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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

9.9K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Electrocardiogram01:29

Electrocardiogram

4.3K
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...
4.3K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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...
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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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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....
7.0K
Pulse rhythm01:30

Pulse rhythm

1.0K
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...
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Special considerations while measuring pulse01:13

Special considerations while measuring pulse

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Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:
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Related Experiment Video

Updated: Oct 30, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

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Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition.

Dalal A AlDuwaile1, Md Saiful Islam1

  • 1Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Entropy (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study demonstrates that short electrocardiogram (ECG) segments, when analyzed using deep learning and time-frequency representations, can achieve high accuracy for biometric recognition. This overcomes limitations of requiring long ECG signal lengths for reliable authentication.

Keywords:
ECG signalbiometricscontinuous wavelet transformationconvolutional neural networkdeep learning

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Area of Science:

  • Biometrics
  • Signal Processing
  • Machine Learning

Background:

  • Electrocardiogram (ECG) signals are a promising biometric modality.
  • Current ECG biometric methods require long signal segments, limiting acceptability.
  • Short ECG segments present a challenge for accurate biometric recognition.

Purpose of the Study:

  • To investigate the effectiveness of short ECG signal segments for biometric recognition.
  • To develop a deep learning model for enhanced recognition using short ECG segments.
  • To evaluate the impact of segment length and type on recognition performance.

Main Methods:

  • A small convolutional neural network (CNN) with entropy enhancement was designed.
  • Time-frequency domain representation of short ECG segments around the R-peak was utilized.
  • Experiments were conducted on two databases (PTB and ECG-ID) with single and multisession records.
  • The proposed model was compared against established CNN architectures (GoogLeNet, ResNet, MobileNet, EfficientNet).

Main Results:

  • The proposed model achieved high accuracy: 99.90% (PTB), 98.20% (ECG-ID mixed-session), and 94.18% (ECG-ID multisession).
  • A pre-trained ResNet model achieved 97.28% accuracy on ECG-ID multisession using 0.5-second segments.
  • The proposed approach outperformed existing methods in ECG biometric recognition using short segments.

Conclusions:

  • Short ECG segments are feasible for biometric recognition.
  • Time-frequency domain analysis enhances accuracy and acceptability of ECG biometrics.
  • Deep learning techniques effectively utilize short ECG segments for reliable authentication.