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LMA-EEGNet: A Lightweight Multi-Attention Network for Neonatal Seizure Detection Using EEG signals

Electronics (Switzerland), ISSN: 2079-9292, Vol: 13, Issue: 12
2024
  • 3
    Citations
  • 0
    Usage
  • 19
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    3
  • Captures
    19
  • Mentions
    2
    • Blog Mentions
      1
      • 1
    • News Mentions
      1
      • 1

Most Recent Blog

Electronics, Vol. 13, Pages 2354: LMA-EEGNet: A Lightweight Multi-Attention Network for Neonatal Seizure Detection Using EEG signals

Electronics, Vol. 13, Pages 2354: LMA-EEGNet: A Lightweight Multi-Attention Network for Neonatal Seizure Detection Using EEG signals Electronics doi: 10.3390/electronics13122354 Authors: Weicheng Zhou Wei Zheng

Most Recent News

Reports Summarize Epilepsy Study Results from Jiangsu University (Lma-eegnet: a Lightweight Multi-attention Network for Neonatal Seizure Detection Using Eeg Signals)

2024 JUL 18 (NewsRx) -- By a News Reporter-Staff News Editor at Pain & Central Nervous System Daily News -- New research on Central Nervous

Article Description

Neonatal epilepsy is an early postnatal brain disorder, and automatic seizure detection is crucial for timely diagnosis and treatment to reduce potential brain damage. This work proposes a novel Lightweight Multi-Attention Network, LMA-EEGNet, for diagnosing neonatal epileptic seizures from multi-channel EEG signals employing dilated depthwise separable convolution (DDS Conv) for feature extraction and using pointwise convolution followed by global average pooling for classification. The proposed approach substantially reduces the model size, number of parameters, and computational complexity, which are crucial for real-time detection and clinical diagnosis of neonatal epileptic seizures. LMA-EEGNet integrates temporal and spectral features through distinct temporal and spectral branches. The temporal branch uses DDS Conv to extract temporal features, enhanced by a channel attention mechanism. The spectral branch utilizes similar convolutions alongside a spatial attention mechanism to highlight key frequency components. Outputs from both branches are merged and processed through a pointwise convolution layer and a global average pooling layer for efficient neonatal seizure detection. Experimental results show that our model, with only 2471 parameters and a size of 23 KB, achieves an accuracy of 95.71% and an AUC of 0.9862, demonstrating its potential for practical deployment. This study provides an effective deep learning solution for the early detection of neonatal epileptic seizures, improving diagnostic accuracy and timeliness.

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