Automated Optical Reading of Scanned ECGs

TL;DR

This paper introduces a novel image analysis method that automatically reads printed ECGs from paper and converts them into digital signals, facilitating automated diagnosis and analysis.

Contribution

The paper presents a new technique for converting printed ECG images into digital signals, bridging the gap between analog recordings and digital analysis tools.

Findings

Successfully converts printed ECGs into digital signals

Enhances compatibility with machine learning diagnostic tools

Improves automation in ECG analysis workflows

Abstract

Electrocardiogram (ECG) is a valuable tool for medical diagnosis used worldwide. Its use has contributed significantly to the prevention of cardiovascular diseases including infarctions. Although physicians need to see the printed curves for a diagnosis, nowadays there exist automated tools based on machine learning that can help diagnosis of arrhythmias and other pathologies, these tools operate on digitalized ECG data that are merely one-dimensional discrete signals (a kind of information that is much similar to digitized audio). Thus, it is interesting to have both the graphical information and the digitized data. This is possible with modern, digital equipment. Nevertheless, there still exist many analog electrocardiogram machines that plot results on paper with a printed gris measured in millimeters. This paper presents a novel image analysis method that is capable of reading a printed ECG and converting it into a sampled digital signal.