DFCNet: A Precise Detection Approach for Obstructive Sleep Apnea-Hypopnea Events Using Airflow and Respiratory Effort Signals

OBJECTIVE: The diagnosis of Sleep Apnea-Hypopnea Syndrome (SAHS) holds significant importance for assessing sleep quality and treating sleep disorders. However, the detection of hypopnea events has not been given due emphasis, and the precise delineation of event boundaries is not straightforward. In this work, we introduce a novel deep learning model for the precise detection of obstructive sleep apnea and hypopnea events. METHODS: Respiration-related signals, processed through a sliding window, serve as inputs to the model. Initially, multi-scale features are extracted using the Dilated Pyramid Convolution module, followed by an adaptive refinement of these features using the Frequency Enhanced Attention module. Finally, the Contextual Representation Learning module captures the temporal dependencies within the features. RESULTS: The model was validated on two public datasets and one local dataset, achieving an accuracy of 84.4%, a precision of 66.3%, a recall of 84.5%, and an F1 score of 72.3% on the SHHS2 dataset. CONCLUSION AND SIGNIFICANCE: We have achieved an automatic detection of both obstructive sleep apnea and hypopnea events with a granularity of one second. Our method offers certain advantages over other approaches, with the potential to assist in clinical diagnosis and to enable home-based respiratory monitoring.