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MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice

Biosensors, ISSN: 2079-6374, Vol: 14, Issue: 8
2024
  • 0
    Citations
  • 0
    Usage
  • 9
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Captures
    9
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • News
        1

Most Recent Blog

Biosensors, Vol. 14, Pages 406: MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice

Biosensors, Vol. 14, Pages 406: MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice Biosensors doi: 10.3390/bios14080406 Authors: Chengyong Jiang Wenbin Xie

Article Description

Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial–temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial–temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset.

Bibliographic Details

Jiang, Chengyong; Xie, Wenbin; Zheng, Jiadong; Yan, Biao; Luo, Junwen; Zhang, Jiayi

MDPI AG

Chemistry; Biochemistry, Genetics and Molecular Biology; Engineering; Physics and Astronomy

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