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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Deep learning for digitizing highly noisy paper-based ECG records.

Yao Li¹, Qixun Qu², Meng Wang²

¹Medicine School of Chinese PLA, Beijing, 100853, China; Core Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, 100853, China; Beijing Key Laboratory of Chronic Heart Failure Precision Medicine, Chinese PLA General Hospital, Beijing, 100853, China.

Computers in Biology and Medicine

|November 10, 2020

Summary

This summary is machine-generated.

This study introduces a deep learning method to digitize noisy, paper-based electrocardiography (ECG) records. The approach effectively extracts ECG signals from scans, improving diagnostic accuracy for heart conditions.

Keywords:

Deep learning Digitization Electrocardiogram Image segmentation Signal processing

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Area of Science:

Biomedical Engineering
Medical Imaging
Artificial Intelligence

Background:

Electrocardiography (ECG) is crucial for diagnosing heart diseases.
Paper-based ECG records often suffer from significant noise, hindering accurate analysis.
Digitizing these noisy records into high-quality signals is essential for reliable interpretation.

Purpose of the Study:

To develop a deep learning method for digitizing highly noisy, paper-based ECG scans.
To formulate ECG digitization as a segmentation problem for improved signal extraction.
To create an end-to-end approach capable of handling diverse paper record layouts.

Main Methods:

A deep learning model was proposed, treating ECG digitization as a segmentation task.
The method was designed for end-to-end signal extraction from noisy scans.
The approach was evaluated on its ability to handle various paper record formats.

Main Results:

The model achieved a Dice coefficient of 0.85 in extracting ECG waveforms.
Accurate measurement of common ECG parameters was demonstrated with Pearson's correlation > 0.90.
The deep learning method proved effective in digitizing noisy ECG scans.

Conclusions:

Deep learning offers a powerful solution for end-to-end ECG digitization from noisy paper records.
This approach represents a novel method for digitizing challenging, low-information binary ECG scans.
The technique shows potential for generalization to a wide range of ECG record types.