Table 4 Performance comparison of the proposed method with other state-of-the-art using the Chapman dataset
Ref | Study | Dataset | Num. of subjects | Year | Method | Classes | Performance |
|---|---|---|---|---|---|---|---|
[46] | Yildirim et al. | Chapman | 10,646 | 2020 | DNN | 7 | Acc = 92.24% |
[47] | Meqdad et al. | Chapman | 10,646 | 2022 | CNN Trees | 7 | Acc = 97.60% |
[48] | Meqdad et al. | Chapman | 10,646 | 2022 | Meta CNN Trees | 7 | Acc = 98.29% |
[49] | Mehari et al. | Chapman | 10,646 | 2022 | Single Classifier | 7 | Acc = 92.89% |
[50] | Rahul et al. | Chapman | 10,646 | 2022 | 1-D CNN | 7 | Acc = 94.01% |
[51] | Kang et al. | Chapman | 10,646 | 2022 | RNN | 7 | Acc = 96.21% |
[52] | Domazetoski et al. | Chapman | 10,646 | 2022 | XGBoost | - | Acc = 89.40% |
[53] | Sepahvand et al. | Chapman | 10,646 | 2022 | Teacher model | 7 | Acc = 98.96% |
Student model | 7 | Acc = 98.13% | |||||
|  | Proposed | Chapman | 10,646 | 2022 | GCN-WMI | 7 | Acc = 99.82% |