A sleep staging system and method based on a dual-stream parallel neural network

By employing a dual-stream parallel neural network architecture and feature fusion technology, the problem of difficult N1 stage identification in single-channel EEG is solved, achieving efficient and accurate sleep staging, which is suitable for health monitoring and medical auxiliary diagnostic systems.

CN122251027APending Publication Date: 2026-06-23SHENZHEN KUKAI BRAIN MACHINE INTELLIGENT TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN KUKAI BRAIN MACHINE INTELLIGENT TECHNOLOGY CO LTD
Filing Date
2026-03-14
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing automatic sleep staging technologies based on single-channel EEG have difficulty accurately identifying N1 stage due to data imbalance, low signal-to-noise ratio, and the lack of independent verification of instantaneous features in the model architecture, resulting in low recall rate for N1 stage.

Method used

A dual-stream parallel neural network architecture is adopted, including a shared feature extraction module, a context processing stream, and a local retention stream. Information is integrated through a feature fusion module, and the model training is optimized by combining a weighted cross-entropy loss function to improve the N1-period recognition capability.

Benefits of technology

It significantly improves the recognition accuracy of N1 stage while maintaining the recognition accuracy of other sleep stages, thereby improving the overall sleep staging performance. It also has high computational efficiency and is suitable for practical applications.

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Abstract

The present application belongs to the cross field of biomedical signal processing and artificial intelligence, and particularly relates to a sleep staging system and method based on a double-flow parallel neural network. The system comprises a brain electrical device, a data acquisition and preprocessing module, a HypnoMamba-Dual neural network model and a downstream application module connected in sequence; the brain electrical device is a single-channel electroencephalogram signal acquisition device for acquiring original EEG signals; the data acquisition and preprocessing module is used for filtering and windowing the original EEG signals for standardized operation; the HypnoMamba-Dual neural network model is used for analyzing and calculating the preprocessed EEG signal sequence and outputting a sleep staging sequence. The double-flow architecture of the present application, especially the introduction of the "local reserved flow", provides a mechanism for the model to resist the "context smoothing effect". When the context flow tends to ignore the short N1 period, the strong instantaneous features provided by the local reserved flow can effectively correct the decision, thereby significantly improving the F1 score of the N1 period.
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