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Electrophysiology of Normal Cardiac Rhythm

Electrophysiology of Normal Cardiac Rhythm

The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase of...

Mechanism of Cardiac Arrhythmias

Mechanism of Cardiac Arrhythmias

Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.

Disturbances in Heart Rhythm

Disturbances in Heart Rhythm

Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...

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...

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...

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias V: Evaluating Dysrhythmias

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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Related Experiment Video

Updated: Jun 9, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

An Artificial Multi-Channel Model for Generating Abnormal Electrocardiographic Rhythms.

Gd Clifford¹, S Nemati, R Sameni

¹Harvard-MIT Division of Health Sciences and Technology (HST), Cambridge, MA 02142, USA.

Computers in Cardiology

|September 3, 2010

Summary

This summary is machine-generated.

This study introduces an advanced artificial model for generating multi-channel electrocardiograms (ECG) with abnormal heart rhythms. The model simulates conditions like T-Wave Alternans (TWA) and incorporates realistic beat variations for improved diagnostic tools.

Related Experiment Videos

Last Updated: Jun 9, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Area of Science:

Computational physiology and signal processing.
Artificial intelligence in medical diagnostics.

Background:

Existing artificial models for multi-channel ECG generation lacked the capability to simulate abnormal heart rhythms.
Accurate simulation of cardiac electrical activity, including arrhythmias, is crucial for developing and testing diagnostic algorithms.

Purpose of the Study:

To generalize previous ECG generation models to enable the simulation of abnormal cardiac rhythms.
To incorporate realistic beat-to-beat variations, heart rate variability, and specific phenomena like T-Wave Alternans (TWA).

Main Methods:

Utilized a three-dimensional vectorcardiogram (VCG) formulation with Gaussian kernels to model normal cardiac dipoles.
Introduced abnormal beats as new or perturbed dipoles, controlled by a hidden Markov model (HMM) coupled to heart rate.
Simulated QT-HR hysteresis, respiration-induced morphology changes, and TWA using VCG perturbations and HMMs, mapping VCG to clinical ECG leads via a Dower-like transform.

Main Results:

Successfully generated multi-channel ECG signals with simulated abnormal rhythms, including TWA.
Demonstrated realistic beat-to-beat morphology changes and QT-HR hysteresis.
Modeled TWA magnitude and its HR-dependent probability, mimicking physiological associations.

Conclusions:

The generalized artificial model effectively simulates normal and abnormal cardiac rhythms, including TWA, with physiological realism.
The model provides a valuable tool for developing and validating ECG analysis algorithms, particularly for arrhythmia detection.
Generated ECGs with calibrated TWA were successfully included in the PhysioNet/CinC Challenge 2008 dataset.