Table II.
Performance obtained by the proposed SeqSleepNet, the developed baselines, and existing works on the MASS dataset. We mark the proposed SeqSleepNet in bold, the developed baselines in italic, and existing works in normal font. SeqSleepNet-L indicates a SeqSleepNet with sequence length of L, a similar notation is used for E2E-DeepSleepNet baseline.
| Method | Feature type | Num. of subjects | Overall metrics | Class-wise sensitivity | Class-wise selectivity | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Acc. | κ | MF1 | Sens. | Spec. | W | N1 | N2 | N3 | REM | W | N1 | N2 | N3 | REM | |||||
| Multi-output Systems | SeqSleepNet-30 | ARNN + RNN | learned | 200 | 87.1 | 0.815 | 83.3 | 82.7 | 96.2 | 89.0 | 59.7 | 90.9 | 80.2 | 93.5 | 90.7 | 65.1 | 88.9 | 84.2 | 90.7 |
| SeqSleepNet-20 | ARNN + RNN | learned | 200 | 87.0 | 0.815 | 83.3 | 82.8 | 96.3 | 89.4 | 60.8 | 90.7 | 80.3 | 92.9 | 90.0 | 65.1 | 89.1 | 84.0 | 90.8 | |
| SeqSleepNet-10 | ARNN + RNN | learned | 200 | 87.0 | 0.814 | 83.2 | 82.4 | 96.2 | 88.6 | 59.9 | 91.2 | 79.4 | 93.0 | 91.3 | 64.9 | 88.6 | 85.1 | 90.2 | |
| E2E-DeepSleepNet-30 | CNN + RNN | learned | 200 | 86.4 | 0.805 | 82.2 | 81.8 | 96.1 | 89.2 | 55.8 | 90.5 | 83.1 | 90.3 | 88.8 | 62.6 | 88.8 | 82.0 | 91.1 | |
| E2E-DeepSleepNet-20 | CNN + RNN | learned | 200 | 86.2 | 0.804 | 82.2 | 82.0 | 96.1 | 88.4 | 57.0 | 89.9 | 84.1 | 90.4 | 89.0 | 62.1 | 89.0 | 81.1 | 91.2 | |
| E2E-DeepSleepNet-10 | CNN + RNN | learned | 200 | 86.3 | 0.804 | 82.0 | 81.6 | 96.1 | 88.4 | 55.6 | 90.3 | 83.4 | 90.6 | 88.8 | 62.0 | 89.0 | 82.3 | 90.2 | |
| M-E2E-ARNN | ARNN | learned | 200 | 83.8 | 0.767 | 77.7 | 77.0 | 95.3 | 85.0 | 37.4 | 89.2 | 79.2 | 94.2 | 86.5 | 61.4 | 86.5 | 82.6 | 81.9 | |
| Multitask 1-max CNN [8] | CNN | learned | 200 | 83.6 | 0.766 | 77.9 | 77.4 | 95.3 | 84.6 | 41.1 | 88.5 | 79.7 | 93.3 | 86.3 | 55.2 | 86.9 | 83.0 | 83.3 | |
| DeepSleepNet2 [9] | CNN + RNN | learned | 62 (SS3) | 86.2 | 0.800 | 81.7 | - | - | - | - | - | - | - | - | - | - | - | - | |
| Dong et al. [25] | DNN + RNN | learned | 62 (SS3) | 85.9 | - | 80.5 | - | - | - | - | - | - | - | - | - | - | - | - | |
| Single-output Systems | E2E-ARNN | ARNN | learned | 200 | 83.6 | 0.766 | 78.4 | 78.0 | 95.3 | 86.6 | 43.7 | 87.8 | 80.9 | 91.2 | 86.3 | 57.6 | 87.2 | 82.3 | 82.4 |
| 1-max CNN [8] | CNN | learned | 200 | 82.7 | 0.754 | 77.6 | 77.8 | 95.1 | 84.8 | 46.8 | 86.4 | 82.0 | 88.6 | 86.2 | 49.8 | 87.4 | 80.2 | 84.2 | |
| Chambon et al. [13] | CNN | learned | 200 | 79.9 | 0.726 | 76.7 | 80.0 | 95.0 | 81.1 | 64.2 | 76.2 | 89.6 | 89.0 | 86.7 | 41.0 | 92.4 | 73.1 | 82.6 | |
| DeepSleepNet1 [9] | CNN (only) | learned | 200 | 80.7 | 0.725 | 75.8 | 75.5 | 94.5 | 80.0 | 51.9 | 85.5 | 69.0 | 91.1 | 87.5 | 46.2 | 85.3 | 84.9 | 79.7 | |
| Tsinalis et al. [10] | CNN | learned | 200 | 77.9 | 0.680 | 70.4 | 69.4 | 93.5 | 82.3 | 30.5 | 86.8 | 61.7 | 85.8 | 77.5 | 44.7 | 80.6 | 80.0 | 80.0 | |
| Chambon et al. [13] | CNN | learned | 61 (SS3) | 83.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | |
| DeepSleepNet1 [9] | CNN (only) | learned | 62 (SS3) | 81.5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | |
| Dong et al. [25] | DNN (only) | learned | 62 (SS3) | 81.4 | - | 77.2 | - | - | - | - | - | - | - | - | - | - | - | - | |
| Dong et al. [25] | RF | hand-crafted | 62 (SS3) | 81.7 | - | 72.4 | - | - | - | - | - | - | - | - | - | - | - | - | |
| Dong et al. [25] | SVM | hand-crafted | 62 (SS3) | 79.7 | - | 75.0 | - | - | - | - | - | - | - | - | - | - | - | - | |