Challenge: ECG Paper Record to Digital Signal Conversion

Background:

In many healthcare settings, ECG results are still recorded and stored in paper format. The conversion of these paper records into digital signals is a crucial step in making the data accessible for further digital processing and analysis. This challenge focuses on developing an automated system to convert ECG paper records into digital ECG signals.

Task:

Develop an image processing algorithm using OpenCV (or other suitable libraries) that can:

Image Preprocessing:

Clean and enhance the quality of the image containing ECG paper records. Handle variations in lighting, angle, and quality of the paper record image.

ECG Trace Extraction:

Identify and extract ECG traces from the enhanced image. Handle different layouts and formats of ECG paper records.

Digitization:

Convert extracted ECG traces into digital ECG signals. Ensure the accuracy and integrity of the digital signal in representing the original paper record.

Evaluation:

Provide a detailed analysis of the algorithm's performance, including any limitations and potential improvements.

Data:

You are provided with a dataset containing images of ECG paper records from various sources, angles, and quality. All the data is storage into the data folder.

Deliverables:

A well-documented Jupyter Notebook containing all code and visualizations. A report detailing the methodology, results, and recommendations for future work.

Evaluation Criteria:

• Quality of Image Preprocessing: How well the algorithm enhances and prepares ECG paper record images for extraction.
• ECG Trace Extraction: The accuracy and completeness of ECG traces extracted from the images.
• Digitization Accuracy: How accurately the extracted ECG traces are converted into digital signals.
• Analysis and Reporting: The depth of analysis and the quality of the final report.

Bonus:

Implement a user-friendly interface for users to upload images of ECG paper records and receive digitized ECG signals. Explore machine learning or deep learning techniques to improve the accuracy of ECG trace extraction and digitization. This challenge will help to assess the ability of data scientists to handle real-world problems involving image processing, signal conversion, and data integrity assurance. The primary objective is to convert ECG paper records into accurate and usable digital ECG signals.