Writing a README for your GitHub project is crucial for providing an overview of what your project is about, how to set it up, and how others can contribute or use it. Below is a template for a README file tailored to your ECG Arrhythmia Classification project:

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ECG Arrhythmia Classification

Overview

This project aims to classify ECG (Electrocardiogram) signals into two categories: "arrhythmia" and "normal" using machine learning models. The dataset used is the MIT-BIH Arrhythmia Database, which contains labeled ECG recordings.

Table of Contents

• Introduction
• Overview
• Dataset
• Models Used
• Setup
• Usage
• License

Dataset

The dataset used in this project is the MIT-BIH Arrhythmia Database, available on https://www.kaggle.com/datasets/sadmansakib7/ecg-arrhythmia-classification-dataset The dataset contains multiple classes of arrhythmias, which are consolidated into two classes for binary classification: "arrhythmia" and "normal".

Models Used

1. Random Forest Classifier: Trained on preprocessed ECG features to classify arrhythmias.
2. Convolutional Neural Network (CNN): Adapted for 1D signal data to learn hierarchical representations.
3. Support Vector Machine (SVM): Used for linear classification of the ECG data.

Setup

To run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/ecg-arrhythmia-classification.git
   cd ecg-arrhythmia-classification
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Download the MIT-BIH Arrhythmia Database CSV file from https://www.kaggle.com and place it in the project directory.

Usage

Training and Evaluation

Each model script contains code to preprocess the data, train the model, and evaluate its performance.

Hyperparameter Tuning

Modify hyperparameters such as learning rates, batch sizes, and model architectures to optimize performance.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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