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

210
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...
210
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

115
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...
115
Electrocardiogram01:29

Electrocardiogram

3.1K
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...
3.1K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

843
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...
843
Exercise Stress Test01:26

Exercise Stress Test

450
Introduction
Exercise stress testing, commonly known as a treadmill test, is a noninvasive procedure used to evaluate cardiovascular function and diagnose heart conditions.
Definition
An exercise stress test measures the heart's response to exertion using a treadmill or stationary bicycle. Chest electrodes record the heart's electrical activity through an ECG, and blood pressure is monitored regularly.
Purposes
450
Pulse rhythm01:30

Pulse rhythm

910
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...
910

You might also read

Related Articles

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

Sort by
Same author

Giant Sinus of Valsalva Aneurysm: A Clinical Case and Literature Review.

Journal of clinical medicine·2026
Same author

Echocardiographically occult nonbacterial thrombotic endocarditis with tumor cell-containing valvular vegetations in metastatic breast cancer.

Cardio-oncology (London, England)·2026
Same author

Cross-Disease Breathomics by PTR-TOF-MS: Multiclass Machine Learning and Network Remodeling Across Asthma, COPD, Cystic Fibrosis, and Lymphangioleiomyomatosis.

International journal of molecular sciences·2026
Same author

New Risk Factor-Weighted Clinical Likelihood (RF-CL) in Diagnosis of Chronic Coronary Syndrome in Men and Women: Our First Impressions About the Differences and Opportunities.

Journal of clinical medicine·2026
Same author

Sex differences in baseline characteristics, stroke care, and outcomes across socioeconomic settings: a multicentre study from Switzerland, Poland, and Ukraine.

Journal of neurology·2026
Same author

A Screening Method for Determining Left Ventricular Systolic Function Based on Spectral Analysis of a Single-Channel Electrocardiogram Using Machine Learning Algorithms.

Diagnostics (Basel, Switzerland)·2026

Related Experiment Video

Updated: Aug 30, 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

8.7K

Left Ventricular Diastolic Dysfunction Screening by a Smartphone-Case Based on Single Lead ECG.

Natalia Kuznetsova1,2, Anastasiia Gubina2, Zhanna Sagirova2

  • 1World-Class Research Center "Digital Biodesign and Personalized Healthcare" Sechenov First Moscow State Medical University, Moscow, Russia.

Clinical Medicine Insights. Cardiology
|September 1, 2022
PubMed
Summary

Smartphone ECGs can screen for left ventricular diastolic dysfunction (LVDD) using advanced signal processing. This novel method shows high accuracy and repeatability for LVDD detection.

Keywords:
ECGartificial intelligenceleft ventricular diastolic dysfunctionmachine learningsignal processingspectral analysis

More Related Videos

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

11.7K
Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation
09:05

Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation

Published on: October 20, 2016

19.7K

Related Experiment Videos

Last Updated: Aug 30, 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

8.7K
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

11.7K
Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation
09:05

Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation

Published on: October 20, 2016

19.7K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Left ventricular diastolic dysfunction (LVDD) is a significant cardiovascular condition.
  • Early screening and diagnosis of LVDD are crucial for timely intervention.
  • Current diagnostic methods can be resource-intensive.

Purpose of the Study:

  • To evaluate the efficacy of a smartphone-case based single-lead electrocardiogram (ECG) as a screening tool for LVDD.
  • To explore the application of advanced signal processing and artificial intelligence (AI) on ECG signals for LVDD detection.

Main Methods:

  • Utilized a cohort of 446 subjects for learning and 259 patients for testing.
  • Employed 2D-echocardiography, tissue Doppler imaging, and a smartphone-case based single-lead ECG.
  • Applied spectral analysis of ECG signals (spECG) with advanced signal processing and AI, analyzing parameters like QTc interval, Tpeak, T value, and QRSfi.

Main Results:

  • Individual ECG parameters showed moderate sensitivity and specificity for LVDD detection.
  • A combination of four parameters (QTc, Tpeak, T value, QRSfi) improved sensitivity to 86% and specificity to 70%.
  • Algorithm approbation demonstrated high performance: 95.6% sensitivity, 97.7% specificity, 96.5% accuracy, and 98.8% repeatability.

Conclusions:

  • Smartphone-case based single-lead ECG, when combined with spECG, advanced signal processing, and machine learning, shows significant potential as a novel screening tool for LVDD.
  • This approach offers a promising, accessible method for LVDD screening.