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Multimodal Sleep Apnea Detection with Missing or Noisy Modalities
Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0. 9) even in the presence of high levels of noise or missingness.
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