J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (4): 720-732.doi: 10.1007/s12204-024-2734-z
收稿日期:
2023-05-16
接受日期:
2023-06-27
发布日期:
2025-07-31
马进1,任泽1,张彤彤1,丁颍2,陆熠磊1,彭颖红3
Received:
2023-05-16
Accepted:
2023-06-27
Published:
2025-07-31
摘要: 自动睡眠分期由于其在分析整晚多导睡眠(PSG)信号方面具有高效性,能够有效支持临床专家对睡眠障碍进行诊疗。然而,现有的研究主要集中在与实际临床数据不相同的公共数据集上。为了缩小理论模型与实际临床实践之间的差距,提出了一种新的深度学习模型,将视觉Transformer与监督对比学习相结合,实现有效的睡眠阶段分期。实验结果表明,该模型能够更有效地对多通道PSG信号进行分期。在两个公开的睡眠数据库上该模型平均F1得分分别为79.2%和76.5%,优于之前的研究,表明了该模型强大的能力,在儿童小数据库上的平均准确率也达到了88.6%。提出的模型不仅在公共数据库上进行了验证,而且在提供的临床数据库上进行了验证,以严格评估其在临床实践中的使用情况。
中图分类号:
. 基于Transformer对比学习的自动睡眠分期方法[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(4): 720-732.
Ma Jin, Ren Ze, Zhang Tongtong, Ding Ying, Lu Yilei, Peng Yinghong. Transformer-Based Contrastive Learning Method for Automated Sleep Stages Classification[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(4): 720-732.
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