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Related Concept Videos

Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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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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Electrocardiogram01:29

Electrocardiogram

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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...
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Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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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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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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.
Parts of an ECG
An ECG utilizes electrodes on the skin...
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Related Experiment Video

Updated: Jan 13, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Deep Learning-Based Risk Assessment and Prediction of Cardiac Outcomes Using Single-Lead 24-Hour Holter-ECG in

Ju Youn Kim1, Kyung Geun Kim2,3, Sunghoon Joo2

  • 1Division of Cardiology, Department of Internal Medicine, Heart Vascular and Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea.

Journal of Clinical Medicine
|October 29, 2025
PubMed
Summary

A deep learning model using Holter-ECG effectively predicts cardiac death and arrhythmias after heart attack or heart failure. This advanced tool surpasses traditional markers, offering improved risk stratification for better patient outcomes.

Keywords:
T-wave alternanscardiac deathdeep learningejection fractionheart failureheart rate turbulencemyocardial infarctionventricular arrhythmia

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Risk stratification after myocardial infarction (MI) and heart failure (HF) is crucial for patient management.
  • Holter electrocardiogram (ECG) monitoring provides valuable data for cardiac assessment.
  • Deep learning (DL) models show promise in analyzing complex ECG data for prognostic insights.

Purpose of the Study:

  • To assess the prognostic performance of a Holter-ECG-based DL model in predicting major adverse cardiac events (MACE).
  • To compare the DL model's predictive power against established noninvasive markers like heart rate turbulence (HRT), T-wave alternans (TWA), and ejection fraction (EF).
  • To evaluate the DL model's ability to stratify risk for cardiac death and ventricular arrhythmias.

Main Methods:

  • The K-REDEFINE study enrolled 1108 patients with acute MI or HF who underwent 24-hour Holter monitoring.
  • A DL model was developed using raw Holter-ECG data to predict a composite outcome of cardiac death and ventricular arrhythmias.
  • Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and compared with HRT, TWA, and EF.

Main Results:

  • The DL model achieved an AUROC of 0.74 for the composite outcome, outperforming HRT (0.62) and TWA (0.55).
  • Combining the DL model with EF improved the AUROC to 0.77. For cardiac death alone, the DL model achieved an AUROC of 0.79, further enhanced to 0.82 with EF.
  • Risk stratification using the DL model identified a seven-fold increase in cardiac death risk in the high-risk group (HR 7.47, p < 0.001), particularly in patients with EF > 40%.

Conclusions:

  • A DL algorithm trained on single-lead Holter-ECG data demonstrates significant efficacy in predicting cardiac death and ventricular arrhythmias.
  • The DL model's performance surpasses conventional noninvasive markers.
  • Integration with EF further enhances predictive accuracy, supporting the DL model's potential for scalable, noninvasive risk stratification in cardiology.