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Related Concept Videos

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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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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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.
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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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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.
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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: Jan 16, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Sequential AI-ECG Diagnostic Protocol for Opportunistic Atrial Fibrillation Screening: A Retrospective Single-Center

Ji-Hoon Choi1, Sung-Hee Song2, Jongwoo Kim2

  • 1Division of Cardiology, Department of Internal Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul 05030, Republic of Korea.

Journal of Clinical Medicine
|September 27, 2025
PubMed
Summary

An AI ECG screening protocol using serial ECGs improves atrial fibrillation detection. This two-stage approach enhances accuracy and prioritizes monitoring for high-risk patients, aiding timely anticoagulation.

Keywords:
artificial intelligenceatrial fibrillationelectrocardiogramscreening

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Atrial fibrillation (AF) episodes are often asymptomatic, delaying crucial anticoagulation treatment.
  • Current screening methods may miss intermittent AF, impacting patient outcomes.
  • Resource limitations necessitate efficient and accurate screening tools.

Purpose of the Study:

  • To evaluate a two-stage artificial intelligence (AI) electrocardiogram (ECG) screening protocol for detecting atrial fibrillation (AF).
  • To enhance diagnostic accuracy and optimize resource allocation in AF screening.
  • To assess the performance of a serial ECG AI model following initial screening.

Main Methods:

  • Analysis of 248,612 12-lead ECGs from 164,793 adults for AI model development.
  • Implementation of a two-stage protocol: initial single-ECG AI model followed by a serial-ECG AI model at three months if needed.
  • Performance evaluation using metrics like AUROC, sensitivity, specificity, accuracy, and F1 score in 11,349 eligible patients.

Main Results:

  • The protocol achieved a high area under the receiver operating characteristic curve (AUROC) of 0.908.
  • Sensitivity was 88.1%, specificity 78.7%, and negative predictive value (NPV) 98.4%.
  • The protocol correctly identified 84.9% of AF-positive patients with a history of stroke.

Conclusions:

  • A sequential AI ECG strategy effectively maintains a high NPV and improves positive predictive value (PPV) through longitudinal confirmation.
  • This AI-driven approach can prioritize ambulatory monitoring for patients most likely to benefit from AF detection.
  • Further prospective, multi-center validation and cost-effectiveness studies are warranted.