Polysomnography is the diagnostic gold standard for obstructive sleep apnea-hypopnea syndrome (OSAHS), requiring medical professionals to analyze apnea-hypopnea events from multidimensional data throughout the sleep cycle. This complex process is susceptible to variability based on the clinician’s experience, leading to potential inaccuracies. Existing automatic diagnosis methods often overlook multimodal physiological signals and medical prior knowledge, leading to limited diagnostic capabilities. This study presents a novel heterogeneous graph convolutional fusion network (HeteroGCFNet) leveraging multimodal physiological signals and domain knowledge for automated OSAHS diagnosis. This framework constructs two types of graph representations: physical space graphs, which map the spatial layout of sensors on the human body, and process knowledge graphs which detail the physiological relationships among breathing patterns, oxygen saturation, and vital signals. The framework leverages heterogeneous graph convolutional neural networks to extract both localized and global features from these graphs. Additionally, a multi-head fusion module combines these features into a unified representation for effective classification, enhancing focus on relevant signal characteristics and cross-modal interactions. This study evaluated the proposed framework on a large-scale OSAHS dataset, combined from publicly available sources and data provided by a collaborative university hospital. It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. Code is available at https://github.com/AmbitYuki/HeteroGCFNet.

多导睡眠图是阻塞性睡眠呼吸暂停-低通气综合征 （OSAHS） 的诊断金标准，要求医疗专业人员在整个睡眠周期中从多维数据中分析呼吸暂停-低通气事件。根据临床医生的经验，这个复杂的过程容易受到变化的影响，从而导致潜在的不准确。现有的自动诊断方法往往忽视多模态生理信号和医学先验知识，导致诊断能力有限。本研究提出了一种新的异质 graph convolutional fusion 网络工作 （HeteroGCFNet），利用多模态生理信号和领域知识进行自动 OSAHS 诊断。该框架构建了两种类型的图表示：物理空间图，它映射了传感器在人体上的空间布局，以及过程知识图谱，它详细说明了呼吸模式、氧饱和度和生命信号之间的生理关系。该框架利用异构图卷积神经网络从这些图中提取局部和全局特征。此外，多头融合模块将这些功能组合成一个统一的表示形式，以实现有效分类，从而加强对相关信号特征和跨模态交互的关注。本研究在大规模 OSAHS 数据集上评估了拟议的框架，该数据集结合了公开可用的来源和合作大学医院提供的数据。 与传统机器学习模型和现有的深度学习方法相比，它表现出卓越的诊断性能，有效地将领域知识与数据驱动的学习相结合，以产生可解释的表示和强大的泛化能力，有可能用于临床应用。代码可在 https://github.com/AmbitYuki/HeteroGCFNet 获取。