From: A hybrid self-attention deep learning framework for multivariate sleep stage classification
 | UCD Dataset (Time Domain) | ||||
---|---|---|---|---|---|
Method | AUC-ROC | AUC-PR | Macro-F1 | Micro-F1 | Accuracy |
PSVM | 0.4945 ±0.0068 | 0.2249 ±0.0042 | 0.1352 ±0.0388 | 0.2584 ±0.0464 | 0.3877 ±0.1107 |
DNN | 0.5024 ±0.0025 | 0.5128 ±0.0144 | 0.1129 ±0.0282 | 0.2372 ±0.1023 | 0.3962 ±0.1193 |
RNN | 0.6236 ±0.0424 | 0.3352 ±0.0358 | 0.2464 ±0.0360 | 0.3417 ±0.0908 | 0.4487 ±0.1174 |
RNNAtt l | 0.6256 ±0.0297 | 0.3254 ±0.0279 | 0.2557 ±0.0328 | 0.3483 ±0.0938 | 0.4521 ±0.1100 |
RNNAtt c | 0.6279 ±0.0549 | 0.3434 ±0.0376 | 0.2465 ±0.0314 | 0.3328 ±0.0870 | 0.4501 ±0.1087 |
CNN | 0.8421 ±0.0186 | 0.5844 ±0.0300 | 0.5775 ±0.0336 | 0.6493 ±0.0326 | 0.6595 ±0.0347 |
CRNN | 0.8453 ±0.0229 | 0.5945 ±0.0297 | 0.5761 ±0.0345 | 0.6483 ±0.0383 | 0.6592 ±0.0456 |
CRNNAtt l | 0.8461 ±0.0115 | 0.6097 ±0.0206 | 0.5954 ±0.0362 | 0.6585 ±0.0437 | 0.6659 ±0.0473 |
CRNNAtt c | 0.8505 ±0.0140 | 0.6120 ±0.0273 | 0.6004 ±0.0319 | 0.6622 ±0.0509 | 0.6632 ±0.0541 |
ChannelAtt | 0.8720 ±0.0203 | 0.6834 ±0.0278 | 0.6107 ±0.0324 | 0.6907 ±0.0545 | 0.7169 ±0.0624 |
HybridAtt l | 0.8885 ±0.0142 | 0.7009 ±0.0223 | 0.6689 ±0.0314 | 0.7264 ±0.0491 | 0.7317 ±0.0512 |
HybridAtt f | 0.8966 ±0.0214 | 0.7082 ±0.0283 | 0.6818 ±0.0304 | 0.7368 ±0.0591 | 0.7424 ±0.0594 |