HeartBeat Classification using ECG

[sreevidya-16]

• [X] I have searched the existing issues

Feature Description

HeartBeat Classification using ECG

This project focuses on developing a system for classifying heartbeats using Electrocardiogram (ECG) data. ECG is a widely used diagnostic tool that records the electrical activity of the heart over a period of time. Accurate classification of heartbeats is crucial for diagnosing various cardiac conditions, such as arrhythmias, myocardial infarctions, and other heart diseases.

Key Aspects:

1. Objective: The primary goal is to accurately classify different types of heartbeats, aiding in the early detection and diagnosis of cardiac conditions.

2. Data Source: ECG data, which captures the electrical signals produced by the heart, serves as the primary input for the classification model.

3. Techniques Used: Advanced machine learning and deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are employed to analyze and classify the ECG signals.

4. Challenges: The project addresses challenges such as the variability in heart signals among different individuals, noise in the ECG recordings, and the need for large annotated datasets for training the models.

5. Significance: By improving the accuracy of heartbeat classification, the system can assist healthcare professionals in making more informed decisions, potentially leading to better patient outcomes and more efficient treatment plans.

Use Case

Problem Statement:

Healthcare professionals often face challenges in accurately diagnosing cardiac conditions based on Electrocardiogram (ECG) data. The variability in heart signals and the need for timely and precise classification of heartbeats are critical for effective treatment planning.

Solution:

Objectives:

• Develop a machine learning system to classify different types of heartbeats in ECG recordings.
• Enable early detection and diagnosis of cardiac abnormalities using advanced classification algorithms.

Implementation Details:

1. Data Collection: Gather a diverse dataset of ECG recordings from various patient demographics and conditions.

2. Preprocessing: Clean and preprocess ECG data to remove noise and normalize signals for consistent analysis.

3. Model Development: Employ Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features and classify heartbeats based on learned patterns.

4. Training and Validation: Train the model on annotated ECG data, validating its performance against a hold-out set to ensure robust classification accuracy.

5. Deployment: Integrate the trained model into healthcare systems or mobile applications for real-time analysis of ECG data.

By implementing this HeartBeat Classification system using ECG, healthcare providers can leverage advanced machine learning techniques to enhance cardiac care and patient management.

Benefits

• Enhanced Diagnosis: Assist healthcare providers in making accurate and timely diagnoses of cardiac conditions, such as arrhythmias and myocardial infarctions.
• Improved Patient Care: Enable personalized treatment plans based on precise classification of heartbeats, potentially improving patient outcomes and reducing healthcare costs.
• Research Advancement: Contribute to ongoing research in cardiac health by providing a scalable and reliable tool for heart rhythm analysis.

Priority

High

Record

• [X] I have read the Contributing Guidelines
• [X] I'm a GSSOC'24 contributor
• [X] I want to work on this issue

@Niketkumardheeryan, could you please assign me this issue under GSSOC'24

[sreevidya-16]

@Niketkumardheeryan, could you please assign me this issue under GSSOC'24

[sreevidya-16]

@invigorzz313 assign me this issue I want to work on it