Table 4.
Recent publications based on HRV + Machine Learning. The accuracy produced and the theoretical computational cost required by the algorithm.
| References | Accuracy (%) | Computational cost | ML algorithm(s) |
|---|---|---|---|
| Castaldo et al. (41) | 94,88,94,94 | 0(n),0(kd),0(nlogn),0(nd2) | MLP, SVM, C4.5,LDA |
| Cho et al. (70) | 90.19 | 0(n·k·d) | CNN |
| Cho et al. (26) | 95 | 0(n4) | K-ELM |
| Coutts et al. (71) | 83 | 0(W) W = 4IH+4H2+3H+HK |
LTSM |
| Taye et al. (72) | 98.6 | 0(W) W = IH+HK |
ANN |
| Arsalan et al. (73) | 92.85 | 0(n) | MLP |
| Lima et al. (38) | 80 | 0(n*log(n)*d*k) | Random Forest |
| Kublanov et al. (74) | 91.3,87.8, 87.1,88.2 |
0(nd2),0(kd), 0(n*log(n)*d), 0(c*d) |
LDA,SVM,DT,NB |
| Ma et al. (75) | 96.58, 98.2 |
0(n·k·d), 0(n) |
CNN, MLP |
| Persson et al. (76) | 77.5,83.4, 82.4,85.4 |
0(nd),0(n2), 0(nt),0(n*log(n)*d*k) |
KNN, SVM, AdaBoost, RF |