Scholarlink

<p>Accurately detecting sleep stages and sleep apnea is essential for assessing sleep quality and diagnosing related health issues, especially in the elderly. However, most existing deep learning models focus on single-task scenarios, overlooking the synergy between sleep staging and apnea detection. Moreover, they don’t consider the impact of age on sleep. To address these issues, this study selects 40 subjects aged 60–90 from the Human Sleep Project dataset as research participants. Supervised principal component analysis is applied to process their polysomnography signals, and S-transform is used to generate S-dynamic spectrograms as model inputs. Furthermore, a deep learning model (RGMNet) based on a multitasking has been designed specifically for older adults. By leveraging its unique feature extraction and interaction mechanisms, RGMNet effectively enables simultaneous sleep staging and apnea detection on datasets of older adults. Experimental results demonstrate that the proposed RGMNet model achieves sleep staging and apnea detection accuracies of 86.61% and 84.12%, respectively, outperforming existing multi-task and single-task methods. These findings highlight the effectiveness of RGMNet in improving the accuracy of simultaneous sleep staging and apnea detection for older adults, providing valuable insights for advancements in sleep medicine and related fields.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Multi-Task Deep Learning Approach for Simultaneous Sleep Staging and Apnea Detection for Elderly People

  • 期刊论文

  • 发表日期：

    2025年6月5日

查看全文
  • Lei Shi,
  • Ranran Gui,
  • Li Wang,
  • Peng Li,
  • Qunfeng Niu

摘要

Accurately detecting sleep stages and sleep apnea is essential for assessing sleep quality and diagnosing related health issues, especially in the elderly. However, most existing deep learning models focus on single-task scenarios, overlooking the synergy between sleep staging and apnea detection. Moreover, they don’t consider the impact of age on sleep. To address these issues, this study selects 40 subjects aged 60–90 from the Human Sleep Project dataset as research participants. Supervised principal component analysis is applied to process their polysomnography signals, and S-transform is used to generate S-dynamic spectrograms as model inputs. Furthermore, a deep learning model (RGMNet) based on a multitasking has been designed specifically for older adults. By leveraging its unique feature extraction and interaction mechanisms, RGMNet effectively enables simultaneous sleep staging and apnea detection on datasets of older adults. Experimental results demonstrate that the proposed RGMNet model achieves sleep staging and apnea detection accuracies of 86.61% and 84.12%, respectively, outperforming existing multi-task and single-task methods. These findings highlight the effectiveness of RGMNet in improving the accuracy of simultaneous sleep staging and apnea detection for older adults, providing valuable insights for advancements in sleep medicine and related fields.

Graphical Abstract