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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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Related Experiment Video

Updated: May 30, 2025

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.

Vaibhav Gadag¹, Simrat Singh¹, Anshul Harish Khatri¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.

|January 30, 2025

Summary

This study presents a 98% accurate Siamese Network model for early detection of myocardial infarction (MI) using electrocardiography (ECG) images. The model effectively diagnoses MI, abnormal heartbeats, and normal rhythms, improving cardiovascular disease detection.

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Area of Science:

Cardiology
Medical Imaging
Machine Learning

Background:

Myocardial infarction (MI) causes heart muscle damage due to poor blood flow and is a leading global cause of death.
Diagnosing MI is challenging due to its asymptomatic nature and reliance on subjective electrocardiography (ECG) interpretation, prone to observer bias.
Automated systems for MI detection from ECG images require high dependability, simplicity, and decision-making accuracy for clinical utility.

Purpose of the Study:

To develop a dependable and accurate automated system for early detection of cardiovascular diseases using ECG images.
To classify ECG images into three categories: Myocardial Infarction, Abnormal heartbeat, and Normal.
To address the need for improved diagnostic tools in clinical settings for MI detection.

Main Methods:

A dataset of 928 ECG images from Mendeley Data was utilized.
ECG images were pre-processed and split into training (70%), validation (20%), and testing (10%) sets.
A Siamese Network model was employed for training and classification.

Main Results:

The developed model achieved a classification accuracy of 98%.
The algorithm demonstrated excellent performance on imbalanced datasets by processing image pairs.
Evaluation metrics for validation and testing datasets indicated near-perfect performance.

Conclusions:

A Siamese Network model was successfully developed for early cardiovascular disease detection based on ECG signals.
The study highlights the potential of automated ECG analysis for improved MI diagnosis.
The model's high accuracy and ability to handle class imbalance offer a promising tool for clinical application.