郑州大学学报（理学版）2026，Vol.58Issue(5)：22-29,8.DOI:10.13705/j.issn.1671-6841.2025035
基于跨域融合和动态损失的睡眠呼吸暂停检测
Sleep Apnea Detection Based on Cross-domain Fusion and Dynamic Loss
摘要
Abstract
To address the challenges of single-feature representation,insufficient cross-domain correla-tion,and data imbalance in sleep apnea detection,a novel method that integrated multi-view representa-tions with a dynamic focusing cost-sensitive loss function was proposed.Firstly,ECG signals of varying durations were mapped into images via the Gramian angular summation field transformation,constructing a temporal-spatial dual-view representation.Secondly,a dual-branch hybrid network was employed,combining the ability of adaptive gated LSTM in temporal modeling with the advantages of Res-Swin Transformer in image spatial feature extraction,and cross-domain attention mechanism was introduced to achieve feature fusion.Finally,a dynamic focusing cost-sensitive loss function was introduced to alleviate the problem of imbalanced data categories.The experimental results on the Apnea-ECG dataset showed that the proposed method achieved an accuracy of 91.2%,a sensitivity of 89.2%,and a specificity of 92.4%in segment-level detection.In individual-level detection,all three metrics reached 100%,which was significantly better than the existing methods.关键词
睡眠呼吸暂停检测/多视图/动态聚焦代价敏感损失函数/格拉姆角和场Key words
sleep apnea detection/multi-view/dynamic focusing cost-sensitive loss function/Gramian angle summation field分类
信息技术与安全科学引用本文复制引用
李奇,刘方姿,张航,武岩,赵文丽..基于跨域融合和动态损失的睡眠呼吸暂停检测[J].郑州大学学报（理学版）,2026,58(5):22-29,8.基金项目
吉林省科技发展计划项目(20240101344JC,20230203098SF) （20240101344JC,20230203098SF）
中山市社会福利与基础研究项目(2023B2015) （2023B2015）
