Augmenting multimodal pediatric sleep embeddings with PHATE trajectories, persistent homology, movement descriptors, and EHR improves AUPRC and calibration for predicting desaturation, EEG arousal, hypopnea, and apnea.
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AttDiCNN reaches 98.56%, 99.66%, and 99.08% accuracy on EDFX, HMC, and NCH sleep datasets via force-directed visibility graph EEG representations and a three-module attentive dilated CNN architecture.
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Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings
Augmenting multimodal pediatric sleep embeddings with PHATE trajectories, persistent homology, movement descriptors, and EHR improves AUPRC and calibration for predicting desaturation, EEG arousal, hypopnea, and apnea.
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Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout
AttDiCNN reaches 98.56%, 99.66%, and 99.08% accuracy on EDFX, HMC, and NCH sleep datasets via force-directed visibility graph EEG representations and a three-module attentive dilated CNN architecture.