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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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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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Electrocardiogram Fundamentals01:28

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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.
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Related Experiment Video

Updated: Aug 28, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals.

Erdenebayar Urtnasan1, Jong-Uk Park2, Jung-Hun Lee3

  • 1Artificial Intelligence Big Data Medical Center, Wonju College of Medicine, Yonsei University, Wonju 26426, Korea.

Diagnostics (Basel, Switzerland)
|September 23, 2022
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Summary

A deep learning model, deepPLM, automatically detects periodic limb movement syndrome (PLMS) using electrocardiogram (ECG) signals. This AI tool shows promise for screening PLMS and supporting elderly home healthcare.

Keywords:
convolutional neural networkdeep learningelectrocardiogramlong short-term memoryperiodic limb movement syndrome

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Area of Science:

  • Cardiology
  • Neurology
  • Artificial Intelligence

Background:

  • Periodic Limb Movement Syndrome (PLMS) diagnosis often relies on polysomnography, a resource-intensive method.
  • Automated detection of PLMS using non-invasive physiological signals is an area of active research.

Purpose of the Study:

  • To develop and validate a deep learning model (deepPLM) for automatic detection of PLMS from single-lead electrocardiogram (ECG) signals.
  • To assess the efficacy of deepPLM as a potential screening tool for PLMS.

Main Methods:

  • A deep learning model, deepPLM, comprising convolutional and recurrent layers was designed.
  • The model was trained, validated, and tested on single-lead ECG data from the Osteoporotic Fractures in Men sleep (MrOS) study dataset (52 subjects).
  • ECG signals were normalized, segmented into 10-second epochs, and divided into training, validation, and testing sets.

Main Results:

  • The deepPLM model achieved high performance metrics: F1-scores of 92.0% for controls and 92.0% for patients.
  • Precision and recall scores were also robust, reaching 90.0% and 93.0% for controls, and 93.0% and 90.0% for patients, respectively.
  • These results indicate accurate differentiation between individuals with and without PLMS based on ECG data.

Conclusions:

  • The deepPLM model demonstrates the feasibility of automatically detecting PLMS using only single-lead ECG signals.
  • This approach offers a potential non-invasive and cost-effective alternative for PLMS screening.
  • The technology could be valuable for remote patient monitoring and home healthcare, particularly for the elderly population.