Table 3 Summary of the classification performances of previous methods
Method (feature + classification) | Total classification accuracy (%) |
|---|---|
Database 1 | |
RQA + SVM (Sultornsanee et al. 2011) | 98.28 |
VVA + SVM (Artameyanant et al. 2014) | 99.07 |
WVA + MLPNN (Artameyanant et al. 2015) | 94.73 |
Proposed method | 99.17 |
Database 2 | |
AR + WNN (Subasi et al. 2006) | 90.70 |
CWT + SVM (Istenic et al. 2010) | 70.40 |
AR + neuro-fuzzy system (Kocer 2010) | 90.00 |
94.00 | |
DWT + ESVM (Subasi 2013a) | 97.00 |
DWT + PSO-SVM (Subasi 2013b) | 97.41 |
DWT + random forest (Gokgoz and Subasi 2015) | 96.67 |
Proposed method | 98.36 |