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

Abstract

Spirometry plays a key role in diagnosing respiratory diseases, but its accuracy often falls short of clinical expectations. While deep learning models have shown promise in automating spirometry analysis, challenges persist. Spirometry curves are vulnerable to noise from minor signals, which can lead to diagnostic errors. Moreover, the high computational demands of current algorithms limit their use in clinical practice. To address these issues, we present a novel approach that integrates n-Order Adaptive Fourier Decomposition with deep learning techniques to enhances quality control in spirometry analysis. Adaptive Fourier Decomposition improves the resolution and processing of flow-volume curves, effectively minimizing noise. By leveraging the strengths of deep learning models alongside AFD, our method accurately detects small and complex abnormalities in spirometry data. Ablation studies show that our method outperforms traditional approaches, raising the mean average precision from 89.5 percent to 95.5 percent. Furthermore, the model’s lightweight design, achieved through computational optimizations and structural simplifications, enables efficient deployment in various clinical settings, improving diagnostic accuracy and accessibility.