# A Domain-Based, Adaptive, Multi-Scale, Inter-Subject Sleep Stage Classification Network
## 4 Discussion
While MF causes the model to pay more attention to the tail class, it helps the model to learn the overall feature distribution better during domain adaptation. Moreover, the additional loss of sequence prediction plays a role in the improvement in accuracy and Kappa. From the comparison of the results in the table, predicting the current sleep stage based on the previous 30 s ECG provides information about the state transition to assist the model in predicting sleep stages more accurately. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & \multicolumn{2}{c}{**SHHS1\(-\)MESA**} & \multicolumn{2}{c}{**SHHS2\(-\)MESA**} & \multicolumn{2}{c}{**SHHS2\(-\)SLPDB**} & \multicolumn{2}{c}{**SHHS2\(-\)SLPHIS1**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**CK**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**CK**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**CK**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**CK**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**CK**} \\ \hline Multi-scale residual & Before DA & 0.524 \(\pm\) 0.042 & 0.325 \(\pm\) 0.038 & 0.541 \(\pm\) 0.054 & 0.297 \(\pm\) 0.043 & 0.423 \(\pm\) 0.098 & 0.105 \(\pm\) 0.042 & 0.436 \(\pm\) 0.052 & 0.235 \(\pm\) 0.046 \\ module (proposed) & After DA & 0.851 \(\pm\) 0.032 & 0.803 \(\pm\) 0.054 & 0.857 \(\pm\) 0.034 & 0.809 \(\pm\) 0.042 & 0.822 \(\pm\) 0.025 & 0.747 \(\pm\) 0.055 & 0.798 \(\pm\) 0.039 & 0.747 \(\pm\) 0.057 \\ \hline \multirow{2}{*}{CNN block module} & Before DA & 0.531 \(\pm\) 0.042 & 0.232 \(\pm\) 0.055 & 0.567 \(\pm\) 0.020 & 0.337 \(\pm\) 0.056 & 0.477 \(\pm\) 0.068 & 0.106 \(\pm\) 0.063 & 0.467 \(\pm\) 0.038 & 0.182 \(\pm\) 0.019 \\  & After DA & 0.751 \(\pm\) 0.034 & 0.607 \(\pm\) 0.080 & 0.774 \(\pm\) 0.020 & 0.705 \(\pm\) 0.032 & 0.668 \(\pm\) 0.043 & 0.384 \(\pm\) 0.138 & 0.746 \(\pm\) 0.032 & 0.667 \(\pm\) 0.062 \\ \hline Residual module & Before DA & 0.597 \(\pm\) 0.031 & 0.304 \(\pm\) 0.025 & 0.553 \(\pm\) 0.003 & 0.220 \(\pm\) 0.065 & 0.527 \(\pm\) 0.040 & 0.110 \(\pm\) 0.063 & 0.525 \(\pm\) 0.037 & 0.323 \(\pm\) 0.043 \\ (kernel size: 5) & After DA & 0.801 \(\pm\) 0.008 & 0.627 \(\pm\) 0.036 & 0.739 \(\pm\) 0.018 & 0.655 \(\pm\) 0.041 & 0.665 \(\pm\) 0.013 & 0.405 \(\pm\) 0.027 & 0.723 \(\pm\) 0.017 & 0.599 \(\pm\) 0.029 \\ \hline Residual module & Before DA & 0.489 \(\pm\) 0.046 & 0.251 \(\pm\) 0.056 & 0.492 \(\pm\) 0.045 & 0.307 \(\pm\) 0.071 & 0.423 \(\pm\) 0.142 & 0.115 \(\pm\) 0.047 & 0.511 \(\pm\) 0.044 & 0.322 \(\pm\) 0.050 \\ (kernel size: 11) & After DA & 0.842 \(\pm\) 0.034 & 0.760 \(\pm\) 0.066 & 0.883 \(\pm\) 0.009 & 0.772 \(\pm\) 0.069 & 0.774 \(\pm\) 0.074 & 0.657 \(\pm\) 0.125 & 0.805 \(\pm\) 0.052 & 0.709 \(\pm\) 0.066 \\ \hline Residual module & Before DA & 0.529 \(\pm\) 0.039 & 0.218 \(\pm\) 0.035 & 0.518 \(\pm\) 0.034 & 0.262 \(\pm\) 0.030 & 0.324 \(\pm\) 0.024 & 0.044 \(\pm\) 0.032 & 0.425 \(\pm\) 0.038 & 0.203 \(\pm\) 0.045 \\ (kernel size: 21) & After DA & 0.761 \(\pm\) 0.051 & 0.634 \(\pm\) 0.097 & 0.824 \(\pm\) 0.108 & 0.755 \(\pm\) 0.181 & 0.700 \(\pm\) 0.109 & 0.433 \(\pm\) 0.317 & 0.753 \(\pm\) 0.066 & 0.615 \(\pm\) 0.147 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy and Kappa of sleep staging across datasets.