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. 2026 Jul 21;16:33171. doi: 10.1038/s41598-026-62064-7

Table 2.

Table shows the performance metrics for each model tested across all 10 of our test mice. The ‘avg’ columns contains the mean and standard deviation over all mice. We can see that our method performs the best on our dataset. Best results in bold.

Classifier Metric M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 avg
Logistic Regression32 precision 0.09 ± 0.00 0.09 ± 0.00 0.10 ± 0.00 0.09 ± 0.00 0.11 ± 0.00 0.10 ± 0.00 0.12 ± 0.00 0.07 ± 0.00 0.07 ± 0.00 0.03 ± 0.00 0.09 ± 0.00
recall 0.98 ± 0.00 0.96 ± 0.00 0.97 ± 0.00 0.98 ± 0.00 0.97 ± 0.00 0.98 ± 0.00 0.97 ± 0.00 0.98 ± 0.00 0.98 ± 0.00 0.99 ± 0.00 0.98 ± 0.00
f1-score 0.16 ± 0.00 0.17 ± 0.00 0.18 ± 0.00 0.16 ± 0.00 0.20 ± 0.00 0.19 ± 0.00 0.22 ± 0.00 0.13 ± 0.00 0.12 ± 0.00 0.06 ± 0.00 0.16 ± 0.00
K-Nearest Neighbors33 precision 0.15 ± 0.00 0.19 ± 0.00 0.14 ± 0.00 0.15 ± 0.00 0.26 ± 0.00 0.17 ± 0.00 0.25 ± 0.00 0.13 ± 0.00 0.10 ± 0.00 0.06 ± 0.00 0.16 ± 0.00
recall 0.70 ± 0.00 0.83 ± 0.00 0.66 ± 0.00 0.79 ± 0.00 0.82 ± 0.00 0.75 ± 0.00 0.71 ± 0.00 0.89 ± 0.00 0.76 ± 0.00 0.83 ± 0.00 0.77 ± 0.00
f1-score 0.25 ± 0.00 0.31 ± 0.00 0.24 ± 0.00 0.25 ± 0.00 0.39 ± 0.00 0.27 ± 0.00 0.37 ± 0.00 0.23 ± 0.00 0.18 ± 0.00 0.11 ± 0.00 0.26 ± 0.00
Decision Tree34 precision 0.27 ± 0.00 0.22 ± 0.00 0.25 ± 0.01 0.26 ± 0.00 0.34 ± 0.01 0.25 ± 0.00 0.36 ± 0.00 0.18 ± 0.00 0.14 ± 0.00 0.09 ± 0.00 0.23 ± 0.00
recall 0.56 ± 0.00 0.66 ± ± 0.00 0.45 ± 0.00 0.60 ± 0.00 0.55 ± 0.01 0.60 ± 0.00 0.64 ± 0.00 0.75 ± 0.01 0.62 ± 0.01 0.59 ± 0.00 0.60 ± 0.00
f1-score 0.36 ± 0.00 0.33 ± 0.01 0.32 ± 0.01 0.36 ± 0.00 0.42 ± 0.00 0.36 ± 0.00 0.46 ± 0.00 0.29 ± 0.00 0.23 ± 0.00 0.16 ± 0.00 0.33 ± 0.00
Random Forest35 precision 0.28 ± 0.01 0.22 ± 0.01 0.24 ± 0.01 0.29 ± 0.01 0.47 ± 0.01 0.20 ± 0.01 0.36 ± 0.00 0.20 ± 0.00 0.13 ± 0.01 0.09 ± 0.01 0.25 ± 0.01
recall 0.37 ± 0.01 0.55 ± 0.02 0.26 ± 0.01 0.44 ± 0.01 0.36 ± 0.02 0.32 ± 0.01 0.34 ± 0.00 0.71 ± 0.02 0.46 ± 0.05 0.44 ± 0.03 0.42 ± 0.02
f1-score 0.32 ± 0.01 0.32 ± 0.01 0.25 ± 0.01 0.35 ± 0.01 0.41 ± 0.02 0.25 ± 0.01 0.35 ± 0.00 0.31 ± 0.01 0.20 ± 0.02 0.15 ± 0.01 0.29 ± 0.01
CNN36 precision 0.66 ± 0.57 0.87 ± 0.06 0.67 ± 0.58 0.67 ± 0.58 0.92 ± 0.04 0.88 ± 0.03 0.84 ± 0.03 0.77 ± 0.03 0.83 ± 0.02 0.33 ± 0.58 0.74 ± 0.21
recall 0.01 ± 0.01 0.34 ± 0.24 0.01 ± 0.01 0.01 ± 0.01 0.78 ± 0.15 0.83 ± 0.04 0.86 ± 0.07 0.89 ± 0.03 0.73 ± 0.06 0.00 ± 0.01 0.45 ± 0.05
