Table 4.
Comparisons between related works.
| Comparison with other works | |||||||
|---|---|---|---|---|---|---|---|
| Work | Year | Features | Classifier | Classes | Acc | Se | PPV |
| Nazarahari et al. [8] | 2015 | Wavelet + distances measures | Multilayer perception | Normal, PVC, APC, paced, LBBB, RBBB | 97.51 | — | — |
| Martis et al. [9] | 2013 | QRS, bispectrum, PCA | SVM NN | N, LBBB, RBBB, APC, VPC | 93.48 | — | — |
| Afkhami et al. [10] | 2016 | RR interval, HOS, GMM | Decision trees, ensemble learnes | AAMI, all classification in MIT-BIH | 99.7 | 100 | 100 |
| Javadi et al. [11] | 2013 | Wavelet + morpho-logical and temporal features | Mixture of experts, negative correlation learning | N, PVC, other | 96.02 | 92.27 | 79.4 |
| Kamath [12] | 2011 | Teager energy functions in time and frequency domains | Neural network | N, LBBB, RBBB, PVC, paced beats | 100 | 100 | 100 |
| Martis et al. [13] | 2013 | DWT + PCA + ICA + LDA | SVM, NN, PNN | AAMI | 99.28 | — | — |
| Sharma and Ray [14] | 2016 | Hilbert–Huang transform, statistical features | SVM | N, LBBB, RBBB, PVC, paced, APC | 99.51 | 99.36 | 100 |
| Banerjee and Mitra [15] | 2014 | Cross wavelet transform | Heuristic classification | Abnormal versus normal | 97.6 | 97.3 | 98.8 |
| Oliveira et al. [16] | 2016 | Dynamic Bayesian networks | Dynamic threshold | PVC versus others | 99.88 | 99 | 99 |
| Work | FSC, SFE | QDA | NB, PVC, OB | 98.3 | 100 | 98 | |