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An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning

R Pitchai1, Bhasker Dappuri2, P V Pramila3

  • 1Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur 502313, Telangana, India.

Computational Intelligence and Neuroscience
|October 24, 2022
PubMed
Summary

This study introduces a machine learning algorithm using electromyography (EMG) data for stroke prediction. The developed system offers a more accessible and accurate method for early stroke detection compared to traditional imaging techniques.

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Neurology

Background:

  • Stroke significantly impacts economic well-being and can be fatal if untreated.
  • Abnormal biosignals are common in stroke survivors, indicating potential for monitoring.
  • Current stroke diagnosis relies on expensive and complex imaging like CT or MRI.

Purpose of the Study:

  • To develop a machine learning algorithm for brain stroke prediction using real-time electromyography (EMG) data.
  • To offer a cost-effective and user-friendly alternative to traditional stroke diagnostic methods.
  • To enhance the accuracy and efficiency of stroke detection through advanced data analysis.

Main Methods:

  • A support vector machine (SVM) classifier was trained using synthetic EMG data.
  • Data augmentation techniques were employed to generate extensive training datasets for improved accuracy.
  • The model was tested using real-time EMG samples to evaluate its predictive performance.

Main Results:

  • The proposed machine learning model demonstrated higher classification accuracy than existing methods.
  • The SVM classifier achieved superior rates of precision, recall, and F-measure.
  • Simulations confirmed the efficacy of the developed algorithm in predicting stroke from EMG data.

Conclusions:

  • Machine learning analysis of real-time EMG signals presents a viable approach for stroke prediction.
  • The developed SVM-based system offers improved accuracy and efficiency over conventional diagnostic tools.
  • This method facilitates prompt patient therapy through precise, real-time biosignal assessment.