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Related Experiment Video

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A Novel Morlet Convolutional Neural Network.

Peilin Zhu1,2, Zirong Li1,2, Chao Cao1,2

  • 1School of Integrated Circuits, Shandong University, Jinan 250100, P. R. China.

International Journal of Neural Systems
|November 19, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a lightweight Morlet convolutional neural network (Morlet-CNN) for automatic seizure detection. The novel framework significantly reduces model size and enhances interpretability, making it ideal for edge devices.

Keywords:
Morlet convolutional kernelMorlet convolutional neural network (Morlet-CNN)deep learning interpretabilityelectroencephalogram (EEG)prune and quantification

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Area of Science:

  • Medical Technology
  • Artificial Intelligence
  • Signal Processing

Background:

  • Automatic seizure detection is crucial for epilepsy diagnosis and treatment.
  • Traditional Convolutional Neural Networks (CNNs) show promise but have limitations like large parameter counts and poor interpretability, hindering edge deployment.
  • Existing CNN models struggle with reliability and practical application on resource-constrained devices.

Purpose of the Study:

  • To introduce an innovative Morlet convolutional neural network (Morlet-CNN) for effective seizure detection.
  • To develop a lightweight and interpretable CNN framework suitable for edge computing.
  • To significantly reduce model size and computational requirements while maintaining high accuracy.

Main Methods:

  • Developed a Morlet-CNN framework with convolutional kernels having only two learnable parameters for a lightweight architecture.
  • Proposed a frequency-domain-response-based kernel pruning algorithm tailored for Morlet-CNN.
  • Implemented an INT8 quantization algorithm using Kullback-Leibler (KL) divergence calibration with a Morlet lookup table (LUT).

    • Main Results:

    • Achieved over 90% reduction in model parameter scale through pruning and quantization algorithms with minimal accuracy loss.
    • Demonstrated enhanced model interpretability from a signal processing perspective.
    • Validated the Morlet-CNN model's efficacy on the Bonn and CHB-MIT datasets, achieving a compact Kilobyte (KB)-level model size.

    Conclusions:

    • The Morlet-CNN framework offers a highly effective and efficient solution for automatic seizure detection.
    • The lightweight and interpretable nature of Morlet-CNN makes it suitable for real-world applications and deployment on edge devices.
    • This approach addresses the limitations of traditional CNNs in terms of size and interpretability for epilepsy management.