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Related Concept Videos

Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

155
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...
155
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

1.5K
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...
1.5K
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

478
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
478
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

197
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
197
Pulse rhythm01:30

Pulse rhythm

981
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...
981
Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

154
Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
154

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Related Experiment Video

Updated: Oct 9, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

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Inter-patient arrhythmia classification with improved deep residual convolutional neural network.

Yuanlu Li1, Renfei Qian2, Kun Li2

  • 1B-DAT, School of Automation, Nanjing University of Information Science & Technology, Nanjing, China, 210044; Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, China, 210044.

Computer Methods and Programs in Biomedicine
|December 21, 2021
PubMed
Summary

An improved deep residual convolutional neural network effectively classifies arrhythmias from ECG segments. This method enhances accuracy, especially for ventricular ectopic beats, using overlapping segmentation and focal loss for better clinical application.

Keywords:
Arrhythmia classificationConvolutional neural networkDeep learningElectrocardiogram (ECG)

Related Experiment Videos

Last Updated: Oct 9, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.9K

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Early detection of arrhythmias is crucial for reducing cardiovascular disease mortality.
  • Electrocardiogram (ECG) analysis is vital for diagnosing arrhythmias.
  • Segment-based ECG classification is preferred for clinical settings.

Purpose of the Study:

  • To develop an improved deep residual convolutional neural network for automated arrhythmia classification from ECG segments.
  • To enhance the clinical applicability of ECG-based arrhythmia detection.

Main Methods:

  • Utilized overlapping segmentation to create 5-second ECG segments from the MIT-BIH database, addressing class imbalance.
  • Applied discrete wavelet transform (DWT) for denoising ECG segments.
  • Employed an improved deep residual convolutional neural network with focal loss for classification.

Main Results:

  • Achieved high performance for normal segments (94.54% sensitivity, 93.33% positive predictivity, 80.80% specificity).
  • Demonstrated strong results for ventricular ectopic segments (88.35% sensitivity, 79.86% positive predictivity, 94.92% specificity).
  • Showcased improved classification performance with the focal loss function.

Conclusions:

  • The proposed deep residual convolutional neural network model demonstrates comparable performance to existing methods.
  • Overlapping segmentation and focal loss significantly improve arrhythmia classification accuracy.
  • The method shows promise for clinical application in automated arrhythmia detection.