mlsanityData to 7 Oct 2026

A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture

arXiv 2207.04585 ↗First posted 11 Jul 2022cs.LGcited by 4

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Abstract (arXiv)

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 based on 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 the microstructure of EEG signals, such as trained kernels and the effect of each kernel on the detected stages, to macrostructures, such as the transition between stages. The proposed system demonstrated greater performance than prior studies and the results of interpretation showed that the system learned information which was consistent with expert knowledge.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 41 of its 133 references that are in the map. First posted on arXiv 11 Jul 2022.

Builds on

41 of 133 references.

References · 41

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025FIC-TSC: Learning Time Series Classification with Fisher Information Constraintcited by 2
  2. 2025Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networkscited by 0
  3. 2026SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Stagingcited by 0
  4. 2026Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasetscited by 0