Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

Instrumentation Amplifier

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...

ECG Interpretation of Rhythms

ECG Interpretation of Rhythms

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. When...

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...

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...

Even and Odd Signals

Even and Odd Signals

An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Detection of resistivity for antibiotics by probabilistic neural networks.

Journal of medical systems·2010

Same author

Diagnosis of airway obstruction or restrictive spirometric patterns by multiclass support vector machines.

Journal of medical systems·2010

Same author

Alterations in sleep EEG activity during the hypopnoea episodes.

Journal of medical systems·2010

Same author

Recurrent neural networks for diagnosis of carpal tunnel syndrome using electrophysiologic findings.

Journal of medical systems·2010

Same author

Differentiation of two subtypes of adult hydrocephalus by mixture of experts.

Journal of medical systems·2010

Same author

Automatic detection of erythemato-squamous diseases using k-means clustering.

Journal of medical systems·2010

Search research articles

Related Experiment Videos

Diverse and composite features for ECG signals processing.

Elif Derya Ubeyli¹

¹Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Universitesi, 06530 Söğütözü, Ankara, Turkey. edubeyli@etu.edu.tr

Bio-Medical Materials and Engineering

|April 15, 2008

Summary

This summary is machine-generated.

Automated diagnostic systems for electrocardiogram (ECG) signal classification were compared. Modified mixture of experts (MME) using diverse features achieved higher accuracy than other systems using composite features.

Related Experiment Videos

Area of Science:

Biomedical Engineering
Artificial Intelligence in Healthcare
Signal Processing

Background:

Automated diagnostic systems are crucial for accurate electrocardiogram (ECG) signal interpretation.
Classifying ECG signals with high accuracy is essential for reliable medical decision-making.
Various machine learning models and feature extraction techniques are employed for ECG analysis.

Purpose of the Study:

To analyze and compare the accuracy of automated diagnostic systems for ECG signal classification.
To investigate the performance of different neural network architectures (MLPNN, RNN, ME, MME) with varying feature sets.
To determine the optimal combination of features and network structure for high-accuracy ECG classification.

Main Methods:

Comparison of classification accuracies for multilayer perceptron neural network (MLPNN), recurrent neural network (RNN), and mixture of experts (ME) trained on composite features.
Evaluation of a modified mixture of experts (MME) model trained on diverse features.
Utilized diverse or composite features, including wavelet coefficients and power spectral density estimates from eigenvector methods, as inputs.

Main Results:

The modified mixture of experts (MME) model trained on diverse features demonstrated superior performance.
MME achieved higher accuracy rates compared to MLPNN, RNN, and ME models when trained on composite features.
The choice of features (diverse vs. composite) significantly impacted the diagnostic accuracy of the automated systems.

Conclusions:

Automated diagnostic systems utilizing diverse features with the MME architecture offer enhanced accuracy for ECG signal classification.
The study highlights the importance of feature selection in conjunction with appropriate network design for improved diagnostic outcomes.
The findings suggest that MME with diverse features is a promising approach for reliable automated ECG analysis.