Enhancing the Interpretation of Spirometry: Joint Utilization of n-Order Adaptive Fourier Decomposition and Deep Learning Techniques
See where this sits in the topic map →Summary AI-generated
- TL;DR
- We propose a novel method combining $n$-Order Adaptive Fourier Decomposition with deep learning to improve spirometry analysis for respiratory disease diagnosis.
- Problem
- Spirometry tests are crucial for diagnosing respiratory diseases, but their diagnostic accuracy often falls short. Current deep learning approaches struggle with noise from minor signals in flow-volume curves, and high computational demands limit their use in clinical practice.
- Method
- Our approach integrates $n$-Order Adaptive Fourier Decomposition (AFD) with deep learning techniques to enhance quality control in spirometry analysis. AFD improves the resolution and processing of flow-volume curves to minimize noise, while deep learning helps detect small and complex abnormalities.
- Results
- Ablation studies demonstrate that our method outperforms traditional approaches, raising the mean average precision from 89.5 percent to 95.5 percent.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- The model features a lightweight design achieved through computational optimizations and structural simplifications, enabling efficient deployment across various clinical settings.
- Applications
- Clinical respiratory diagnostics and automated spirometry quality control.
- Topics
- Spirometry analysis; $n$-Order Adaptive Fourier Decomposition; Deep learning; Biomedical signal processing
- For industry
- Healthcare and clinical instrumentation.
- Why it matters
- Improves diagnostic accuracy and accessibility for respiratory disease screening in clinical settings.
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.