From: A hybrid self-attention deep learning framework for multivariate sleep stage classification
 | UCD Dataset (frequency Domain) | ||||
---|---|---|---|---|---|
Method | AUC-ROC | AUC-PR | Macro-F1 | Micro-F1 | Accuracy |
PSVM | 0.8177 ±0.0142 | 0.5767 ±0.0172 | 0.5204 ±0.0275 | 0.5854 ±0.0733 | 0.6193 ±0.1053 |
DNN | 0.7213 ±0.1435 | 0.5224 ±0.1048 | 0.3542 ±0.2171 | 0.4331 ±0.2269 | 0.5262 ±0.1613 |
RNN | 0.6228 ±0.0465 | 0.3350 ±0.0394 | 0.2663 ±0.0241 | 0.3970 ±0.0428 | 0.5091 ±0.0391 |
RNNAtt l | 0.6172 ±0.0386 | 0.3305 ±0.0386 | 0.2457 ±0.0307 | 0.3734 ±0.0566 | 0.5002 ±0.0476 |
RNNAtt c | 0.6234 ±0.0451 | 0.3335 ±0.0345 | 0.2554 ±0.0258 | 0.3712 ±0.0325 | 0.5010 ±0.0367 |
CNN | 0.8732 ±0.0129 | 0.6725 ±0.0120 | 0.5925 ±0.0604 | 0.6492 ±0.0841 | 0.6590 ±0.0979 |
CRNN | 0.8660 ±0.0074 | 0.6454 ±0.0135 | 0.5693 ±0.0060 | 0.6395 ±0.0370 | 0.6634 ±0.0412 |
CRNNAtt l | 0.8570 ±0.0183 | 0.6281 ±0.0359 | 0.5810 ±0.0371 | 0.6486 ±0.0641 | 0.6683 ±0.0657 |
CRNNAtt c | 0.8671 ±0.0274 | 0.6418 ±0.0401 | 0.5849 ±0.0577 | 0.6528 ±0.0547 | 0.6791 ±0.0546 |
ChannelAtt | 0.8705 ±0.0483 | 0.6818 ±0.0580 | 0.6517 ±0.0334 | 0.7070 ±0.0605 | 0.7152 ±0.0574 |
HybridAtt l | 0.8719 ±0.0214 | 0.6669 ±0.0297 | 0.6342 ±0.0316 | 0.6962 ±0.0645 | 0.7070 ±0.0707 |
HybridAtt f | 0.8854 ±0.0137 | 0.6886 ±0.0256 | 0.6639 ±0.0301 | 0.7231 ±0.0489 | 0.7328 ±0.0546 |