcomes of children and young people attending a secondary care respiratory clinic. Pediatric Pulmonology, 55(7), 1736-1744.
Purnomo, A. T., Lin, D. B., Adiprabowo, T., & Hendria, W. F. (2021). Non-contact monitoring and classification of breathing pattern for the supervision of people infected by COVID-19. Sensors, 21(9), 3172.
Purnomo, A. T., Komariah, K. S., Lin, D. B., Hendria, W. F., Sin, B. K., & Ahmadi, N. (2022). Non-contact supervision of COVID-19 breathing behaviour with FMCW radar and stacked ensemble learning model in real-time. IEEE Transactions on Biomedical Circuits and Systems, 16(4), 664-678.
Romero Starke, K., Friedrich, S., M, S., Kämpf, D., Girbig, M., Pretzsch, A., Nienhaus, A., & Seidler, A. (2021). Are healthcare workers at an increased risk for obstructive respiratory diseases due to cleaning and disinfection agents? A systematic review and meta-analysis. International journal of environmental research and public health, 18(10), 5159.
Turppa, E., Kortelainen, J. M., Antropov, O., & Kiuru, T. (2020). Vital sign monitoring using FMCW radar in various sleeping scenarios. Sensors, 20(22), 6505.
Wang, Q., Dong, Z., Liu, D., Cao, T., Zhang, M., Liu, R., Zhong, X., & Sun, J. (2021). Frequency-modulated continuous wave radar respiratory pattern detection technology based on multifeature. Journal of Healthcare Engineering, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Ariana Tulus Purnomo, Raffy Frandito, Edrick Hansel Limantoro, Rafie Djajasoepena, Muhammad Agni Catur Bhakti, Ding-Bing Lin
This work is licensed under a Creative Commons Attribution 4.0 International License.
