A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is partic...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 46; no. 7; pp. 5044 - 5061
Main Authors:	Niknazar, Hamid, Mednick, Sara C.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.07.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adult Algorithms Artificial neural networks Black-box problem Brain modeling Computer vision convolutional neural network Deep Learning electroencephalogram Electroencephalography Feature extraction Female Humans interpretable system Kernel Machine learning Male Neural Networks, Computer Polysomnography Signal processing Signal Processing, Computer-Assisted Sleep sleep stages Sleep Stages - physiology Time-frequency analysis Young Adult
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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