f1-score 0.01 ± 0.02 0.46 ± 0.26 0.01 ± 0.02 0.02 ± 0.02 0.84 ± 0.08 0.85 ± 0.01 0.85 ± 0.02 0.83 ± 0.01 0.77 ± 0.03 0.01 ± 0.01 0.47 ± 0.03
PyramidalCNN37 precision 0.67 ± 0.58 0.88 ± 0.02 0.67 ± ± 0.58 0.67 ± 0.58 0.86 ± 0.03 0.82 ± 0.02 0.78 ± 0.01 0.73 ± 0.03 0.77 ± 0.02 0.33 ± 0.58 0.72 ± 0.20
recall 0.10 ± 0.09 0.63 ± 0.05 0.00 ± 0.00 0.08 ± 0.08 0.92 ± 0.04 0.93 ± 0.03 0.95 ± 0.02 0.95 ± 0.03 0.84 ± 0.04 0.01 ± 0.01 0.54 ± 0.04
f1-score 0.17 ± 0.15 0.73 ± 0.03 0.01 ± 0.01 0.14 ± 0.14 0.89 ± 0.00 0.87 ± 0.00 0.86 ± 0.00 0.82 ± 0.01 0.81 ± 0.01 0.01 ± 0.02 0.53 ± 0.03
LSTM38 precision 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00
recall 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00
f1-score 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00
biLSTM39 precision 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00
recall 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00
f1-score 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00 0.00 ± 0.00
ConvLSTM40 precision 0.00 ± 0.00 0.95 ± 0.02 0.00 ± 0.00 0.33 ± 0.58 0.97 ± 0.01 0.97 ± 0.02 0.95 ± 0.01 0.82 ± 0.03 0.96 ± 0.02 0.00 ± 0.00 0.60 ± 0.06
recall 0.00 ± 0.00 0.57 ± 0.07 0.00 ± 0.00 0.00 ± 0.00 0.87 ± 0.02 0.71 ± 0.07 0.81 ± 0.08 0.91 ± 0.03 0.65 ± 0.07 0.00 ± 0.00 0.45 ± 0.02
f1-score 0.00 ± 0.00 0.71 ± 0.06 0.00 ± 0.00 0.00 ± 0.00 0.92 ± 0.02 0.82 ± 0.05 0.87 ± 0.05 0.86 ± 0.01 0.77 ± 0.05 0.00 ± 0.00 0.50 ± 0.02
EEGNet18 precision 0.01 ± 0.02 0.54 ± 0.45 0.02 ± 0.03 0.01 ± 0.02 0.54 ± 0.48 0.12 ± 0.16 0.25 ± 0.38 0.62 ± 0.14 0.20 ± 0.31 0.00 ± 0.01 0.23 ± 0.14
recall 0.01 ± 0.02 0.18 ± 0.18 0.04 ± 0.06 0.03 ± 0.06 0.58 ± 0.50 0.34 ± 0.56 0.40 ± 0.53 0.85 ± 0.12 0.38 ± 0.54 0.02 ± 0.03 0.28 ± 0.21
f1-score 0.01 ± 0.02 0.27 ± 0.25 0.03 ± 0.04 0.02 ± 0.03 0.56 ± 0.49 0.05 ± 0.05 0.15 ± 0.16 0.70 ± 0.06 0.09 ± 0.11 0.01 ± 0.01 0.19 ± 0.10
InceptionTime19 precision 0.85 ± 0.25 0.88 ± 0.06 0.67 ± 0.58 1.00 ± 0.00 0.89 ± 0.04 0.88 ± 0.02 0.84 ± 0.02 0.72 ± 0.02 0.82 ± 0.02 1.00 ± 0.00 0.86 ± 0.05
recall 0.03 ± 0.04 0.62 ± 0.13 0.00 ± 0.01 0.04 ± 0.06 0.90 ± 0.04 0.88 ± 0.03 0.92 ± 0.02 0.94 ± 0.02 0.79 ± 0.02 0.01 ± 0.00 0.51 ± 0.03
f1-score 0.05 ± 0.07 0.72 ± 0.08 0.01 ± 0.01 0.07 ± 0.11 0.89 ± 0.01 0.88 ± 0.02 0.88 ± 0.02 0.81 ± 0.01 0.81 ± 0.02 0.01 ± 0.00 0.51 ± 0.03
Xception41 precision 0.02 ± 0.03 0.94 ± 0.01 0.02 ± 0.03 0.02 ± 0.03 0.92 ± 0.02 0.90 ± 0.04 0.87 ± 0.04 0.75 ± 0.05 0.87 ± 0.06 0.00 ± 0.01 0.53 ± 0.02
