Download PDFOpen PDF in browserComparison of ensemble-combination approaches in an automatic sleep staging inter-database generalization task4 pages•Published: February 16, 2023AbstractDeep learning has demostrated its usefulness in reaching top-level performance. How- ever, inter-database generalization is still a broad of concern due to the aroused differences between local and external datasets’ performances. In this work we explore different deep learning model’s combination strategies applied to a multi-database case of study in the domain of sleep medicine. More specifically, three ensemble combination methods (namely max-voting, output averaging and weighted combination using the Nelder-Mead search) are analyzed in comparison to baseline methods (local models, database assembly approach) in a sleep staging inter-database generalization task.Keyphrases: cnn, deep learning, domain adaptation, ensemble models, inter database generalization, lstm, sleep medicine In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 170-173.
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