Advanced Epileptic Seizure Detection

The Challenge

Epilepsy diagnosis requires the precise identification of seizure types, particularly distinguishing between focal and non-focal seizures, which originate in different parts of the brain. Manual analysis of EEG signals is time-consuming, subjective, and requires specialized expertise. The goal was to automate this process with a highly accurate machine learning pipeline that could provide reliable classifications to aid medical professionals.

My Solution

I developed a comprehensive end-to-end pipeline that processes raw EEG data, extracts meaningful features, and employs a suite of advanced machine learning and deep learning models for classification. The system was engineered to handle the complexities of biomedical signals, including class imbalance and signal noise.

Key Features & Technical Implementation:

• Advanced Feature Engineering: Went beyond standard metrics by implementing time-frequency domain analysis. This involved using wavelet-based decomposition to capture transient, non-stationary features in the EEG signals, which are often critical indicators of seizure type but are missed by simpler methods.
• Robust Model Selection: Implemented and benchmarked a variety of powerful algorithms, including XGBoost for its gradient boosting prowess, Support Vector Machines (SVM) for their effectiveness in high-dimensional spaces, and a Long Short-Term Memory (LSTM) network to capture temporal dependencies in the EEG time-series data.
• Handling Class Imbalance: The dataset naturally contained an unequal number of focal and non-focal seizure samples. I addressed this critical issue by implementing the Synthetic Minority Over-sampling Technique (SMOTE), which generated synthetic data points for the minority class, leading to a more balanced dataset and preventing model bias.
• Data-Driven Validation: Rigorously evaluated model performance using metrics like accuracy, precision, recall, and F1-score to ensure the classifier was not only accurate but also reliable in identifying positive cases.

This project demonstrates a deep understanding of applying complex signal processing and machine learning techniques to solve a critical real-world problem in the medical domain.