recall 0.31 ± 0.54 0.59 ± 0.07 0.31 ± 0.54 0.31 ± 0.54 0.91 ± 0.01 0.89 ± 0.05 0.94 ± 0.02 0.96 ± 0.01 0.82 ± 0.03 0.30 ± 0.52 0.63 ± 0.22
f1-score 0.03 ± 0.05 0.72 ± 0.05 0.04 ± 0.06 0.03 ± 0.06 0.92 ± 0.01 0.90 ± 0.01 0.90 ± 0.02 0.84 ± 0.03 0.84 ± 0.01 0.01 ± 0.02 0.52 ± 0.02
U-Net20 precision 0.99 ± 0.01 0.62 ± 0.51 1.00 ± 0.00 0.99 ± 0.01 0.95 ± 0.02 0.96 ± 0.01 0.94 ± 0.01 0.57 ± 0.11 0.94 ± 0.00 0.98 ± 0.02 0.90 ± 0.05
recall 0.26 ± 0.19 0.34 ± 0.08 0.20 ± 0.27 0.40 ± 0.26 0.86 ± 0.06 0.69 ± 0.13 0.75 ± 0.12 0.97 ± 0.03 0.61 ± 0.11 0.24 ± 0.31 0.53 ± 0.13
f1-score 0.38 ± 0.24 0.39 ± 0.28 0.29 ± 0.34 0.54 ± 0.26 0.90 ± 0.02 0.80 ± 0.09 0.83 ± 0.08 0.71 ± 0.09 0.74 ± 0.08 0.32 ± 0.38 0.59 ± 0.15
SalientSleepNet23 precision 0.27 ± 0.46 0.60 ± 0.41 0.27 ± 0.47 0.25 ± 0.43 0.86 ± 0.09 0.55 ± 0.48 0.85 ± 0.13 0.66 ± 0.01 0.52 ± 0.45 0.21 ± 0.37 0.50 ± 0.25
recall 0.28 ± 0.48 0.44 ± 0.37 0.30 ± 0.52 0.31 ± 0.53 0.69 ± 0.45 0.61 ± 0.53 0.64 ± 0.55 0.95 ± 0.00 0.55 ± 0.48 0.30 ± 0.51 0.51 ± 0.39
f1-score 0.27 ± 0.47 0.50 ± 0.41 0.28 ± 0.49 0.27 ± 0.47 0.67 ± 0.34 0.58 ± 0.50 0.57 ± 0.49 0.78 ± 0.01 0.53 ± 0.46 0.25 ± 0.43 0.47 ± 0.36
DETRtime22 precision 0.06 ± 0.01 0.05 ± 0.01 0.04 ± 0.04 0.04 ± 0.02 0.06 ± 0.01 0.07 ± 0.01 0.06 ± 0.03 0.02 ± 0.02 0.04 ± 0.01 0.01 ± 0.00 0.05 ± 0.01
recall 0.34 ± 0.28 0.32 ± 0.27 0.36 ± 0.31 0.33 ± 0.28 0.31 ± 0.25 0.36 ± 0.30 0.35 ± 0.29 0.31 ± 0.27 0.33 ± 0.26 0.33 ± 0.28 0.33 ± 0.28
f1-score 0.07 ± 0.04 0.07 ± 0.04 0.08 ± 0.07 0.07 ± 0.05 0.08 ± 0.04 0.09 ± 0.05 0.10 ± 0.07 0.04 ± 0.04 0.05 ± 0.01 0.02 ± 0.01 0.07 ± 0.04
Twin Peaks24 precision 0.57 ± 0.00 0.96 ± 0.00 0.59 ± 0.00 0.64 ± 0.00 0.82 ± 0.00 0.82 ± 0.00 0.89 ± 0.00 0.39 ± 0.00 0.71 ± 0.00 0.25 ± 0.00 0.67 ± 0.00
recall 0.73 ± 0.00 0.45 ± 0.00 0.83 ± 0.00 0.92 ± 0.00 0.82 ± 0.00 0.89 ± 0.00 0.86 ± 0.00 0.92 ± 0.00 0.81 ± 0.00 0.95 ± 0.00 0.82 ± 0.00
f1-score 0.64 ± 0.00 0.62 ± 0.00 0.69 ± 0.00 0.75 ± 0.00 0.81 ± 0.00 0.85 ± 0.00 0.87 ± 0.00 0.55 ± 0.00 0.76 ± 0.00 0.40 ± 0.00 0.69 ± 0.00
AugUNet1D (ours) precision 0.92 ± 0.01 0.96 ± 0.01 0.92 ± 0.01 0.87 ± 0.01 0.97 ± 0.02 0.95 ± 0.01 0.94 ± 0.02 0.83 ± 0.02 0.91 ± 0.03 0.79 ± 0.03 0.91 ± 0.06
recall 0.84 ± 0.01 0.66 ± 0.09 0.94 ± 0.00 0.97 ± 0.00 0.89 ± 0.02 0.95 ± 0.00 0.96 ± 0.01 0.95 ± 0.00 0.89 ± 0.02 0.96 ± 0.01 0.90 ± 0.09
f1-score 0.87 ± 0.00 0.78 ± 0.06 0.93 ± 0.01 0.92 ± 0.01 0.93 ± 0.00 0.95 ± 0.00 0.95 ± 0.01 0.89 ± 0.01 0.90 ± 0.01 0.87 ± 0.02 0.90 ± 0.05