A Dual-Channel CBFA Network for Sleep Respiratory Event Detection Using SpO2 and Nasal Airflow

Sleep Apnea-Hypopnea Syndrome (SAHS) is a common and severe sleep-related respiratory disorder that significantly impacts human health. Its main characteristics include the recurrent occurrence of apnea or hypopnea during sleep, leading to oxygen desaturation, disruption of sleep architecture, and various complications. Traditional diagnostic methods primarily rely on polysomnography (PSG), which, although highly valuable in clinical reference, is associated with issues such as complex testing procedures, poor real-time performance, and high costs of manual interpretation. To address these limitations, this paper proposes a deep learning-based SAHS detection method. The method utilizes blood oxygen saturation (SpO2) and nasal airflow signals from the UCD dataset as dual-channel inputs and constructs a composite model, CBFA, which integrates a one-dimensional convolutional neural network (1D-CNN), bidirectional long short-term memory (BiLSTM) network, and Forgetting Attention mechanism, for the automatic recognition and classification of sleep respiratory events. Experimental results demonstrate that the proposed model outperforms baseline methods in both classification accuracy and real-time detection capabilities, effectively extracting local abnormal features and temporal dynamic information. This method shows promising application potential and can provide technical support for the intelligent screening and auxiliary diagnosis of SAHS, with potential clinical value.