A Deep Learning Approach to Localizing Multi-level Airway Collapse Based on Snoring Sounds
<|reference_start|>A Deep Learning Approach to Localizing Multi-level Airway Collapse Based on Snoring Sounds: This study investigates the application of machine/deep learning to classify snoring sounds excited at different levels of the upper airway in patients with obstructive sleep apnea (OSA) using data from drug-induced sleep endoscopy (DISE). The snoring sounds of 39 subjects were analyzed and labeled according to the Velum, Oropharynx, Tongue Base, and Epiglottis (VOTE) classification system. The dataset, comprising 5,173 one-second segments, was used to train and test models, including Support Vector Machine (SVM), Bidirectional Long Short-Term Memory (BiLSTM), and ResNet-50. The ResNet-50, a convolutional neural network (CNN), showed the best overall performance in classifying snoring acoustics, particularly in identifying multi-level obstructions. The study emphasizes the potential of integrating snoring acoustics with deep learning to improve the diagnosis and treatment of OSA. However, challenges such as limited sample size, data imbalance, and differences between pharmacologically induced and natural snoring sounds were noted, suggesting further research to enhance model accuracy and generalizability.<|reference_end|>
@article{hsu2024a,
    title={A Deep Learning Approach to Localizing Multi-level Airway Collapse Based
  on Snoring Sounds},
    author={Ying-Chieh Hsu, Stanley Yung-Chuan Liu, Chao-Jung Huang, Chi-Wei Wu,
  Ren-Kai Cheng, Jane Yung-Jen Hsu, Shang-Ran Huang, Yuan-Ren Cheng, Fu-Shun
  Hsu},
    journal={arXiv preprint arXiv:2408.16030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16030},
    primaryClass={cs.SD cs.AI cs.LG eess.AS}
}