Abstract
The study focuses on automatic sleep spindle detection. Plenty of methods have been proposed in previous decades. However, there is still space for improvement. In this study, we investigate aggregation methods such as voting to achieve better results. We employ an unweighted model and two weighted voting models in which assigned weights represent reliability of automatic detectors. First weighted model utilizes supervised approach based on logistic regression. The second one applies unsupervised generative Bayesian model often used in crowdsourcing. Using the expectation maximization algorithm, we uncover hidden true labels and weighs of detectors. We test methods on the real world datasets. The aggregation method overcome single detectors on 10% on average in terms of F1. Moreover, a probabilistic explanation of weights could be used in applications for visual analysis.
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Acknowledgment
Research was supported by the project Temporal context in analysis of long-term non-stationary multidimensional signal, Register Number 17-20480S of the Grant Agency of the Czech Republic. This work was also supported by the Charles University research program PROGRES Q35, by the project No. LO1611 with financial support from the MEYS under the NPU I program and by the Ministry of Health of the Czech Republic, grant No. NV18-07-00272. All rights reserved.
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Saifutdinova, E., Dudysova, D., Gerla, V., Lhotska, L. (2020). Improvement of Sleep Spindle Detection by Aggregation Techniques. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_27
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DOI: https://doi.org/10.1007/978-3-030-31635-8_27
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