Analysis of Transformer’s Attention Behavior in Sleep Stage Classification and Limiting It to Improve Performance

Author:

Kim Dongyoung1ORCID,Ko Young-Woong1,Kim Dong-Kyu2ORCID,Lee Jeong-Gun1ORCID

Affiliation:

1. Department of Computer Engineering, Hallym University, Chuncheon, South Korea

2. Department of Otorhinolaryngology-Head and Neck Surgery, Chuncheon Sacred Heart Hospital, Chuncheon, South Korea

Funder

Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by Korean Government [Ministry of Science and Information-Communication-Technology (MSIT)]

NRF through the Basic Science Research Program

University of California, Davis

New York University

University of Minnesota

Johns Hopkins University

University of Arizona

University of Washington

Boston University

Case Western Reserve University

National Heart, Lung, and Blood Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Reference24 articles.

1. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin;arXiv:1810.04805,2018

2. An image is worth 16×16 words: Transformers for image recognition at scale;Dosovitskiy;arXiv:2010.11929,2020

3. Sleep stage classification using single-channel EOG

4. Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks

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