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Enhancing Efficiency in CNN-Based Respiratory Sound Analysis through Temporal Self-Attention and Frequency Band

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    This study introduces a new lightweight network for automated lung sound classification. The CNN-TSA model with Frequency Band Selection (FBS) offers efficient, real-time analysis for healthcare in resource-limited settings.

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    Area of Science:

    • Medical Informatics
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Automating lung sound classification requires capturing local spectral patterns and long-range temporal dependencies.
    • Traditional models like CNNs struggle with long-range dependencies, while Transformers are computationally expensive for real-time applications.

    Purpose of the Study:

    • To develop a computationally efficient and effective model for real-time lung sound analysis.
    • To address the limitations of existing models in resource-constrained healthcare environments.

    Main Methods:

    • A lightweight CNN-based Temporal Self-Attention (CNN-TSA) network was proposed.
    • A Frequency Band Selection (FBS) strategy was integrated to eliminate irrelevant frequency bands.
    • The model was evaluated on the SPRSound 2022 dataset.

    Main Results:

    • The proposed CNN-TSA network with FBS achieved state-of-the-art results.
    • Computational costs were reduced by 50% without performance compromise.
    • The approach demonstrated enhanced noise resilience and generalization.

    Conclusions:

    • The efficient CNN-TSA framework facilitates reliable real-time lung sound analysis.
    • This method is a promising solution for healthcare applications in resource-limited environments.
    • The integration of FBS improves model robustness and efficiency.