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Related Experiment Video

Updated: Sep 17, 2025

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Ensemble learning for biomedical signal classification: a high-accuracy framework using spectrograms from percussion

Abdul Karim1, Semin Ryu2, In Cheol Jeong3,4,5

  • 1Cerebrovascular Disease Research Center, Hallym University, Chuncheon, Gangwon, 24252, South Korea.

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|July 2, 2025
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Summary

This study introduces an ensemble learning framework using Random Forest, Support Vector Machines, and Convolutional Neural Networks for accurate biomedical signal classification. The novel approach achieved 95.4% accuracy in identifying anatomical regions from percussion and palpation signals.

Keywords:
Biomedical Signal ClassificationConvolutional Neural Networks (CNN)Ensemble learningMachine learning in healthcareRandom ForestSupport Vector Machines (SVM)

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Accurate classification of biomedical signals is vital for non-invasive diagnostics, especially for gastrointestinal conditions.
  • Conventional diagnostic techniques often have limitations in identifying subtle variations.

Purpose of the Study:

  • To develop an ensemble learning framework for accurate classification of biomedical signals.
  • To improve the identification of gastrointestinal and related medical conditions using non-invasive methods.

Main Methods:

  • An ensemble framework integrating Random Forest, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) was developed.
  • Spectrogram images were generated from percussion and palpation signals.
  • Short-Time Fourier Transform (STFT) was used for spectral and temporal information extraction.

Main Results:

  • The ensemble model achieved a classification accuracy of 95.4%.
  • The framework demonstrated superior performance compared to traditional classifiers in detecting subtle diagnostic variations.
  • The model successfully classified signals into distinct anatomical regions.

Conclusions:

  • The developed ensemble framework provides a robust solution for biomedical signal classification.
  • This method holds significant potential for enhancing clinical diagnostics.
  • Future work includes real-time clinical integration and multi-modal data incorporation.