A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts’ Knowledge Into a Deep Network Architecture | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges rem...Show More

Abstract:

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.
Page(s): 5044 - 5061
Date of Publication: 15 February 2024

ISSN Information:

PubMed ID: 38358869

Funding Agency:


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