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Related Concept Videos

Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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Electrocardiogram01:29

Electrocardiogram

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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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Pulse rhythm01:30

Pulse rhythm

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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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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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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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Related Experiment Video

Updated: Jul 24, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction.

Qingxue Zhang1, Dian Zhou2

  • 1Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Purdue School of Engineering and Technology, 723 W. Michigan St., Indianapolis, IN 46202, USA.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

A new machine learning framework accurately estimates heart health using mobile electrocardiogram (ECG) signals. This approach extracts crucial QRS duration patterns from noisy data for improved remote heart monitoring.

Keywords:
ECGmachine learningmedical decision supportpattern recognitionsmart health

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Signal Processing

Background:

  • Internet-of-Things (IoT) technologies are transforming healthcare delivery.
  • Focus on long-term, out-of-clinic electrocardiogram (ECG)-based heart health management.
  • Need for robust analysis of noisy mobile ECG signals.

Purpose of the Study:

  • Propose a machine learning framework for extracting vital patterns from mobile ECG.
  • Accurately estimate heart-disease-related ECG QRS duration from mobile signals.
  • Enhance the reliability of remote ECG monitoring.

Main Methods:

  • A three-stage hybrid machine learning framework.
  • Support Vector Machine (SVM) for heartbeat recognition.
  • Multiview Dynamic Time Warping (MV-DTW) for QRS boundary detection and artifact quantification.
  • Regression model to convert mobile ECG QRS duration to standard chest ECG duration.

Main Results:

  • Highly encouraging performance in ECG QRS duration estimation.
  • Correlation coefficient of 91.2% compared to traditional chest ECG measurements.
  • Mean error/standard deviation of 0.4 ± 2.6 ms, mean absolute error of 1.7 ms, and root mean absolute error of 2.6 ms.

Conclusions:

  • Demonstrated effectiveness of the proposed framework through promising experimental results.
  • Significant advancement in machine learning-enabled ECG data mining.
  • Potential to enhance smart medical decision support systems for cardiology.