A compact U-Net-inspired convolutional neural network with 740,551 parameters, designed to predict non-apnea sleep arousals from full-length multi-channel polysomnographic recordings at 5-millisecond resolution. Achieves similar performance to DeepSleep with lower computational cost.
machine-learning deep-learning time-series cnn pytorch neural-networks segmentation convolutional-networks physionet multichannel encoder-decoder sleep-analysis u-net medical-ai polysomnography computational-efficiency sleep-apnea sleep-arousals
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Updated
Oct 22, 2024 - Jupyter Notebook