This GitHub repo contains a deep learning project focused on detecting arrhythmias in electrocardiogram (ECG) data and how to extract ECG signals from human.
This GitHub repository contains a machine learning project focused on ECG (Electrocardiogram) arrhythmia detection. The project provides a comprehensive implementation of an algorithm for detecting arrhythmias (abnormal heart rhythms) using electrocardiogram (ECG) data. This project includes pre-processing steps, deep learning model architectures (such as CNN), and evaluation metrics to accurately classify ECG signals into different arrhythmia categories. The repository offers a complete package for researchers, developers, and healthcare professionals interested in ECG analysis and arrhythmia detection, facilitating the development of reliable and efficient tools for identifying irregular heartbeats from ECG recordings.
Kaggle - ECG Heartbeat Categorization Dataset | Link
This dataset consist of 2 types of datasets. i.e. MIT-BIH Arrhythmia Dataset and PTB Diagnostic ECG dataset. In this, I considered arrhythmia dataset for building my project.
Dataset info:
Number of Samples: 109446
Number of Categories: 5
Sampling Frequency: 125Hz
Classes: ['N': 0, 'S': 1, 'V': 2, 'F': 3, 'Q': 4]
-N : Non-ecotic beats (normal beat)
-S : Supraventricular ectopic beats
-V : Ventricular ectopic beats
-F : Fusion Beats
-Q : Unknown Beats
Feature 1 : Extracting ECG signals from humans using ECG AD8252 sensor along with arduino UNO. This can also be implemented using ESP32 module.
This is how the connectivity looks like:
I have included a live video demo under ECG extraction folder. Link
Feature 2: Trained a model with deep convolutional neural network architecture in tensorflow framework for detecting the type of arrhythmic condition.
Use Python 3.8.5. Setup conda environment, git clone repo and run the below commands,
pip install -r requirements.txt
For extracting ECG signals, you need hardware components. i.e, Arduino UNO/ESP32/ESP8266 , ECG sensor(AD8232), connecting wires, ECG electrodes and software tools like Arduino IDE and Pycharm.
Use the ECG extraction folder and circuit diagram for implementing this segment and set the baud rate to 9600 in arduino ide for viewing the ECG signal.
For training the model, edit the path directory of dataset and path where the model has to be placed and run the notebook. You can twirk hyperparameter values for seeing the performance of the model.
- Medium
- Kaggle
- Blog on ECG signal extraction
- Paper publication on Arrhythmia detection by Acharya