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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.6K
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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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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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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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism.

Byungjin Choi1, Jong Hwan Jang2,3, Minkook Son4

  • 1Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.

European Heart Journal. Digital Health
|January 30, 2023
PubMed
Summary
This summary is machine-generated.

A deep learning model (DLM) can detect overt hyperthyroidism using electrocardiograms (ECG). This non-invasive tool aids early diagnosis and improves patient outcomes by identifying subtle ECG changes.

Keywords:
Artificial intelligenceDeep learningElectrocardiographyHyperthyroidism

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Area of Science:

  • Cardiology
  • Endocrinology
  • Artificial Intelligence in Medicine

Background:

  • Overt hyperthyroidism negatively impacts patient prognosis.
  • Thyroid function tests (TFTs) are not routinely performed, and hyperthyroid symptoms can be subtle and overlooked.
  • Electrocardiograms (ECGs) show associations with thyroid function, but subtle changes are difficult for clinicians to detect.

Purpose of the Study:

  • To develop and validate a deep learning model (DLM) for detecting overt hyperthyroidism using electrocardiographic (ECG) data.
  • To create a non-invasive biomarker for early hyperthyroidism detection.
  • To improve diagnostic capabilities for hyperthyroidism.

Main Methods:

  • A multicentre retrospective cohort study involving over 113,000 patients for internal validation and over 33,000 patients for external validation.
  • Development of a DLM using 500 Hz raw ECG data from 12-lead, 6-lead, and single-lead ECGs.
  • Performance evaluation using the area under the receiver operating characteristic curve (AUC) on internal and external validation sets.

Main Results:

  • The DLM achieved an AUC of 0.926 for internal validation and 0.883 for external validation using 12-lead ECGs.
  • DLMs using six and single-lead ECGs demonstrated AUCs ranging from 0.847 to 0.906.
  • The model showed robust performance in detecting overt hyperthyroidism across different validation sets.

Conclusions:

  • A DLM utilizing ECG data offers a promising non-invasive method for screening overt hyperthyroidism.
  • This AI-driven approach can facilitate earlier disease diagnosis.
  • The developed DLM has the potential to improve patient prognosis through timely intervention.