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 | 169 | 270 | 4 | 0 | 100 | 97.7 | 100 | 99.1 |
1,3,4,5,6,7 | 4751 | 2 | 259 | 110 | 149 | 0 | 0 | 100 | 100 | 100 | 100 |
1,2,4,5,6,7 | 4731 | 3 | 279 | 82 | 196 | 1 | 0 | 100 | 98.8 | 100 | 99.6 |
1,2,3,5,6,7 | 3372 | 4 | 1638 | 823 | 812 | 1 | 2 | 99.8 | 99.9 | 99.8 | 99.8 |
1,2,3,4,6,7 | 4088 | 5 | 922 | 455 | 455 | 12 | 0 | 100 | 97.4 | 100 | 98.7 |
1,2,3,4,5,7 | 4466 | 6 | 544 | 231 | 312 | 0 | 1 | 99.6 | 100 | 99.7 | 99.8 |
1,2,3,4,5,6 | 4085 | 7 | 925 | 201 | 716 | 0 | 8 | 100 | 96.2 | 100 | 99.1 |
Overall performance of the 11 layers WF-CNN in the entire 6 hours | 99.44±0.44 |
Table S5. Results of the WF-CNN classifier for post-HI spike transient identification in experimental data (entire 6 hours – 9 layers)