SApneaNet：基于自适应挤压激励的CNN-Transformer网络结合AGFF用于IoMT下利用ECG图像检测睡眠呼吸暂停事件

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中文摘要

背景与目的： 阻塞性睡眠呼吸暂停（OSA）是一种致命的广泛性睡眠相关呼吸障碍，也是心脑血管疾病的主要危险因素，严重影响全球老年人的健康。由于此类并发症的风险，及时准确地识别OSA至关重要。多导睡眠图被认为是检测OSA最准确的技术；然而，其复杂性和多通道要求限制了其应用。一种有前景的替代方案是基于心电图（ECG）的诊断，它持续监测心律并捕捉与OSA相关的细微心脏变化。然而，现有的基于ECG的方法仍面临复杂特征工程、对互补的时谱信息和全局依赖关系捕捉有限以及特征重校准和融合不足等挑战，这些可能限制OSA检测。因此，仍需进一步改进以实现临床可靠的性能。方法：为应对这些挑战，本研究提出了SApneaNet，一种利用ECG信号检测OSA事件的新型先进深度学习方法。所提出的方法采用连续小波变换（CWT）将ECG信号转换为RGB对数尺度图，从而能够同时分析时域和频域特征。生成的RGB对数尺度图随后被输入到具有自适应挤压激励（ASE）的深度CNN编码器中，接着是Transformer和自适应门控特征融合（AGFF）架构。在该框架中，为提高OSA检测性能，CNN提取丰富的局部特征，ASE模块进行通道重校准以增强特征表示，Transformer执行数据并行处理并捕捉全局上下文依赖关系，AGFF机制自适应地强调信息性特征同时抑制较不相关的特征。结果：在Apnea-ECG数据集上的实验结果表明，该模型在逐段分类中实现了94.7%的灵敏度、95.2%的特异性、93.5%的F1分数、95.1%的准确率、89.4%的Cohen's kappa以及0.989的受试者工作特征（ROC）曲线下面积（AUC）。此外，对于逐记录分类，该模型实现了100.0%的准确率、2.025的平均绝对误差（MAE）和0.992的皮尔逊相关系数（PCC）。总体而言，实验结果表明，与其他先进的OSA分类最先进方法相比，所提出的模型取得了优异且有竞争力的性能。结论：所提出的模型在OSA检测中展现出强大的功效，为传统诊断方法提供了一种新颖且稳健的替代方案。该模型可靠且一致的诊断性能凸显了其整合到实际OSA诊断系统中的潜力，包括家庭健康监测设备和临床决策支持工具。

英文摘要

Background and Objective: Obstructive sleep apnea (OSA) is a fatal widespread sleep-related breathing disorder and a major risk factor for cardiovascular and cerebrovascular diseases, significantly impacting older adults' health worldwide. Due to the risk of such complications, timely and accurate identification of OSA is crucial. Polysomnography is considered the most accurate technique for detecting OSA; however, it is limited by its complexity and multi-channel requirements. A promising alternative is electrocardiogram (ECG)-based diagnosis, which continuously monitors heart rhythm and captures subtle cardiac changes associated with OSA. Nevertheless, existing ECG-based approaches still face challenges related to complex feature engineering, limited capture of complementary temporal-spectral information and global dependencies, along with inadequate feature recalibration and fusion, which can restrict OSA detection. Thus, further improvements are still required to achieve clinically reliable performance. Methods: To address these challenges, this study proposes SApneaNet, a novel advanced deep learning method for detecting OSA events using ECG signals. The proposed approach employs the continuous wavelet transform (CWT) to convert ECG signals into RGB log-scalograms, enabling the simultaneous analysis of temporal and frequency-domain features. The generated RGB log-scalograms are then fed into a deep CNN encoder with adaptive squeeze-and-excitation (ASE), followed by a transformer and an adaptive gated feature fusion (AGFF) architecture. In this framework, to improve OSA detection performance, the CNN extracts rich local features, the ASE module performs channel-wise recalibration to enhance feature representations, the transformer performs data-parallel processing and captures global contextual dependencies, and the AGFF mechanism adaptively emphasizes informative features while suppressing less relevant ones. Results: The experimental results on the Apnea-ECG dataset showed that the model achieved a sensitivity of 94.7%, specificity of 95.2%, F1-score of 93.5%, accuracy of 95.1%, Cohen's kappa of 89.4%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 for per-segment classification. Furthermore, for per-recording classification, the model achieved an accuracy of 100.0%, a mean absolute error (MAE) of 2.025, and a Pearson correlation coefficient (PCC) of 0.992. Overall, the experimental results demonstrated that the proposed model achieved excellent and competitive performance compared with other advanced state-of-the-art methods for OSA classification. Conclusions: The proposed model demonstrates strong efficacy in OSA detection, providing a novel and robust alternative to conventional diagnostic methods. The model's reliable and consistent diagnostic performance highlights its potential for integration into practical OSA diagnostic systems, including home-based health monitoring devices and clinical decision-support tools.