Research Article
A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning
Table 6
The performance of models for arousal detection and sleep stage classification using metrics such as accuracy and computational complexity.
| Author | Procedure type | Signal (s) | Accuracy (%) | Computational complexity |
| Supratak et al. [56] | Sleep stage scoring | Raw single-channel EEG | 85.50 | High | Willemen et al. [57] | Sleep stage classification | Cardiorespiratory and movement | 86.75 | Moderate | Fonseca et al. [58] | Sleep stage detection | Cardiorespiratory | 87.38 | High | Alickovic et al. [59] | Sleep stage classification | EEG | 91.10 | Moderate | Fernández-Varela et al. [60] | Arousals monitoring | Polysomnographic | 86.00 | Low | Li and Guan [25] | Arousals monitoring | Polysomnographic | 92.80 | High | Mousavi et al. [26] | Sleep stage detection | Single-channel EEG signals | 93.55 | High | Our proposed approach | Sleep stage recognition | EEG and ECG | 93.76 | Low | Arousals detection | EEG and ECG | 93.23 | Low |
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