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

Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

323
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...
323
Electrocardiogram01:29

Electrocardiogram

3.3K
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...
3.3K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

887
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...
887
Pulse rhythm01:30

Pulse rhythm

940
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...
940
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

123
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...
123

You might also read

Related Articles

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

Sort by
Same author

Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.

JMIR mHealth and uHealth·2026
Same author

Association Between the Use of Proton Pump Inhibitors and Osteoporosis/Fracture: Nested Case-Control Studies Using a National Health Screening Cohort.

Journal of clinical medicine·2026
Same author

Using Digital Phenotyping for Depression Screening in Community-Dwelling Older Adults: Bayesian Multilevel Hurdle Model Machine Learning Approach.

JMIR AI·2026
Same author

Korean Medical Consultation With Open-Weight Large Language Models: Pilot Comparative Evaluation of Retrieval-Augmented Generation With Metadata Filtering.

JMIR formative research·2026
Same author

Clinical and kinematic responses to neck injuries in low-speed reverse motor vehicle collision tests: a human volunteer study.

BMC musculoskeletal disorders·2026
Same author

Comparison of the Hemodynamic Effects of Epinephrine on Blood Pressure Augmentation at 1-, 3-, and 5-Minute Dosing Intervals.

Journal of the American Heart Association·2026

Related Experiment Video

Updated: Sep 21, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

969

Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and

Erdenebayar Urtnasan1,2, Jung Hun Lee3, Byungjin Moon2

  • 1Artificial Intelligence Big Data Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.

JMIR Medical Informatics
|June 3, 2022
PubMed
Summary

A novel deep learning model using a single-lead electrocardiogram (ECG) can noninvasively screen for hyperkalemia in emergency medicine. This method offers a real-time alternative to blood testing for patients with chronic kidney disease (CKD).

Keywords:
ECGdeep learningelectrocardiogramemergency medicinehyperkalemianoninvasive screeningsingle-lead ECG

More Related Videos

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

8.8K
A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

24.4K

Related Experiment Videos

Last Updated: Sep 21, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

969
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

8.8K
A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

24.4K

Area of Science:

  • Cardiology
  • Nephrology
  • Artificial Intelligence in Medicine

Background:

  • Hyperkalemia monitoring is critical for patients with chronic kidney disease (CKD) in emergency settings.
  • Current diagnosis relies on serum potassium levels via blood testing, necessitating a noninvasive, real-time alternative.
  • Emergency medicine requires efficient tools for rapid hyperkalemia assessment.

Purpose of the Study:

  • To develop a novel, noninvasive method for hyperkalemia screening.
  • To utilize a single-lead electrocardiogram (ECG) and deep learning for this purpose.
  • To provide a real-time monitoring solution in emergency departments.

Main Methods:

  • A deep learning model, comprising convolutional and pooling layers, was developed to analyze ECG signals.
  • Data from 855 patients (555 with CKD) with hyperkalemia events were used, with ECGs matched to electrolyte tests within 2 hours.
  • The model was trained and validated on 2-second ECG segments, with performance evaluated across multiple leads (I, II, V1-V6).

Main Results:

  • The noninvasive screening tool achieved high performance, with F1 scores of 100% (training), 96% (validation), and 95% (test set).
  • Lead II of the single-lead ECG demonstrated the highest performance in detecting hyperkalemia.
  • The deep learning model effectively captured cardiac rhythm complexities for serum potassium level estimation.

Conclusions:

  • A novel, noninvasive method for hyperkalemia screening using single-lead ECG and deep learning was successfully developed.
  • This tool can serve as a valuable aid in emergency medicine for hyperkalemia assessment.
  • The findings support the potential of ECG-based AI for real-time patient monitoring.