Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.3K
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...
2.3K
Pulse rhythm01:30

Pulse rhythm

785
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...
785
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

830
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
830
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

563
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...
563

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A caveat regarding the unfolding argument: implications of plasticity.

Neuroscience of consciousness·2026
Same author

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset.

bioRxiv : the preprint server for biology·2026
Same author

Co-occurring clonal hematopoiesis exhibits strong selection and high leukemia risk.

Nature communications·2026
Same author

Rare coding variant architecture and gene discovery from 130,000 sequenced cases of atrial fibrillation.

Research square·2026
Same author

Artificial Intelligence-Enhanced Electrocardiography and Health Records to Predict Cardiac Arrest.

JACC. Advances·2026
Same author

Statistical inference for high-dimensional generalized estimating equations.

Biostatistics (Oxford, England)·2026

Related Experiment Video

Updated: Jun 24, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

8.6K

Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.

Jessica Perry1, Jennifer A Brody2, Christine Fong3

  • 1Department of Biostatistics (J.P., N.S., A.S.), University of Washington, Seattle.

Circulation
|June 11, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models using electronic health records can identify out-of-hospital cardiac arrest (OHCA) risk in the general population. These models uncover diverse risk factors beyond traditional cardiovascular indicators for OHCA prediction.

Keywords:
death, sudden, cardiacheart arrestmachine learning

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
Technical Refinement of a Bilateral Renal Ischemia-Reperfusion Mouse Model for Acute Kidney Injury Research
03:13

Technical Refinement of a Bilateral Renal Ischemia-Reperfusion Mouse Model for Acute Kidney Injury Research

Published on: November 3, 2023

2.2K

Related Experiment Videos

Last Updated: Jun 24, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

8.6K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
Technical Refinement of a Bilateral Renal Ischemia-Reperfusion Mouse Model for Acute Kidney Injury Research
03:13

Technical Refinement of a Bilateral Renal Ischemia-Reperfusion Mouse Model for Acute Kidney Injury Research

Published on: November 3, 2023

2.2K

Area of Science:

  • Cardiology
  • Public Health
  • Health Informatics

Background:

  • Out-of-hospital cardiac arrests (OHCAs) predominantly affect the general population without established risk identification strategies.
  • Electronic health record (EHR) data offers a potential avenue for identifying OHCA risk factors.

Purpose of the Study:

  • To assess the utility of EHR data for identifying OHCA in the general population.
  • To define key factors contributing to OHCA risk using machine learning models.

Main Methods:

  • A population-based case-control study involving 2366 individuals with OHCA and 23,660 matched controls.
  • Abstraction of comorbidities, electrocardiographic measures, vital signs, and medications from EHR data.
  • Development and validation of machine learning models to predict OHCA, assessing performance metrics like AUC and PPV.

Main Results:

  • Machine learning models demonstrated superior discrimination (AUC 0.80-0.85) compared to conventional risk factor models (AUC 0.66).
  • Positive predictive value for OHCA risk ranged from 2.5% to 3.1% with machine learning models at 99% specificity.
  • Identified predictors included longer corrected QT interval, substance abuse, fluid/electrolyte disorders, alcohol abuse, and higher heart rate, alongside traditional cardiovascular factors and demographic characteristics.

Conclusions:

  • Machine learning models utilizing EHR data show promise for OHCA risk prediction in the general population.
  • OHCA risk is influenced by a complex interplay of cardiovascular, noncardiovascular, and demographic factors.
  • Future public health strategies for OHCA prediction and prevention must integrate this multifaceted risk profile.