A Conflict-aware Evidential Framework for Reliable Sleep Stage Classification

TL;DR

This paper introduces ConfSleepNet, a conflict-aware evidential framework for sleep stage classification that dynamically resolves inter-view conflicts in multi-modal data, improving reliability.

Contribution

The novel hybrid category structures and conflict-aware aggregation method enhance evidence learning and decision reliability in multi-view sleep staging.

Findings

ConfSleepNet outperforms existing methods in sleep staging accuracy.

The conflict-aware aggregation effectively resolves modality conflicts.

Theoretical analysis confirms the robustness of the framework.

Abstract

Multi-view learning has been widely applied for sleep stage classification using multi-modal data. However, existing methods typically assume that different modalities are well-aligned, which is often unattainable in real-world scenarios, thereby compromising the reliability of the staging results. In this paper, we propose ConfSleepNet, a conflict-aware evidential framework that dynamically resolves inter-view conflicts. The framework consists of multi-view evidence extraction and conflict-aware aggregation. In the first phase, it learns category-related evidence from different modalities, which represents the degree of support for individual sleep stages. Considering the inherent characteristics of varying modalities, we propose hybrid category structures for different modalities to promote more reasonable evidence learning. In the second phase, view-specific opinions, including prediction results and uncertainty, are constructed from the learned evidence. Notably, we propose a novel conflict-aware aggregation method that integrates these view-specific opinions into a reliable joint decision. This mechanism can effectively resolve conflicts among opinions and synthesize them into a reliable joint decision. Both theoretical analysis and experimental results demonstrate the effectiveness of ConfSleepNet in sleep staging tasks. The code is available at https://github.com/By4te/ConfSleepNet_ICML2026/.