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Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere

Xiaoxuan Fu1,2, Youhua Wang1,2, Abdelkader Nasreddine Belkacem3

  • 1State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China.

Journal of Healthcare Engineering
|November 8, 2021
PubMed
Summary

An optimized multiscale entropy (MSE) model combined with machine learning accurately identifies the epileptogenic hemisphere using resting-state functional MRI (rfMRI). This approach achieves over 90% classification accuracy, improving epilepsy diagnosis.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Parameter validation for entropy models has hindered their application in resting-state functional magnetic resonance imaging (rfMRI).
  • Accurate localization of the epileptogenic hemisphere is crucial for epilepsy treatment planning.

Purpose of the Study:

  • To develop an optimization algorithm for the multiscale entropy (MSE) model parameters.
  • To validate the optimized MSE model's effectiveness in localizing the epileptogenic hemisphere using rfMRI and machine learning.

Main Methods:

  • Developed an optimization algorithm for MSE model parameters using receiver operating characteristic (ROC) curve analysis.
  • Collected and preprocessed rfMRI data from 20 mesial temporal lobe epilepsy patients.
  • Utilized a support vector machine (SVM) with optimized entropy values as feature vectors for classification.

Main Results:

  • Identified nine biomarked brain areas sensitive to the epileptogenic hemisphere, including medial superior frontal gyrus and superior parietal gyrus (p < 0.01).
  • Achieved a mean classification accuracy greater than 90% in identifying the epileptogenic hemisphere.
  • Demonstrated the feasibility of integrating the epilepsy side-fixing algorithm into a cloud platform via 5G communication for preoperative assessment.

Conclusions:

  • The combination of the optimized MSE model and machine learning accurately confirms the epileptogenic hemisphere using rfMRI.
  • This integrated approach offers a powerful tool for preoperative epilepsy assessment, potentially enhancing surgical outcomes.
  • Cloud-based implementation via 5G communication facilitates accessible and efficient preoperative epilepsy evaluation.