Trained and validated on Sheep No. | No. of patterns in the train dataset | Tested on Sheep No. | No. of patterns in the Test-set | TP hits | TN hits | FP hits | FN hits | Sensitivity [%] | Selectivity [%] | Precision [%] | Accuracy [%] |
2,3,4,5,6,7 | 4567 | 1 | 443 | 173 | 266 | 4 | 2 | 100 | 98.5 | 98.7 | 99.1 |
1,3,4,5,6,7 | 4751 | 2 | 259 | 110 | 146 | 3 | 0 | 100 | 98.0 | 97.3 | 98.8 |
1,2,4,5,6,7 | 4731 | 3 | 279 | 83 | 194 | 2 | 0 | 100 | 99.0 | 97.6 | 99.3 |
1,2,3,5,6,7 | 3372 | 4 | 1638 | 823 | 803 | 11 | 1 | 99.9 | 98.6 | 98.7 | 99.3 |
1,2,3,4,6,7 | 4088 | 5 | 922 | 454 | 454 | 1 | 13 | 97.2 | 99.8 | 99.8 | 98.5 |
1,2,3,4,5,7 | 4466 | 6 | 544 | 231 | 313 | 0 | 0 | 100 | 100 | 100 | 100 |
1,2,3,4,5,6 | 4085 | 7 | 925 | 208 | 716 | 0 | 1 | 99.5 | 100 | 100 | 99.9 |
Overall performance of the 11-layers 1D-CNN in the entire 6 hours | 99.27±0.51 |
Table S9. Results of the 1D-CNN classifier for post-HI spike transient identification in experimental data (entire 6 hours – 9 layers)
Trained and validated on Sheep No. | No. of patterns in the train dataset | Tested on Sheep No. | No. of patterns in the Test-set | TP hits | TN hits | FP hits | FN hits | Sensitivity [%] | Selectivity [%] | Precision [%] | Accuracy [%] |
2,3,4,5,6,7 | 4567 | 1 | 443 | 139 | 270 | 0 | 34 | 80.3 | 100 | 100 | 92.3 |
1,3,4,5,6,7 | 4751 | 2 | 259 | 109 | 149 | 0 | 1 | 99.1 | 100 | 100 | 99.6 |
1,2,4,5,6,7 | 4731 | 3 | 279 | 73 | 196 | 0 | 10 | 88.0 | 100 | 100 | 96.4 |
1,2,3,5,6,7 | 3372 | 4 | 1638 | 822 | 813 | 1 | 2 | 99.8 | 99.9 | 99.9 | 99.8 |
1,2,3,4,6,7 | 4088 | 5 | 922 | 455 | 454 | 1 | 12 | 97.4 | 99.8 | 99.8 | 98.6 |
1,2,3,4,5,7 | 4466 | 6 | 544 | 231 | 313 | 0 | 0 | 100 | 100 | 100 | 100 |
1,2,3,4,5,6 | 4085 | 7 | 925 | 209 | 714 | 2 | 0 | 100 | 99.7 | 99.1 | 99.8 |
Overall performance of the 9 layers 1D-CNN in the entire 6 hours | 98.07±2.63 |