@inproceedings{de67f4c2d3e54466a7eb7ddf930cfae6,
title = "Enhancing Sleep Staging's interpretability via Frequency-Dropout",
abstract = "Automated sleep staging is crucial for efficient sleep disorder diagnosis, but existing deep learning models lack interpretability and struggle to leverage domain-specific knowledge. We propose Frequency-Dropout, a novel regularization technique that enhances model interpretability and performance by controlling useful information flow in the frequency domain. Frequency-Dropout integrates sleep priors into deep networks by applying Discrete Cosine Transform (DCT) to feature maps, followed by targeted dropout of high-frequency noise components. Evaluated on the SleepEDF-20 dataset, Frequency-Dropout boosted single branch CNN network's accuracy by 2.01\% (to 81.42\%).",
keywords = "Dropout, Frequency domain analysis, Regularization, Sleep staging",
author = "Diehan Song and Ruoyun Ji and Yuyang You",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 China Automation Congress, CAC 2025 ; Conference date: 26-09-2025 Through 28-09-2025",
year = "2025",
doi = "10.1109/CAC67268.2025.11487557",
language = "English",
series = "Proceedings - 2025 China Automation Congress, CAC 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4108--4113",
booktitle = "Proceedings - 2025 China Automation Congress, CAC 2025",
address = "United States",
}