Enhancing the Interpretation of Spirometry - Joint Utilization of n-Order Adaptive Fourier Decomposition and Deep Learning Techniques: Human Oversight and Decision Accountability

Human oversight is meaningful when intervention points, responsibility, escalation paths, and the evidence available to decision makers are explicitly defined. This structured evidence review evaluates "Enhancing the Interpretation of Spirometry: Joint Utilization of n-Order Adaptive Fourier Decomposition and Deep Learning Techniques" alongside nine author-disjoint, topically matched publications in biomedical signal interpretation. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through human oversight and decision accountability, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda specifies reviewer authority, override logging, escalation procedures, and evaluation of automation bias.

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2. Bonthada, S., Perumal, S.-P., Naik, P.-P., Padukudru, M.-A., & Rajan, J. (2024). An automated deep learning pipeline for detecting user errors in spirometry test. Biomedical Signal Processing and Control, 90, 105845. https://doi.org/10.1016/j.bspc.2023.105845 DOI
3. Hasan, N.-I., & Bhattacharjee, A. (2019). Deep Learning Approach to Cardiovascular Disease Classification Employing Modified ECG Signal from Empirical Mode Decomposition. Biomedical Signal Processing and Control, 52, 128-140. https://doi.org/10.1016/j.bspc.2019.04.005 DOI
4. Guo, Y., Bao, Y., Li, H., & Zhang, Y. (2023). Deep learning-based adaptive mode decomposition and instantaneous frequency estimation for vibration signal. Mechanical Systems and Signal Processing, 199, 110463. https://doi.org/10.1016/j.ymssp.2023.110463 DOI
5. Hu, Z., Yang, X., Jiang, W., & Shi, Y. (2025). Research on Deep Learning Financial Volatility Prediction Method Based on Signal Decomposition and Data Augmentation. . https://doi.org/10.21203/rs.3.rs-7726466/v1 DOI
6. Anogeianaki, A. (2007). Interpretation of Spirometry through Signal Analysis. Upsala Journal of Medical Sciences, 112(3), 313-334. https://doi.org/10.3109/2000-1967-204 DOI
7. Garro, A., & Sorrenti, A. (2025). Integrating Signal Decomposition, Stochastic Modeling, and Deep Learning for Interpretable Predictive Maintenance. 2025 IEEE International Symposium on Systems Engineering (ISSE), 1-8. https://doi.org/10.1109/isse65546.2025.11370104 DOI
8. Bai, Y., Peng, M., & Wang, M. (2024). A River Water Quality Prediction Method Based on Dual Signal Decomposition and Deep Learning. Water, 16(21), 3099. https://doi.org/10.3390/w16213099 DOI
9. Yaghi, M.-A., & Al-Omari, H. (2026). Physics-Aware Deep Learning Framework for Solar Irradiance Forecasting Using Fourier-Based Signal Decomposition. Algorithms, 19(1), 81. https://doi.org/10.3390/a19010081 DOI
10. Rajadura, V., & Ayyaswamy, K. (2026). NeuroLightNet: A Lightweight Attention-Driven Deep Learning Framework for Adaptive Electroencephalogram Signal Interpretation in Brain–Computer Interfaces. Traitement du Signal, 43(3), 1213-1226. https://doi.org/10.18280/ts.430311 DOI