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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...

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...

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...

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Updated: Jul 5, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Incremental HMM training applied to ECG signal analysis.

Rodrigo V Andreão¹, Sandra M T Muller, Jérôme Boudy

¹Coordenadoria de Eletrotécnica, CEFETES, Av. Vitória, 1729, Jucutuquara, Vitória, ES, CEP 29040-780, Brazil. rodrigo@ele.ufes.br

Computers in Biology and Medicine

|May 9, 2008

Summary

Incremental Hidden Markov Model (HMM) training enhances electrocardiogram (ECG) analysis by improving beat segmentation and ischemia detection. This method offers improved performance with reduced computational cost for personalized ECG signal modeling.

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Last Updated: Jul 5, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Area of Science:

Biomedical Engineering
Signal Processing
Machine Learning

Background:

Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
Traditional Hidden Markov Models (HMMs) require significant computational resources for training.
Personalized ECG analysis necessitates adaptive modeling techniques.

Purpose of the Study:

To implement and evaluate incremental training methods for HMMs in ECG analysis.
To adapt HMMs to individual ECG signal characteristics.
To assess the impact of incremental training on beat segmentation and ischemia detection.

Main Methods:

Utilized incremental versions of Expectation-Maximization (EM), Segmental k-means, and Bayesian approaches for HMM training.
Modeled ECG signals as sequences of elementary waveforms.
Implemented an adaptation process for individual ECG signal modeling.

Main Results:

Incremental HMM training demonstrated improved beat segmentation accuracy.
Enhanced performance in ischemia detection was observed using incremental methods.
The incremental approaches offered a low computational effort compared to standard methods.

Conclusions:

Incremental HMM training is an efficient strategy for ECG analysis.
Personalized ECG modeling can be achieved effectively with adaptive incremental methods.
These techniques offer a computationally advantageous approach to cardiac diagnostics.