Table 5.
Performance of Personalized and Generalized Sleep-Wake States Predictors Averaged Over All Participants Across All Studies Combined. In Each Column, tThe Boldface Number(s) Indicate(s) the Highest Value Obtained for the Corresponding Metric
| Classifier | Personalized Approach | Generalized Approach | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| ACC | SN | SP | MCC | AUC | ACC | SN | SP | MCC | AUC | |
| NB | 0.75 | 0.74 | 0.74 | 0.38 | 0.83 | 0.75 | 0.69 | 0.77 | 0.36 | 0.83 |
| RLR | 0.86 | 0.29 | 0.98 | 0.36 | 0.82 | 0.86 | 0.26 | 0.99 | 0.35 | 0.83 |
| RF | 0.85 | 0.45 | 0.93 | 0.40 | 0.81 | 0.86 | 0.41 | 0.96 | 0.41 | 0.80 |
| AB | 0.86 | 0.46 | 0.95 | 0.44 | 0.80 | 0.87 | 0.37 | 0.98 | 0.45 | 0.85 |
| XGB | 0.86 | 0.45 | 0.95 | 0.45 | 0.84 | 0.87 | 0.38 | 0.98 | 0.45 | 0.85 |
Notes: © 2018 IEEE. Reprinted, with permission, from Khademi A, El-Manzalawy Y, Buxton OM, Honavar V. Toward personalized sleep-wake prediction from actigraphy. In 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018 (Vol. 2018-March, pp. 414-417). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/BHI.2018.8333456.45
Abbreviations: NB, Naive Bayes; RLR, regularized logistic regression; RF, random forest; AB, adaptive boosting; XGB, extreme gradient boosting; ACC, accuracy; SN, sensitivity; SP, specificity; MCC, Matthews correlation coefficient; AUC, area under curve.