This study proposes a novel lightweight neural network model leveraging features extracted from electrocardiogram (ECG) and respiratory signals for early OSA screening. ECG signals are used to generate feature spectrograms to predict sleep stages, while respiratory signals are employed to detect sleep-related breathing abnormalities. By integrating these predictions, the method calculates the apnea-hypopnea index (AHI) with enhanced accuracy, facilitating precise OSA diagnosis. The method was validated on three publicly available sleep apnea databases: the Apnea-ECG database, the UCDDB dataset, and the MIT-BIH Polysomnographic database. Results showed an overall OSA detection accuracy of 0.978, highlighting the model's robustness. Respiratory event classification achieved an accuracy of 0.969 and an area under the receiver operating characteristic curve (ROC-AUC) of 0.98. For sleep stage classification, in UCDDB dataset, the ROC-AUC exceeded 0.85 across all stages, with recall for Sleep reaching 0.906 and specificity for REM and Wake states at 0.956 and 0.937, respectively. This study underscores the potential of integrating lightweight neural networks with multi-signal analysis for accurate, portable, and cost-effective OSA screening, paving the way for broader adoption in home-based and wearable health monitoring systems.

本研究解决了传统睡眠呼吸暂停筛查方法准确性不足的问题，提出了一种新颖的轻量级神经网络模型，利用心电图（ECG）和呼吸信号提取特征，以实现早期阻塞性睡眠呼吸暂停（OSA）筛查。研究结果显示，该方法在三个公开的睡眠呼吸暂停数据库上进行了验证，整体OSA检测准确率达到0.978，展现了其在居家和可穿戴健康监测系统中的广泛应用潜力。

MobileNetV2：一种用于居家睡眠呼吸暂停筛查的轻量级分类模型