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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.2K
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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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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Pulse rhythm01:30

Pulse rhythm

775
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...
775

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Related Experiment Video

Updated: Jun 14, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model.

Hamed Alqahtani1, Ghadah Aldehim2, Nuha Alruwais3

  • 1Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia.

Heliyon
|September 3, 2024
PubMed
Summary

This study presents an automated deep learning technique for ECG signal recognition to detect cardiovascular diseases. The ADL-ECGSR method achieved 91.24% accuracy, improving upon existing approaches for arrhythmia detection.

Keywords:
Cardiovascular diseaseDeep learningECG signalsHealthcareParameter tuningPattern recognition

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Electrocardiography (ECG) is a crucial non-invasive tool for diagnosing cardiovascular diseases (CVDs).
  • Accurate and rapid detection of arrhythmias like atrial fibrillation and ventricular tachycardia is vital for patient outcomes.
  • Challenges in ECG analysis include high waveform variability and noise, impacting automated recognition model performance.

Purpose of the Study:

  • To introduce an automated deep learning enabled ECG signal recognition (ADL-ECGSR) technique for CVD detection and classification.
  • To enhance the accuracy and efficiency of automated ECG analysis in clinical decision-making systems.
  • To address the challenges of waveform variability and noise in ECG signal processing.

Main Methods:

  • The ADL-ECGSR technique integrates pre-processing, feature extraction, parameter tuning, and classification.
  • A bidirectional long short-term memory (BiLSTM) network serves as the feature extractor, optimized using the Adamax optimizer.
  • The dragonfly algorithm (DFA) combined with a stacked sparse autoencoder (SSAE) module is employed for signal recognition and classification.

Main Results:

  • The ADL-ECGSR technique demonstrated a remarkable performance of 91.24% accuracy on the PTB-XL benchmark dataset.
  • Comparative analysis confirmed the enhanced ECG recognition efficiency of the proposed methodology over existing methods.
  • The study validates the effectiveness of DL models in improving cardiovascular disease detection from ECG signals.

Conclusions:

  • The developed ADL-ECGSR technique offers a robust and accurate solution for automated ECG signal recognition and CVD classification.
  • The integration of BiLSTM, Adamax, DFA, and SSAE shows significant potential for advancing automated cardiac diagnostics.
  • This deep learning approach holds promise for improving the reliability of healthcare decision-making systems in cardiology.