Research Article
Wavelets and Morphological Operators Based Classification of Epilepsy Risk Levels
Table 14
Performance analysis of twenty patients using morphological filters with SVD, EM, and MEM postclassifiers.
| Parameters | Code converter method | SVD optimization | With EM optimization | With MEM optimization |
| Risk level classification rate (%) | 62.6 | 91.22 | 87.27 | 88.71 | Weighted delay (s) | 2.34 | 2.26 | 2.2 | 2.18 | False alarm rate/set | 19.13 | 1.42 | 5.47 | 5.67 | Performance Index % | 33.26 | 89.48 | 85.03 | 86.95 | Sensitivity | 77.84 | 98.57 | 95.59 | 98.97 | Specificity | 78.91 | 92.65 | 98.11 | 97.67 | Average detection | 78.875 | 95.61 | 96.85 | 98.32 | Relative risk | 1.166 | 1.063 | 0.974 | 1.013 | Quality Value | 12.74 | 20.62 | 19.52 | 20.3 |
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