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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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

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Artificial intelligence in cardiac telemetry.

Jiaying Lu¹, Ran Xiao¹, Xiao Hu^1,2

¹Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, USA.

Heart (British Cardiac Society)

|March 23, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing cardiac telemetry, moving beyond traditional methods to deep neural networks for superior cardiac monitoring. These advanced AI models improve real-time analysis, predictive capabilities, and personalized patient care.

Keywords:

arrhythmias, cardiac electrocardiography

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Area of Science:

Cardiology
Biomedical Engineering
Artificial Intelligence

Background:

Cardiac telemetry is essential for continuous cardiac monitoring and early detection of heart abnormalities.
Traditional statistical machine learning models have been the standard in cardiac telemetry analysis.
There is a growing integration of artificial intelligence (AI) into cardiac telemetry systems.

Purpose of the Study:

To review the current state of AI in cardiac telemetry.
To focus on deep learning techniques and their clinical applications.
To examine challenges, limitations, and future directions of AI in this field.

Main Methods:

Review of current literature on AI applications in cardiac telemetry.
Focus on deep neural networks compared to traditional machine learning models.
Analysis of clinical applications, challenges, and future trends.

Main Results:

Deep neural networks demonstrate superior accuracy in detecting complex patterns in telemetry data.
AI enhances real-time monitoring, predictive analytics, and personalized cardiac care.
Shift from traditional statistical models to advanced deep learning techniques is evident.

Conclusions:

AI, particularly deep learning, represents a significant advancement in cardiac telemetry.
These technologies offer enhanced capabilities for monitoring, prediction, and personalized treatment.
Further research is needed to address challenges and fully realize the potential of AI in cardiology.