DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning (2022-03-01T00:00:00.000000Z)

TL;DR

Results suggest a lightweight architecture, which can achieve similar prediction performance at a lower computational cost, is realizable in DeepSleep 2.0.

Abstract

DeepSleep 2.0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully convolutional deep neural network, which achieved the highest unofficial score in the 2018 PhysioNet Computing Challenge. The proposed network architecture has a compact encoder/decoder structure containing only 740,551 trainable parameters. The input to the network is a full-length multichannel polysomnographic recording signal. The network has been designed and optimized to efficiently predict nonapnea sleep arousals on held-out test data at a 5 ms resolution level, while not compromising the prediction accuracy. When compared to DeepSleep, the obtained experimental results in terms of gross area under the precision–recall curve (AUPRC) and gross area under the receiver operating characteristic curve (AUROC) suggest a lightweight architecture, which can achieve similar prediction performance at a lower computational cost, is realizable.

Authors

R. Fonod

3 papers

TL;DR

Abstract

Authors

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