A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants
Amir H. Ansari(IMEC), Maarten De Vos(Rega Institute for Medical Research), Kirubin Pillay(University of Oxford), Sabine Van Huffel(KU Leuven), A Dereymaeker(Universitair Ziekenhuis Leuven), Gunnar Naulaers(KU Leuven), Ofelie De Wel(IMEC), Katrien Jansen(KU Leuven)
Cited by 62
Related Papers
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
|BMJ|2020|3.3k
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
|IEEE Transactions on Neural Systems and Rehabilitation Engineering|2019|608
How about taking a low‐cost, small, and wireless <scp>EEG</scp> for a walk?
|Psychophysiology|2012|555
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
|IEEE Transactions on Biomedical Engineering|2018|468