Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pulse rhythm

Pulse rhythm

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...

You might also read

Related Articles

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

Sort by

Same author

Tunable Redox Dynamics of Organic Electrochemical Transistors for High-Performance Parallel Reservoir Computing.

ACS applied materials & interfaces·2026

Same author

A hierarchical Ru nanoparticle-decorated MoS<sub>2</sub>@Ni-MOF for highly efficient overall water splitting.

Chemical communications (Cambridge, England)·2026

Same author

Insights into distinct biocarbon assembly and graphitization derived from large-scale bioresource by integrating machine learning with Raman and XRD spectroscopic profiling.

Bioresource technology·2026

Same author

Benchmark Low-Pressure Ethane Uptake for Ethylene Purification Enabled by a Metal-Organic Framework with Confined Pore Space and Abundant Binding Sites.

Inorganic chemistry·2026

Same author

Broadly applicable hydrophilic additive enhances electrochemical transistor function.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Ligand Engineering of Ultrasmall CsPbI<sub>3</sub> Quantum Dots via In Situ S<sub>N</sub>2 Substitution Enables Bright Rec. 2020 Pure-Red Perovskite LEDs with Exceptional Current Efficiency.

Advanced materials (Deerfield Beach, Fla.)·2026

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN.

Junsheng Yu¹, Xiangqing Wang¹, Xiaodong Chen²

¹School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

|April 3, 2021

Summary

This summary is machine-generated.

This study introduces a novel, automated method for detecting premature ventricular contractions (PVCs) from long-term ECGs using deep metric learning and KNN classification, achieving high accuracy without complex preprocessing.

Keywords:

deep metric learning electrocardiogram k-nearest neighbors classifier premature ventricular contraction

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Nov 10, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Cardiology
Biomedical Engineering
Artificial Intelligence in Medicine

Background:

Premature ventricular contractions (PVCs) are common irregular heartbeats indicating potential heart disease.
Long-term electrocardiograms (ECG) from wearable devices are crucial for PVC diagnosis but analysis is time-consuming.
Current methods often rely on manual feature engineering, introducing potential bias.

Purpose of the Study:

To develop a simplified, automated approach for detecting PVCs from long-term ECG data.
To leverage deep metric learning for intelligent, bias-free feature extraction.
To improve the efficiency and accuracy of PVC detection.

Main Methods:

Utilized deep metric learning for supervised feature extraction, focusing on intra-product variance and inter-product differences.
Employed a k-nearest neighbors (KNN) classifier to detect PVCs based on extracted heartbeat features.
Evaluated the method on the MIT-BIH Arrhythmia Database without complex preprocessing.

Main Results:

Achieved high diagnostic performance: 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity.
Demonstrated the reliability of deep metric learning and KNN for PVC recognition.
Confirmed the method's effectiveness without requiring complicated preprocessing steps.

Conclusions:

The proposed deep metric learning and KNN approach offers a reliable and automated solution for PVC detection from long-term ECGs.
This method overcomes limitations of manual feature engineering, reducing bias and improving efficiency.
The technique shows significant potential for clinical application in diagnosing irregular heartbeats.