99% Accuracy in Predicting Epilepsy with Machine Learning


A leading Healthcare Tech Company based in the USA

Problem Statement
The client was looking for a partner to Develop the algorithm for sensing human brain signals through a single bipolar electroencephalogram (EEG) to predict, detect, and monitor epileptic seizures and other neurological events.
  • Used AI model on Android and iOS to detect seizure on the watch using technologies like NodeJS, HIPAA-compliant AWS cloud infrastructure
  • Used a 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, and offline review of data for doctors
  • It achieved 99% detection accuracy when trained for individual patients. It can operate in real-time, consuming a very low CPU load. Able to achieve detection and alert Leading US Medical University 2-3 minutes of seizure
  • Helped to alert the caregiver to provide help to the patient in time. Also, the data captured can be used for medicine tuning by doctors


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