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Electrocardiogram

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...

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Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports.

Mahmoud M Bassiouni¹, Islam Hegazy², Nouhad Rizk³

¹Egyptian E-Learning University (EELU), 33 El-messah Street, Eldokki, El-Giza, 11261 Egypt.

Circuits, Systems, and Signal Processing

|May 26, 2022

Summary

This summary is machine-generated.

This study developed ECGConvnet, a deep learning model using convolutional neural networks (CNNs), for accurate COVID-19 diagnosis from ECG images. The model achieved over 99% accuracy, showing potential for an automated diagnostic system.

Keywords:

COVID-19 Diagnosis Convolutional neural network (CNN)Deep learning ECGConvnet Paper-based ECG image reports

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Published on: December 11, 2019

Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Cardiology

Background:

The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Existing COVID-19 diagnostic methods have limitations, driving the need for novel approaches.
Electrocardiogram (ECG) signals offer a potential, non-invasive biomarker for COVID-19 detection.

Purpose of the Study:

To develop and evaluate a deep learning-based system for the early diagnosis of COVID-19 using ECG images.
To enhance diagnostic performance by employing advanced feature extraction and classification techniques.
To investigate the efficacy of a novel ensemble model, ECGConvnet, for COVID-19 detection.

Main Methods:

Utilized publicly available ECG image datasets, including those with COVID-19 reports.
Applied image preprocessing and data augmentation techniques to enhance data quality and balance classes.
Employed deep learning, specifically Convolutional Neural Networks (CNNs), for feature extraction using pre-trained models (Vgg16, Vgg19, ResNet-101, Xception).
Proposed an ensemble model, ECGConvnet (Xception + TCN), and evaluated its performance with classifiers like SVM, RF, MLP, and Softmax via fivefold cross-validation.

Main Results:

The proposed ECGConvnet model demonstrated superior performance compared to individual pre-trained models.
The Support Vector Machine (SVM) classifier achieved the highest accuracy when combined with ECGConvnet.
Achieved high accuracies, including 99.74%, 98.6%, and 99.1% for multi-class diagnosis, and up to 100% for binary-class diagnosis of COVID-19.

Conclusions:

ECGConvnet, an ensemble deep learning model, shows significant potential for accurate COVID-19 diagnosis.
ECG data, analyzed via deep learning, can form the basis of an effective automated COVID-19 diagnostic system.
The study highlights the clinical utility of ECG in the pandemic response, offering a promising non-invasive diagnostic avenue.