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<p>Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a prevalent chronic disorder that affects sleep quality and general health. The current diagnostic methods, primarily polysomnography (PSG), are laborious. Furthermore, audio-based methods for diagnosing OSAHS face limited sample sizes and neglect patients’ physiological signs and medical histories. To address these challenges, we introduce a data-driven framework called DFNet, which also considers patients’ medical histories and health indicators. DFNet incorporates an automated audio segmentation- and labeling-based preprocessing procedure to reduce expert annotation costs and subjective errors. We employed random convolutional kernels based on receptive fields for audio feature extraction purposes. These kernels captured both local and global features within the input audio. Additionally, for the first time, we introduced a medical language model that utilizes patients’ medical histories and physiological information as covariates to enhance features. We extensively validated DFNet on an OSAHS dataset obtained from a collaborative university hospital. Our framework classified patients into four categories according to their OSAHS severity: normal, mild, moderate, and severe. DFNet achieved state-of-the-art performance, with a four-class accuracy of 84.12%. DFNet offers a large-scale and cost-effective screening approach for diagnosing OSAHS, reducing the labor requirements of diagnosis. Our code is available at <a href="https://github.com/testlbin/DFNet">https://github.com/testlbin/DFNet</a>.</p>

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An end-to-end audio classification framework with diverse features for obstructive sleep apnea-hypopnea syndrome diagnosis

  • 期刊论文

  • 发表日期：

    2025年2月10日

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  • Bin Li,
  • Xihe Qiu,
  • Xiaoyu Tan,
  • Long Yang,
  • Jing Tao,
  • Zhijun Fang,
  • Jingjing Huang

摘要

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a prevalent chronic disorder that affects sleep quality and general health. The current diagnostic methods, primarily polysomnography (PSG), are laborious. Furthermore, audio-based methods for diagnosing OSAHS face limited sample sizes and neglect patients’ physiological signs and medical histories. To address these challenges, we introduce a data-driven framework called DFNet, which also considers patients’ medical histories and health indicators. DFNet incorporates an automated audio segmentation- and labeling-based preprocessing procedure to reduce expert annotation costs and subjective errors. We employed random convolutional kernels based on receptive fields for audio feature extraction purposes. These kernels captured both local and global features within the input audio. Additionally, for the first time, we introduced a medical language model that utilizes patients’ medical histories and physiological information as covariates to enhance features. We extensively validated DFNet on an OSAHS dataset obtained from a collaborative university hospital. Our framework classified patients into four categories according to their OSAHS severity: normal, mild, moderate, and severe. DFNet achieved state-of-the-art performance, with a four-class accuracy of 84.12%. DFNet offers a large-scale and cost-effective screening approach for diagnosing OSAHS, reducing the labor requirements of diagnosis. Our code is available at https://github.com/testlbin/DFNet.