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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

476
Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
476
Electrocardiogram01:29

Electrocardiogram

2.1K
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...
2.1K
Pulse rhythm01:30

Pulse rhythm

754
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
754
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

419
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
419
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

171
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
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Related Experiment Video

Updated: May 28, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

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Deep Learning-Driven Single-Lead ECG Classification: A Rapid Approach for Comprehensive Cardiac Diagnostics.

Mohamed Ezz1

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|February 13, 2025
PubMed
Summary

Advanced deep learning models can accurately classify cardiac conditions using single-lead ECG data. This enables accessible, real-time cardiac diagnostics, improving global healthcare accessibility.

Keywords:
VGG16cardiac condition classificationcardiovascular diseases (CVDs)deep learning modelssingle-lead ECGtelemedicine applications

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Addressing the need for accessible cardiac diagnostics in remote or resource-limited settings.
  • Exploring single-lead ECG analysis as an alternative to traditional multi-lead ECG.
  • Utilizing deep learning for classifying cardiac conditions like Myocardial Infarction (MI).

Purpose of the Study:

  • Evaluate deep learning models for single-lead ECG analysis.
  • Identify optimal models for accuracy, inference time, and size.
  • Facilitate development of portable cardiac diagnostic tools.

Main Methods:

  • Systematic evaluation of five deep learning architectures: Inception, DenseNet201, MobileNetV2, NASNetLarge, and VGG16.
  • Analysis of model performance using metrics like F1-score, inference time, and model size.
  • Testing on individual ECG leads for cardiac condition classification.

Main Results:

  • VGG16 achieved the highest F1-score (98.11%) with a 4.2 ms prediction time, suitable for high-precision settings.
  • MobileNetV2 offered a balanced performance with a 97.24% F1-score, 3.2 ms inference time, and a compact 13.4 MB size, ideal for real-time monitoring.
  • Both models demonstrated high accuracy in classifying Normal, Abnormal, Previous Myocardial Infarction (PMI), and Myocardial Infarction (MI).

Conclusions:

  • Demonstrated the feasibility of lightweight, scalable single-lead ECG analysis using deep learning.
  • Paved the way for portable diagnostic tools to enhance global cardiac care accessibility.
  • Highlighted the potential of MobileNetV2 for real-time monitoring and telehealth applications.