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Related Concept Videos

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

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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.
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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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Abnormal recognition-assisted and onset-offset aware network for pathological wearable ECG delineation.

Yue Zhang1, Jiewei Lai1, Chenyu Zhao1

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.

Artificial Intelligence in Medicine
|October 6, 2024
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Summary

This study introduces a novel network for electrocardiogram (ECG) delineation, improving abnormal cardiac status detection in wearable devices. The method enhances precise waveform localization and generalizes well to diverse pathological ECG patterns.

Keywords:
Abnormal recognition-assisted networkOnset-offset aware lossPathological ECG delineationWearable ECG

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Electrocardiogram (ECG) delineation is critical for diagnosing cardiac conditions, particularly with remote monitoring via wearable devices.
  • Identifying diverse pathological ECG patterns presents a significant challenge due to the complexity of cardiac abnormalities.

Purpose of the Study:

  • To develop an advanced network for accurate ECG delineation and abnormal pattern recognition.
  • To enhance precise waveform localization for improved diagnostic capabilities.

Main Methods:

  • A two-branch framework was established, with ECG delineation as the primary task and an abnormal recognition-assisted network as an auxiliary task.
  • An onset-offset aware loss function was designed to focus on precise localization of ECG waveform features.
  • A large-scale wearable 12-lead ECG dataset comprising 4,913 signals was utilized for training and validation.

Main Results:

  • The proposed method achieved high sensitivity (94.97% and 94.27%) on test datasets.
  • Excellent localization accuracy was demonstrated with an error tolerance below 20 ms.
  • The model proved effective in identifying various abnormal ECG signals, including ST-segment changes and bundle branch blocks.

Conclusions:

  • The joint learning approach effectively correlates ECG delineation with abnormality recognition, enhancing model generalization.
  • The onset-offset aware loss significantly improves the precision of waveform localization.
  • This method offers a robust solution for accurate ECG analysis in wearable health monitoring systems.