NAMRTNet: Automatic Classification of Sleep Stages Based on Improved ResNet-TCN Network and Attention Mechanism  
[PERSON]  
1 School of Computer Science and Technology, Xi'an University of Posts & Telecommunications, Xi'an 710121, China; ccp99999126.com (X.X. ); [EMAIL_ADDRESS] (K.M. ); [EMAIL_ADDRESS] (L.L. ); [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (H.F)  
[PERSON]  
1 School of Computer Science and Technology, Xi'an University of Posts & Telecommunications, Xi'an 710121, China; ccp99999126.com (X.X. ); [EMAIL_ADDRESS] (K.M. ); [EMAIL_ADDRESS] (L.L. ); [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (H.F)  
[PERSON]  
1 School of Computer Science and Technology, Xi'an University of Posts & Telecommunications, Xi'an 710121, China; ccp99999126.com (X.X. ); [EMAIL_ADDRESS] (K.M. ); [EMAIL_ADDRESS] (L.L. ); [EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (H.F)  
Longbin Lu  
1 School of Computer Science and Technology, Xi'an University of Posts & Telecommunications, Xi'an 710121, China; ccp99999126.com (X.X. ); [EMAIL_ADDRESS] (K.M. ); [EMAIL_ADDRESS] (L.L. ); cox  
criteria of the AASM (American Academy of Sleep Medicine): W, N1, N2, and N3, REM. The need for manual classification and statistical analysis of sleep stages based on the characteristics of brain waves during different sleep periods and personal experience makes manual sleep staging a tedious and time-consuming task, and the staging results are highly subjective [4]. With the increasing number of people suffering from sleep disorders and the growing impact of other sleep problems, many researchers have developed automatic sleep detection classification based on machine learning and deep learning [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Based on traditional machine learning, such as decision trees [5], random forests [6; 7], and support vector machines [8; 9; 10], the construction and selection of features for these methods are critical. Standard features can be broadly classified into time domain, frequency domain, time-frequency, and nonlinear features. Considering that all EEG sleep information is contained in the time domain waveform, the first approach is to extract valuable features directly from the time domain. [PERSON] used spectral analysis to classify EEG signals into five bands: \(\alpha\), \(\theta\), \(\delta\), \(\beta\), and \(\sigma\), and found that \(\delta\) and \(\sigma\) waves can distinguish sleep states well [11]. After long-term research, the widely used time-frequency analysis methods include the short time Fourier transform [12], wavelet transform [13], and Hilbert-Yellow transform [14]. The nonlinear features are mainly based on entropy and complexity. Although the traditional machine learning-based sleep detection grading method has achieved some results, the feature data extracted by this method is not comprehensive enough, and the accuracy of sleep scoring is not high enough. With the gradual advancement of research, neural network-based deep learning techniques have attracted a lot of attention from researchers. Compared with traditional machine learning, deep learning can automatically learn multidimensional abstract features of data from different perspectives by overlaying network layers and powerful neurons. Several studies have designed convolutional neural networks (CNNs) [15; 16; 17; 18; 19; 20; 21]. On study [15] developed a high-precision real-time automatic k-complex wave detection system using multiple CNN networks with migration learning to implement a convolutional neural network (faster R-CNN) detector with faster regions. Another study [16] used end-to-end training with two pairs of convolutional layers for filtering, pooling layers for subsampling, a backpropagation algorithm for iterative optimization training, and a class balancing processor for batch sampling to solve typical data imbalance problems.