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The Cardiac Cycle

The Cardiac Cycle

The heart beats rhythmically in a sequence called the cardiac cycle—a rapid coordination of contraction (systole) and relaxation (diastole).
The Process
Electrical signals—sent from the sinoatrial (SA) node in the right atrial wall to the atrioventricular (AV) node between the right atrium and right ventricle—cause both atria to simultaneously contract. When the signal reaches the AV node, it pauses for approximately a tenth of a second, allowing the atria to contract and empty blood into the...

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Mechanism of Cardiac Arrhythmias

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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.

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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...

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Related Experiment Video

Updated: Jun 20, 2026

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Published on: July 29, 2011

Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.

Jung-Chi Hsu¹, Yi-Hsien Hsieh², Yen-Yun Yang³

¹Department of Internal Medicine, National Taiwan University Hospital Jinshan Branch, New Taipei City, Taiwan; Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.

|June 11, 2025

Summary

This summary is machine-generated.

A new model, ForeSIIN, accurately predicts stroke risk in atrial fibrillation (AF) patients. This advanced tool interprets dynamic risk factors over time, improving patient care.

Keywords:

atrial fibrillation explainable artificial intelligence gated recurrent unit recurrent neural network (RNN)stroke

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Last Updated: Jun 20, 2026

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Published on: July 29, 2011

The WATCHMAN Left Atrial Appendage Closure Device for Atrial Fibrillation

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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

Area of Science:

Cardiology
Neurology
Artificial Intelligence in Medicine

Background:

Atrial fibrillation (AF) is a significant risk factor for transient ischemic attack (TIA) and ischemic stroke (IS).
Accurate prediction of IS risk is crucial for managing AF patients.

Purpose of the Study:

To develop and validate a high-dimensional time-series model for predicting IS risk in AF patients.
To improve upon existing risk prediction models by incorporating dynamic temporal data.

Main Methods:

A cohort study included 7,710 AF patients (2014-2019) with external validation on 6,822 patients.
The Forecasting Strokes via Interpretable Independent Networks (ForeSIIN) model, utilizing gated recurrent units, was developed.
Kaplan-Meier analysis and log-rank tests assessed risk group differences.

Main Results:

The ForeSIIN model achieved an AUC of 0.764, outperforming the CHA2DS2-VASc score (AUC: 0.650) and other non-sequential models.
External validation yielded an AUC of 0.646.
Key predictors identified include history of TIA/IS, eGFR, C-reactive protein, hematocrit, and plasma fasting glucose.

Conclusions:

The ForeSIIN model offers accurate stroke prediction in AF patients.
This innovative model enhances the interpretation of dynamic risk factors over time.