Predicting Epilepsy with Machine Learning: Advancements and Applications

Epilepsy Prediction using Machine Learning

Problem Statement

Client was looking for a partner to

  • Develop the algorithm applied to sensing human brain signals through a single, bipolar electroencephalogram (EEG), for the purpose of predicting, detecting, and monitoring epileptic seizures and other neurological events


  • Understanding of research publication “Automatic Seizure Prediction and Monitoring Algorithms and Evaluation for a Single, Strategically-placed, Bipolar Electroencephalogram” to develop algorithm for detection.
  • Usage of supervised learning algorithm that uses a decision tree classifier approach
  • Developed Matlab software to train decision classifier using 1000+ hours of training data recorded from multiple patients.
  • Developed Matlab software for real-time acquisition from DAQ and detection, dynamic learning, offline review of data for doctors.


Achieved 99% of detection accuracy when trained for individual patients. Can perform the operation real-time consuming very low CPU load. Able to achieve detection and alert Leading US Medical University before 2-3 minutes of Seizure.


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