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  3. Emg-spectrogram-empowered Cnn Stroke-classifier Model Development.
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  3. Emg-spectrogram-empowered Cnn Stroke-classifier Model Development.

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EMG-Spectrogram-Empowered CNN Stroke-Classifier Model Development.

Katherine1, Riries Rulaningtyas1, Kalaivani Chellappan2

  • 1Biomedical Engineering, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Surabaya 60115, Indonesia.

Life (Basel, Switzerland)
|January 28, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel deep learning method using electromyography (EMG) spectrograms to accurately classify stroke patients. This approach enhances objective stroke assessment and automates rehabilitation monitoring for home-based rehabilitation (HBR).

Keywords:
CNNEMGdeep learningspectrogramstroke classification

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Area of Science:

  • Biomedical Engineering
  • Neurorehabilitation
  • Machine Learning in Healthcare

Background:

  • Stroke is a primary cause of long-term disability, leading to motor dysfunction and reduced productivity.
  • Limited access to rehabilitation services, especially in low- and middle-income countries, hinders post-stroke recovery.
  • Current home-based rehabilitation (HBR) relies on subjective assessments, highlighting the need for objective evaluation methods like electromyography (EMG).

Purpose of the Study:

  • To develop and validate a novel deep learning (DL) methodology for objective stroke assessment using EMG signals.
  • To automate the classification of stroke patients versus healthy individuals based on EMG data.
  • To explore the potential of this method for enhancing stroke rehabilitation procedures and monitoring in HBR settings.

Main Methods:

  • EMG signals were transformed into time-frequency representation (TFR) spectrograms.
  • A novel convolutional neural network (CNN) model, Tri-CCNN, was developed using these spectrograms as input.
  • The Tri-CCNN model's performance was compared against Shallow CNN and LeNet-5 architectures.

Main Results:

  • The proposed Tri-CCNN model achieved a classification accuracy of 93.33%, outperforming existing models.
  • Analysis of spectrogram amplitude distributions revealed distinct patterns differentiating stroke patients from healthy individuals.
  • The findings indicate the method's potential for objective stroke assessment and classification.

Conclusions:

  • The developed DL approach using EMG spectrograms offers an effective tool for objective stroke classification.
  • This methodology shows significant promise for automating rehabilitation monitoring in home-based rehabilitation (HBR) settings.
  • The study paves the way for improved stroke rehabilitation strategies and accessibility.