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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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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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Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
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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.
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...
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

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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...
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QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases.

Indu Saini1, Dilbag Singh2, Arun Khosla1

  • 1Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144 011, India.

Journal of Advanced Research
|February 17, 2015
PubMed
Summary

This study introduces a K-Nearest Neighbor (KNN) algorithm for accurate QRS-complex detection in electrocardiogram (ECG) analysis. The KNN classifier achieved high detection rates on standard databases, establishing its reliability for precise ECG interpretation.

Keywords:
ClassifierCross-validationECGGradientKNNQRS detection

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Accurate QRS-complex delineation is crucial for computer-aided ECG analysis.
  • Existing methods may struggle with signal interference and require precise feature extraction.

Purpose of the Study:

  • To apply the K-Nearest Neighbor (KNN) algorithm for robust QRS-complex detection in ECG signals.
  • To evaluate the performance of the proposed KNN classifier on standard ECG databases.

Main Methods:

  • Utilized a digital band-pass filter to mitigate signal interference.
  • Employed signal gradient as a key feature for QRS detection.
  • Implemented KNN with K=3 and Euclidean distance, validated using fivefold cross-validation.

Main Results:

  • Achieved high detection rates: 99.89% on CSE database and 99.81% on MIT-BIH Arrhythmia database.
  • Demonstrated excellent sensitivity (Se) and specificity (Sp): CSE (Se=99.86%, Sp=99.86%), MIT-BIH (Se=99.81%, Sp=99.86%).
  • Comparative analysis confirmed the proposed algorithm's superior performance.

Conclusions:

  • The K-Nearest Neighbor (KNN) algorithm provides a reliable and accurate method for QRS-complex detection.
  • The proposed approach effectively handles ECG signal interference and achieves high diagnostic accuracy.
  • This study validates KNN as a powerful tool for automated ECG analysis.