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Found 3 papers, 2 papers with code
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study
However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models.
EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition
The results show the proposed EEGmatch performs better than the state-of-the-art methods under different incomplete label conditions (with 6. 89% improvement on SEED and 1. 44% improvement on SEED-IV), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals.
Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks
Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics.
Ranked #1 on
Blood pressure estimation
on MIMIC-III
