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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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 the T...
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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

Correlation between ECG and Cardiac Cycle

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...
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

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...
Classification of Signals01:30

Classification of Signals

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.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

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Related Experiment Video

Updated: Jul 5, 2026

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

Detection of QRS complexes in electrocardiogram using support vector machine.

S S Mehta1, N S Lingayat

  • 1Department of Electrical Engineering, J. N. Vyas University, MBM Engineering College, Jodhpur, Rajasthan, India.

Journal of Medical Engineering & Technology
|April 25, 2008
PubMed
Summary
This summary is machine-generated.

This study applies support vector machines (SVM) for accurate electrocardiogram (ECG) QRS complex detection. Using 12-lead ECG data significantly improves detection rates and reduces errors compared to single-lead analysis.

Related Experiment Videos

Last Updated: Jul 5, 2026

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

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
  • Accurate detection of QRS complexes is fundamental for automated ECG interpretation.
  • Existing QRS detection methods face challenges with noise and signal variability.

Purpose of the Study:

  • To develop and evaluate a support vector machine (SVM) based algorithm for QRS complex detection in ECG signals.
  • To compare the performance of single-lead versus 12-lead ECG analysis for QRS detection.
  • To assess the impact of data preprocessing and classifier parameters on detection accuracy.

Main Methods:

  • Digital filtering techniques were employed to preprocess ECG signals, removing noise and baseline wander.
  • A support vector machine (SVM) was utilized as a classifier to distinguish QRS complexes from other ECG segments.
  • Two distinct algorithms were developed: one for single-lead ECG and another for simultaneous 12-lead ECG analysis.
  • The algorithms were validated using the standard CSE ECG database.

Main Results:

  • The single-lead ECG algorithm achieved a QRS detection rate of 99.3%.
  • The 12-lead ECG algorithm demonstrated a superior detection rate of 99.75%.
  • False negative rates decreased from 0.7% (single-lead) to 0.26% (12-lead).
  • False positive rates significantly reduced from 12.4% (single-lead) to 1.61% (12-lead).

Conclusions:

  • Support vector machines provide an effective method for QRS complex detection in ECG.
  • Simultaneous analysis of 12-lead ECG data yields significantly higher accuracy and fewer errors than single-lead analysis.
  • Optimal performance is contingent upon careful selection of training data, data representation, and classifier parameters.