Epilepsy Detection

EEG Signal Classification for Seizure Prediction

Project Overview

  • Developed a machine learning system to detect epileptic seizures by analyzing EEG signal data.
  • Extracted time-domain and frequency-domain features from EEG data and used classification models like SVM and Random Forest.
  • Focused on early detection with minimal false positives to support proactive healthcare monitoring.
  • Performed cross-validation and ROC analysis to fine-tune the model for clinical-grade performance.
  • Tech Stack: Python, Scikit-learn, Pandas, NumPy, Matplotlib

Project Visuals

EEG Signal Visualization

A visual representation of the EEG signal data.

Model Output Prediction

An example of the model's output predicting a seizure.