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Yunzhi Tian1,2, Qiang Zhou1,2, Wan Li3,2
1School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi'an 710021, P. R. China.
This study introduces a novel transfer learning-based stochastic depth residual network (TL-SDResNet) for efficient automatic sleep staging using single-channel electroencephalogram (EEG) signals. The proposed algorithm achieves high accuracy, outperforming existing methods with faster training times.
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