# Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Diagnosis (Male)**} & \multicolumn{4}{c}{_Precision (\%)_} & \multicolumn{4}{c}{_Recall (\%)_} & **Acc.** & **Perf.** & **10-Fold** \\ \cline{3-10}  & & _C1_ & _C2_ & _C3_ & _C1_ & _C2_ & _C3_ & **(\%)** & & **Cross-Valid.** \\ \hline
1 & Healthy Ind. & - F5 & 89.4 & 90.1 & 81.8 & 89.9 & 92.6 & 71.7 & 88.9 & 0.0977 & 0.15 \\  & & - F12 & 89.8 & 90.7 & 79.5 & 91.6 & 92.2 & 70.3 & 89.1 & 0.0941 & 0.12 \\ \hline
2 & Sleep Arena & - F5 & 92.9 & 94.8 & 79.7 & 94.8 & 95.4 & 72.5 & 92.7 & 0.0632 & 0.11 \\  & & - F12 & 93.8 & 95.4 & 81.4 & 95.3 & 95.6 & 76.5 & 93.5 & 0.0598 & 0.07 \\ \hline
3 & RL Syndrome & - F5 & 84.5 & 94.7 & 81.7 & 88.8 & 92.7 & 82.5 & 90.1 & 0.0845 & 0.13 \\  & & - F12 & 86.0 & 95.0 & 84.3 & 89.3 & 93.5 & 85.1 & 91.1 & 0.0758 & 0.10 \\ \hline
4 & SA and RL Syn. & - F5 & 91.5 & 88.1 & 76.7 & 86.8 & 94.0 & 62.5 & 87.8 & 0.0996 & 0.19 \\  & & - F12 & 90.9 & 89.3 & 83.2 & 88.3 & 94.2 & 67.6 & 89.1 & 0.0973 & 0.15 \\ \hline \multicolumn{10}{c}{**Diagnosis (Female)**} & \multicolumn{4}{c}{_Precision (\%)_} & \multicolumn{4}{c}{_Recall (\%)_} & **Acc.** & **Perf.** & **10-Fold** \\ \cline{3-10}  & & _C1_ & _C2_ & _C3_ & _C1_ & _C2_ & _C3_ & **(\%)** & & **Cross-Valid.** \\ \hline
1 & Healthy Ind. & - F5 & 82.9 & 90.6 & 72.5 & 77.5 & 92.1 & 73.8 & 86.0 & 0.1186 & 0.16 \\  & & - F12 & 83.0 & 91.9 & 76.9 & 81.8 & 92.5 & 76.2 & 87.6 & 0.1055 & 0.14 \\ \hline
2 & Sleep Apnea & - F5 & 86.0 & 91.7 & 73.5 & 90.6 & 93.3 & 62.4 & 88.0 & 0.1061 & 0.17 \\  & & - F12 & 87.5 & 92.2 & 74.7 & 92.4 & 93.4 & 64.1 & 88.7 & 0.1015 & 0.15 \\ \hline
3 & RL Syndrome & - F5 & 85.9 & 92.0 & 81.3 & 86.3 & 93.1 & 75.9 & 89.3 & 0.1006 & 0.15 \\  & & - F12 & 87.5 & 92.2 & 80.0 & 86.3 & 93.5 & 76.7 & 89.6 & 0.0953 & 0.12 \\ \hline
4 & SA and RL Syn. & - F5 & 83.0 & 89.7 & 81.9 & 84.0 & 91.4 & 72.6 & 86.9 & 0.1063 & 0.21 \\  & & - F12 & 84.0 & 90.9 & 83.9 & 86.1 & 91.9 & 75.3 & 88.1 & 0.1049 & 0.14 \\ \hline \end{tabular}
\end{table} 
Table 6: Evaluation of sleep stage classification (Classes: C1—Wake, C2—NonREM, C3—REM) of sets of (i) healthy individuals; (ii) individuals with sleep apnea (SA); (iii) individuals with the restless leg (RL) syndrome, and (iv) individuals with sleep apnea and RL syndrome using the single EEG channel for 5 (F5) mean EEG power features (in bands 1–4, 4–8, 8–12, 12–16, and 16–20 Hz), and 12 (F12) features with additional 4 features evaluated from the flow channel (for frequency bands 0.05–0.2, 0.2–0.3, 0.3–0.4, 0.4–0.8 Hz) and 3 features related to the electro-oculogram channels (4–15, 15–30, 30–40 Hz). \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{_Diagnosis_} & \multicolumn{4}{c}{_Wake Segments_} & \multicolumn{2}{c}{_REM Segments_} \\ \cline{2-5}  & _RC_ & _Confidence Bounds_ & _RC_ & _Confidence Bounds_ \\ \hline All & 0.272 & (0.145, 0.399) & \(-0.082\) & (\(-0.141\), \(-0.023\)) \\
1 & 0.437 & (0.227, 0.648) & \(-0.094\) & (\(-0.198\), \(0.010\)) \\
2 & 0.248 & (0.055, 0.442) & \(-0.119\) & (\(-0.216\), \(-0.022\)) \\
3 & \(-0.064\) & (\(-0.687, 0.559\)) & 0.009 & (\(-0.334\), \(0.353\)) \\
4 & 0.178 & (\(-0.377\), 0.734) & 0.057 & (\(-0.133\), \(0.246\)) \\ \hline \end{tabular}
\end{table} 
Table 5: The summary of regression coefficients (RC) of the age dependence of Wake and REM sleep stages with their 95% confidence bounds. An analysis of the features in each 30 s long PSG segment was then performed for spectral components in selected regions for the EEG, Flow and OCG channels for all 184 individuals. This resulted in the specification of the pattern matrix for the adaptive classification by the two layer neural network after the learning process based on associated target values. Results for the training, validation and test sets for healthy individuals (after about 15,000 training epochs) are presented in associated confusion matrices presented in Table 7 for classification into three classes using 12 (F12) multimodal (EEG, Flow and ECG) features.