Table 6.
Studies exploring model-based TCD methods
| Method | References | Population | Classification use score | Estimation use score |
|---|---|---|---|---|
| Data-based models | ||||
| Schmidt system ID model [154] | Schmidt [154] | TBI, 11 | – | 2 |
| Schmidt [155] | TBI, 17 | – | 2 | |
| Schmidt [156] | Hydrocephalus, 21 | – | 3 | |
| Budohoski [157] | TBI, 292 | – | 2 | |
| Cardim [147] | TBI, 40 | 2 | 2 | |
| Cardim [148] | Hydrocephalus, 53 | – | 1 | |
| Schmidt [158] | TBI, 137 | – | 3 | |
| Schmidt [159] | Varied cerebral diseases, 41 | – | 2 | |
| SCA Schmidt model [160] | Schmidt [160] | TBI, 135; hemorrhagic stroke, 10 | – | 2 |
| Schmidt fuzzy pattern model [161] | Schmidt [161] | TBI, 103 | 3 | 2 |
| Calibrated Schmidt model [162] | Schmidt [162] | Brain lesions, 13 | – | 2 |
| Schmidt [159] | Varied cerebral diseases, 41 | – | 2 | |
| Nonlinear Schmidt model [163] | Xu [163] | TBI, 14; hydrocephalus, 9 | – | 2 |
| Data mining [164] | Hu [164] | TBI, 9 | – | 2 |
| Kim [165] | TBI, 57 | – | 2 | |
| Ensemble sparse classifiers [166] | Kim [166] | TBI, 80 | 2 | – |
| Semisupervised learning model [167] | Kim [167] | TBI/SAH/NPH, 90 | 4 | – |
| Linear discriminant analysis [168] | Aggarwal [168] | ALF, 16 | 2 | – |
| SVM [169] | Chacon [169] | TBI, 8 | – | 4 |
| Random forest [170] | Wadehn [170] | TBI, 36 | 3 | – |
| Theory-based models | ||||
| Kashif model [171] | Kashif [171] | TBI, 37 | 3 | 3 |
| Park [125] | TBI, 11 | 2 | 3 | |
| Pressure corrected Kashif model [172] | Noraky [172] | SAH, 5 | – | 3 |
| DC Kashif model [125] | Park [125] | TBI, 11 | 3 | 3 |
| Hybrid models | ||||
| Hybrid model | Wang [173] | SAH/TBI, 2 | – | 4 |
Refer to Table 3 for interpretation of scores. If a method was not evaluated in the context of either classification or evaluation, then no score is provided for that use case