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Related Concept Videos

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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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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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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Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

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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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Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements.

Hui Liu1, Dan Chen1,2, Da Chen1

  • 1Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China.

Scientific Data
|June 7, 2022
PubMed
Summary

This study introduces a large, standardized 12-lead electrocardiogram (ECG) dataset. The database addresses limitations in public ECG data, facilitating improved deep learning for cardiac diagnostics.

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Deep learning shows promise for electrocardiogram (ECG) interpretation.
  • Limited availability and non-uniform diagnostic labels in public ECG datasets hinder clinical application.
  • A semantic gap exists between current deep learning models and clinical practice for ECG analysis.

Purpose of the Study:

  • To present a large-scale, multi-label 12-lead ECG database with standardized diagnostic statements.
  • To bridge the gap between deep learning interpretation and clinical practice by providing uniform data.
  • To facilitate the development and validation of advanced ECG diagnostic algorithms.

Main Methods:

  • Compiled a dataset of 25,770 12-lead ECG records from 24,666 patients at Shandong Provincial Hospital (2019-2020).
  • Ensured all diagnostic statements comply with AHA/ACC/HRS recommendations, including 44 primary statements and 15 modifiers.
  • Included patient demographics and ECG record lengths ranging from 10 to 60 seconds.

Main Results:

  • The dataset comprises 25,770 ECG records with standardized diagnostic labels.
  • 46.04% of the records exhibit ECG abnormalities.
  • 14.45% of the records feature multiple diagnostic statements, reflecting complex cardiac conditions.

Conclusions:

  • The presented ECG database offers a valuable resource for advancing automated ECG interpretation.
  • Standardized labels and large scale enhance the utility of ECG data for deep learning models.
  • This resource can improve the accuracy and reliability of AI-driven cardiac diagnostics.