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Electrocardiogram

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 Fundamentals

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...

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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...

Pulse rhythm

Pulse rhythm

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Correlation between ECG and Cardiac Cycle

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 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.
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A unified procedure for detecting, quantifying, and validating electrocardiogram T-wave alternans.

H Naseri¹, H Pourkhajeh, M R Homaeinezhad

¹Department of Mechanical Engineering, Faculty of Mechanical Engineering, KN Toosi University of Technology, PO Box 19395-1999, Tehran, Iran. hnaseri@gmx.com

Medical & Biological Engineering & Computing

|May 23, 2013

Summary

This study introduces a new method for precisely quantifying T-wave alternans (TWA) in electrocardiograms (ECGs). The approach improves TWA detection accuracy by optimizing T-wave alignment and incorporating ECG quality feedback.

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Measurement of Heart Contractility in Isolated Adult Human Primary Cardiomyocytes

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Area of Science:

Cardiology
Biomedical Engineering
Signal Processing

Background:

Precise quantification of T-wave alternans (TWA) is crucial for cardiac diagnostics.
The accuracy of TWA measurement is significantly influenced by T-wave alignment and electrocardiogram (ECG) signal quality.

Purpose of the Study:

To develop and validate a novel method for automated TWA quantification in ECGs.
The study aims to enhance TWA measurement precision through optimized T-wave alignment and ECG quality assessment.

Main Methods:

A seven-section method involving preprocessing, ECG event detection-delineation, cycle alignment, T-wave template extraction, delineation, lobe synchronization, and quality-feedback-based TWA quantification.
Parameter optimization using artificially generated ECGs with controlled TWA patterns and qualities.
Application and evaluation on the PhysioNet/Computing in Cardiology Challenge 2008 database.

Main Results:

The proposed method achieved approximately 91.0% accuracy when applied to the PhysioNet database.
Validation on synthetic data allowed for parameter tuning to maximize performance.
The method demonstrates a marginal improvement in the field of TWA quantification.

Conclusions:

The developed automated method offers a reliable approach for TWA quantification.
Incorporating ECG quality feedback and optimized T-wave alignment enhances measurement precision.
The findings suggest potential for improved cardiac risk stratification using TWA analysis.