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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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 46; H. 7; S. 5044 - 5061
Hauptverfasser: Niknazar, Hamid, Mednick, Sara C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2024.3366170