Seizure Detection Using Machine Learning

Project Overview

This project focuses on detecting seizures from EEG (electroencephalogram) data using machine learning techniques. The primary goal is to create a reliable model that can classify segments of EEG data as either seizure or non-seizure events.

Table of Contents

• Installation
• Usage
• Data
• Modeling
• Results

Installation

To run this project, you'll need to install the necessary libraries. You can create a new Conda environment and install the dependencies using the following commands:

conda create --name seizure_detection python=3.x
conda activate seizure_detection
pip install -r requirements.txt

Usage

To use the seizure detection model, run the following command in your terminal:

bash

python main.ipynb

Data

Dataset code

Modeling

This project utilizes a CNN architecture defined as follows:

Input Layer: Accepts EEG signal data.
Convolutional Layers: Three Conv1D layers with increasing filter sizes to extract features from the data.
Pooling Layers: MaxPooling1D layers to reduce dimensionality.
Dense Layers: A fully connected layer followed by a dropout layer for regularization.
Output Layer: A dense layer with a softmax activation function to classify between seizure and non-seizure events.

Results

The model achieved an accuracy of 95% on the test set. The confusion matrix and classification report are as follows: