CFSCNet: A Coarse-Fine Stream Conformer Neural Network for Swallow Segmentation.

This study develops an efficient and accurate method for segmenting complex swallowing events in patients with dysphagia. We propose CFSCNet, a novel model that integrates multimodal signals-audio, nasal airflow, and triaxial accelerometry-to enhance swallowing event segmentation. The model employs a dual-stream architecture to capture swallowing information across varying durations and utilizes an exponential moving average-based cost-sensitive weighting method to address data imbalance. To further improve the stability of predictions for clinical applications, a Swallowing State Machine-Driven Post-Processing Method is introduced to smooth the segmentation sequences. CFSCNet achieves state-of-the-art performance on two benchmark datasets, reaching an AUC of 89.83 on the HRCA dataset and 94.10 on the SMSD dataset, significantly outperforming existing methods. Built on a series of tailored methodological innovations, the proposed framework offers a comprehensive and novel solution for swallowing event segmentation. It demonstrates strong accuracy and reliability, as well as great potential for future extension and clinical translation. This work is expected to advance high-precision detection and diagnosis of dysphagia, thereby supporting more effective swallowing rehabilitation and medical interventions.