Yanming Huo*, Luyuan Jia, Guo Zhang, Jiajing Ma, Congkang Zhang, Xv Guo, Yongdong Song and Shenu2019ao Hao
Volume6-Issue9
Dates: Received: 2025-09-04 | Accepted: 2025-09-13 | Published: 2025-09-16
Pages: 1244-1264
Abstract
This paper proposes a respiratory pattern recognition method for anxiety disorders based on an SVM-XGBoost-LR stacked ensemble model, enabling objective screening and early monitoring of anxiety disorders through the analysis of respiratory signals. A portable respiratory monitoring system was designed and implemented based on the ESP32 microcontroller and SM9541 high-precision pressure sensor, capable of real-time acquisition of oral-nasal airflow signals and data transmission via WiFi. In the algorithmic processing, Kalman filtering was employed for signal denoising, combined with a three-stage feature selection strategy (VIF-ANOVA-RF) to extract key respiratory features, along with the introduction of a Transformer self-attention mechanism for feature enhancement. The proposed SVM-XGBoost-LR stacked ensemble model achieved an overall classification accuracy of 96% on a dataset containing 600 samples, and improved the F1-score for the minority class (simulated breath-holding) from 60% to 87%, effectively alleviating the issue of missed diagnoses of abnormal patterns caused by class imbalance. Experimental results indicate that this method demonstrates superior performance in identifying anxiety-related respiratory abnormalities, providing a reliable algorithmic foundation and technical pathway for the development of portable mental health monitoring devices.
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DOI: 10.37871/jbres2181
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© 2025 Huo Y, et al. Distributed under Creative Commons CC-BY 4.0 
How to cite this article
Huo Y, Jia L, Zhang G, Ma J, Zhang C, Guo X, Song Y, Hao S. A Portable SM9541-Based Monitoring System and Stacked Ensemble Model for Recognizing Anxiety-Related Respiratory Patterns. J Biomed Res Environ Sci. 2025 Sept 16; 6(9): 1244- 1264. doi: 10.37871/jbres2181, Article ID: JBRES2181, Available at: https://www.jelsciences.com/articles/jbres2181.pdf
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