CNN ECG Heart Disease Classification

Overview

This repository contains an implementation of Convolutional Neural Networks (CNNs) for the classification of heart diseases using Electrocardiogram (ECG) signals. The primary goal is to classify ECG recordings into categories such as 'Normal' and 'Left Bundle Branch Block' (LBBB) to aid in accurate diagnosis and treatment planning.

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage
5. Contributing
6. License

Introduction

Electrocardiogram (ECG) signals are essential in diagnosing various heart conditions. This project focuses on leveraging deep learning techniques, particularly CNNs, to automate the classification process. By training the model on labeled ECG data, we aim to accurately classify heart conditions, including 'Normal' and 'Left Bundle Branch Block' (LBBB), among others.

Features

• Preprocessing pipeline for cleaning and preparing ECG data.
• Implementation of CNN architectures optimized for ECG classification.
• Training pipeline with hyperparameter tuning and model evaluation.
• Visualization tools for interpreting results and analyzing model performance.
• Detailed documentation and tutorials for seamless usage.

Installation

To get started, clone the repository to your local machine:

git clone https://github.com/yourusername/cnn-ecg-heart-disease-classification.git](https://github.com/JustCallMeSidd/CNN-ECG-Heart-Disease-Classification/tree/main)https://github.com/JustCallMeSidd/CNN-ECG-Heart-Disease-Classification/tree/main