Abstract
This paper examines the benefits and shortfalls of using local binary “one-vs.-all” classifiers with a variety of models in different hierarchical layouts in order to classify sleep stages using raw signal inputs in 30-s increments. Although under-performing more advanced algorithms, our best hierarchies outperform a Random Forest multi-class classifier, indicating the potential of binary classifiers in future work. This paper lays the groundwork for developing more advanced hierarchical classifiers using pre-trained binary classifiers.
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Oh, E., Barkdoll, K. (2022). Hierarchical Binary Classifiers for Sleep Stage Classification. In: Duffy, V.G., Gao, Q., Zhou, J., Antona, M., Stephanidis, C. (eds) HCI International 2022 – Late Breaking Papers: HCI for Health, Well-being, Universal Access and Healthy Aging. HCII 2022. Lecture Notes in Computer Science, vol 13521. Springer, Cham. https://doi.org/10.1007/978-3-031-17902-0_9
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