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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.
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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.
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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
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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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Related Experiment Video

Updated: Sep 14, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Convolutional Neural Network-Transformer Model to Predict and Classify Early Arrhythmia Using Electrocardiogram

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  • 1Jain University; manjeshbn@gmail.com.

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Summary

This study presents a deep learning model for accurate arrhythmia detection from ECG signals, achieving 99.99% accuracy in identifying five key heartbeat types for improved cardiovascular disease diagnosis.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Cardiovascular diseases, particularly arrhythmias, are a leading cause of global mortality.
  • Precise and automated diagnostic technologies for early detection are crucial.

Purpose of the Study:

  • To develop a deep learning model for classifying five main heartbeat types from electrocardiogram (ECG) signals.
  • To achieve high accuracy and robustness in automated arrhythmia detection.

Main Methods:

  • Utilized Lead I ECG signals from multiple large databases (INCART, MIT-BIH, etc.) with over 3.9 million training segments.
  • Employed a hybrid deep learning architecture combining Transformer layers for temporal dependencies and 1D CNNs for spatial feature extraction.
  • Applied data preprocessing techniques including window segmentation, Min-Max normalization, and SMOTE for class balancing.

Main Results:

  • The proposed model achieved an outstanding accuracy, precision, and F1-score of 99.99% across all five heartbeat classes (Normal, Left Bundle Branch Block, Right Bundle Branch Block, Atrial Premature Beat, Premature Ventricular Contraction).
  • Demonstrated superior performance compared to existing benchmarks like the TN4 model.
  • Validated feature robustness through deep hybrid architectures and CNNs.

Conclusions:

  • The developed AI-driven model offers a promising solution for scalable, real-time arrhythmia identification.
  • This technology has the potential to advance individualized digital healthcare for cardiovascular disease management.