CASE STUDIES

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

OUR SOLUTION

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.

RESULTS

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.