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- W2897225663 abstract "Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Our network architecture consists of two components. Inputs were presented to a Scattering Transform (ST) representation layer which fed a recurrent neural network for sequence learning using three layers of Long Short-Term Memory (LSTM). The STs were calculated for each signal with downsampling parameters chosen to give approximately 1 s time resolution, resulting in an eighteen-fold data reduction. The LSTM layers then operated at this downsampled rate. The proposed approach detected arousal regions on the 10% random sample of the hidden test set with an AUROC of 88.0% and an AUPRC of 42.1%." @default.
- W2897225663 created "2018-10-26" @default.
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- W2897225663 date "2018-12-30" @default.
- W2897225663 modified "2023-10-12" @default.
- W2897225663 title "Sleep Arousal Detection From Polysomnography Using the Scattering Transform and Recurrent Neural Networks" @default.
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- W2897225663 doi "https://doi.org/10.22489/cinc.2018.368" @default.
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