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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.2K
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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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

3.5K
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....
3.5K
Pulse rhythm01:30

Pulse rhythm

917
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...
917
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

860
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...
860
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

232
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...
232
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

8.2K
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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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Wearable Electrocardiogram Quality Assessment Using Wavelet Scattering and LSTM.

Feifei Liu1, Shengxiang Xia1, Shoushui Wei2

  • 1School of Science, Shandong Jianzhu University, Jinan, China.

Frontiers in Physiology
|July 18, 2022
PubMed
Summary

A new method accurately classifies wearable electrocardiogram (ECG) signal quality, crucial for real-time cardiovascular disease monitoring despite noise. This approach enhances diagnostic reliability in free-living conditions.

Keywords:
dynamic electrocardiogramlong short-term memory networksignal-quality assessmentsignal-quality indexwavelet scattering

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiovascular Health

Background:

  • Real-time electrocardiogram (ECG) monitoring is vital for cardiovascular disease (CVD) detection, especially with the rise of wearable devices and the Internet of Things.
  • Dynamic ECG signals from free-living conditions are often severely affected by noise, limiting the effectiveness of traditional signal quality assessment algorithms.
  • Existing methods that merely classify signals as acceptable or unacceptable are insufficient for nuanced, real-time CVD monitoring.

Purpose of the Study:

  • To develop an advanced signal quality assessment (SQA) classification method for wearable ECG data.
  • To improve the accuracy and robustness of ECG signal evaluation in noisy, real-world environments.
  • To enable more reliable long-term dynamic ECG monitoring for cardiovascular health.

Main Methods:

  • Creation of a large wearable ECG quality database comprising 50,085 recordings across three grades: A (high), B (medium), and C (low quality).
  • Implementation of a novel SQA classification approach utilizing a three-layer wavelet scattering network combined with transfer learning Long Short-Term Memory (LSTM).
  • Systematic and deep analysis of ECG signals to extract comprehensive characteristics for quality classification.

Main Results:

  • The proposed method achieved high classification accuracies across all quality grades (e.g., 97.90% for A, 98.16% for B, 99.60% for C).
  • Experimental results and real-world data validation demonstrated the method's high accuracy, robustness, and computational efficiency.
  • The SQA method effectively distinguishes between high, medium, and low-quality ECG signals, enabling better data selection for analysis.

Conclusions:

  • The developed SQA method offers a significant advancement in evaluating long-term dynamic ECG signal quality.
  • By accurately removing contaminating signals and selecting high-quality segments, this method is advantageous for promoting effective cardiovascular disease monitoring.
  • The approach provides a reliable foundation for utilizing wearable ECG data in clinical and research settings.