## 4 Results and Discussion
### Comparisons and Discussion  
The proposed approach is compared with the following eight techniques using the same datasets:  
(1) Raw EEG + ResNet: this approach uses the raw multi-channel EEG signals as input to the ResNet architecture used in the proposed framework. Comparison with this technique highlights the benefit of using the PSD of the EEG signals over the raw EEG signals as input.  
(2) PSD + CNN (5 layers): this approach uses the same input data, i.e., PSD of raw EEG signals, but uses a 5-layer CNN instead of a ResNet. Comparison with this technique highlights the benefit of using ResNet compared to CNN.  
(3) PSD + CNN (10 layers): this approach uses the same input data, i.e., PSD of raw EEG signals, but uses a 10-layer CNN instead of a ResNet. Comparison with this technique highlights the benefit of using ResNet compared to a CNN with 10 layers.  
(4) PSD + CNN (14 layers): this approach uses the same input data, i.e., PSD of raw EEG signals, but uses a 10-layer CNN instead of a ResNet. Comparison with this technique and the two techniques prior to it highlights the benefit of using ResNet compared to CNN with increasing number of layers.  
(5) PSD + CNN (18 layers): this approach uses the same input data, i.e., PSD of raw EEG signals, but uses an 18-layer CNN instead of a ResNet. The number of layers in the CNN is the same as the number of layers in the ResNet in the proposed framework. Nevertheless, the performance of ResNet is better in comparison.  
(6) FE + RF: this approach uses time-frequency based features extraction (FE) and a random forest (RF) classifier [41]. Comparison with this approach highlights the significance of both the input data and ResNet architecture used in the proposed framework.  
(7) FFT + MPC: this approach uses fast Fourier transform (FFT) based feature analysis and a multilayered perceptron (MPC) based classifier [42] to highlight the importance of the deeper residual network architecture.  
(8) PSD + MPC: this approach uses the PSD of EEG signals as input to a MPC based classifier to highlight the importance of the deeper residual network architecture.  
Comparison of all these approaches with the proposed framework is given in Table 4.  
Figure 6: Training loss curve comparisons for different datasets.  
The results in Table 4 clearly show that the proposed approach is significantly more effective than all the other approaches in correctly identifying sleep stages in both the genders. The \"Improvement\" column in Table 4 shows the percentage improvement in absolute terms that the proposed method has over the other techniques. The comparison results show that the proposed framework (PSD + ResNet) yields an average performance improvement of 3.8-40.6% and 1.3-37.9% for female and male subjects, respectively.  
The results in Table 3 show that the performance of raw EEG + ResNet is inferior to the proposed PSD + ResNet based framework, which indicates that the ResNet architecture is more efficient in learning the smaller changes of the subjective sleep states from the PSD compared to the raw EEG. Achieving the same performance with the raw EEG as input would require a deeper ResNet architecture with more identity shortcut connections. However, as the input length of raw EEG is far greater than the PSD, it would increase the training time for the ResNet. Moreover, approaches based on deep learning do not require artifact removal from the raw data, since these approaches are very good at automatically learning small changes in the data distribution [30,31,49].