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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

5.1K
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...
5.1K
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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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...
243
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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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm

Chin-Sheng Lin1, Chin Lin2,3,4, Wen-Hui Fang5

  • 1Division of Cardiology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.

JMIR Medical Informatics
|March 6, 2020
PubMed
Summary
This summary is machine-generated.

A new deep-learning model, ECG12Net, can detect dyskalemias (hypokalemia and hyperkalemia) using electrocardiograms (ECGs), outperforming clinicians in accuracy. This AI tool shows promise for early detection and reducing cardiac events.

Keywords:
artificial intelligenceelectrocardiogrammachine learningpotassium homeostasissudden cardiac death

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Current dyskalemia detection relies on laboratory tests.
  • Cardiac tissue is highly sensitive to potassium level changes.
  • Electrocardiography (ECG) offers potential for early dyskalemia detection.

Purpose of the Study:

  • Develop a deep-learning model, ECG12Net, for dyskalemia detection using ECGs.
  • Evaluate the performance and logic of the ECG12Net model.
  • Compare AI model performance against human clinicians.

Main Methods:

  • Utilized 66,321 ECG records from 40,180 emergency department patients.
  • Developed an 82-layer convolutional neural network (ECG12Net) to estimate serum potassium.
  • Conducted a human-machine competition with six physicians on 300 ECGs.

Main Results:

  • ECG12Net demonstrated superior performance in detecting hypokalemia (AUC 0.926) and hyperkalemia (AUC 0.958) compared to clinicians.
  • High sensitivities and specificities were achieved for both conditions in human-machine competition and test sets.
  • The model showed a mean absolute error of 0.531 for serum potassium estimation.

Conclusions:

  • A deep-learning model using 12-lead ECGs can aid prompt recognition of severe dyskalemias.
  • ECG12Net has the potential to significantly reduce cardiac events through early detection.
  • AI-powered ECG analysis represents a promising advancement in cardiovascular diagnostics.