Auto Lead Extraction and Digitization of ECG Paper Records using cGAN

TL;DR

This paper presents a deep learning approach to automatically extract, digitize, and convert paper ECG records into digital format using YOLOv3 and pix-2-pix models, achieving 97.4% accuracy.

Contribution

It introduces a novel method combining YOLOv3 and pix-2-pix for accurate extraction and digitization of ECG leads from paper records, facilitating easy storage and analysis.

Findings

Achieved 97.4% accuracy in lead extraction and digitization.

Enabled conversion of paper ECGs into digital signals for storage and analysis.

Facilitated improved accessibility and research potential for ECG data.

Abstract

Purpose: An Electrocardiogram (ECG) is the simplest and fastest bio-medical test that is used to detect any heart-related disease. ECG signals are generally stored in paper form, which makes it difficult to store and analyze the data. While capturing ECG leads from paper ECG records, a lot of background information is also captured, which results in incorrect data interpretation. Methods: We propose a deep learning-based model for individually extracting all 12 leads from 12-lead ECG images captured using a camera. To simplify the analysis of the ECG and the calculation of complex parameters, we also propose a method to convert the paper ECG format into a storable digital format. The You Only Look Once, Version 3 (YOLOv3) algorithm has been used to extract the leads present in the image. These leads are then passed on to another deep learning model which separates the ECG signal and background from the single-lead image. After that, vertical scanning is performed on the ECG signal to convert it into a 1-Dimensional (1D) digital form. To perform the task of digitalization, we used the pix-2-pix deep learning model and binarized the ECG signals. Results: Our proposed method was able to achieve an accuracy of 97.4 %. Conclusion: The information on the paper ECG fades away over time. Hence, the digitized ECG signals make it possible to store the records and access them anytime. This proves highly beneficial for heart patients who require frequent ECG reports. The stored data can also be useful for research purposes, as this data can be used to develop computer algorithms that are capable of analyzing the data.