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

Assessment of blood pressure in brachial artery(one-step method)01:15

Assessment of blood pressure in brachial artery(one-step method)

This procedural guide systematically measures blood pressure using an oscillometric digital sphygmomanometer, emphasizing accuracy, patient safety, and comfort.
Prepare for the Procedure:
Assessment of blood pressure in brachial artery(two-step method)01:23

Assessment of blood pressure in brachial artery(two-step method)

Measuring blood pressure is a fundamental skill in healthcare that aids in diagnosing and monitoring hypertension and other cardiovascular conditions. An aneroid sphygmomanometer, commonly used in clinical settings, offers a manual and precise method for blood pressure measurement. The technique for using this instrument involves specific steps that must be carefully executed to ensure accuracy. The following detailed description outlines a two-step technique for assessing blood pressure using...

You might also read

Related Articles

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

Sort by
Same author

MIRF-Net: A Multimodal Data Fusion Framework for Intrapartum Fetal Risk Assessment.

Bioengineering (Basel, Switzerland)·2026
Same author

Commentary: Clinical utility of anthropometric parameters in identifying glucose dysregulation in women with polycystic ovary syndrome.

Frontiers in endocrinology·2026
Same author

Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos.

Medical image analysis·2026
Same author

Resilient Distributed Filtering for Multitarget Systems With Coupled Measurements Under Multichannel Deception Attacks.

IEEE transactions on cybernetics·2026
Same author

Maternal-Fetal Ultrasouno Video Dataset for End-to-end Intrapartum Biometry and Multi-task Learning.

Scientific data·2026
Same author

FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation.

IEEE transactions on medical imaging·2026
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

IEEE journal of biomedical and health informatics·2026
Same journal

PGCASurv: A Prior-Guided Cross-Attention Framework for Dynamic Survival Model with Longitudinal Data.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.7K

Multi-Scale, Multi-Basis Wavelet Voting Network for Automatic Analysis of Fetal Heart Rate Signals.

Yaosheng Lu, Jiewen Liu, Jieyun Bai

    IEEE Journal of Biomedical and Health Informatics
    |December 22, 2025
    PubMed
    Summary
    This summary is machine-generated.

    WaveFHR-VNet analyzes fetal heart rate (FHR) signals using a novel wavelet-based network, improving the detection of accelerations and decelerations. This advanced deep learning model offers enhanced accuracy and reliability for intrapartum monitoring.

    More Related Videos

    Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
    09:35

    Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

    Published on: March 10, 2017

    9.6K
    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
    08:22

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

    Published on: April 26, 2024

    2.9K

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Semi-automated Optical Heartbeat Analysis of Small Hearts
    12:10

    Semi-automated Optical Heartbeat Analysis of Small Hearts

    Published on: September 16, 2009

    12.7K
    Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
    09:35

    Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

    Published on: March 10, 2017

    9.6K
    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
    08:22

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

    Published on: April 26, 2024

    2.9K

    Area of Science:

    • Medical Imaging and Signal Processing
    • Artificial Intelligence in Healthcare
    • Fetal Monitoring Technologies

    Background:

    • Accurate interpretation of fetal heart rate (FHR) recordings is crucial for intrapartum monitoring.
    • Current deep learning models often overlook the time-frequency structure of FHR signals, leading to missed detections and noise susceptibility.
    • Transient accelerations (Acc) and decelerations (Dec) are key indicators requiring precise identification against the baseline FHR.

    Purpose of the Study:

    • To introduce WaveFHR-VNet, a U-Net-style wavelet-voting network designed for analyzing FHR signals in the joint time-frequency domain.
    • To overcome the limitations of 1-D sequence analysis by incorporating multi-scale, multi-basis wavelet transforms.
    • To improve the detection accuracy of FHR events and reduce susceptibility to noise and artifacts.

    Main Methods:

    • WaveFHR-VNet utilizes a discrete wavelet transform (DWT) within each encoder block to separate low-frequency baseline trends and high-frequency Acc/Dec patterns.
    • An Interactive Coefficient Selection (ICS) module in skip connections learns attention masks to filter noise and amplify salient transients.
    • Parallel processing with five complementary wavelet bases and a voting layer enhances spectral diversity and eliminates manual tuning.

    Main Results:

    • WaveFHR-VNet achieved state-of-the-art performance on four FHR datasets, demonstrating significant improvements in Dice, IoU, and accuracy metrics compared to existing models.
    • The model showed superior performance on the LCU-DB benchmark, a widely used public dataset for FHR analysis.
    • Strong cross-dataset generalization was observed, with WaveFHR-VNet consistently outperforming all comparison models.

    Conclusions:

    • WaveFHR-VNet effectively analyzes FHR signals in the time-frequency domain, leveraging wavelet transforms for improved feature extraction.
    • The proposed network architecture and modules enhance the detection of critical FHR events while mitigating noise and artifacts.
    • WaveFHR-VNet shows promise as a reliable and accurate tool for computer-aided interpretation and intrapartum fetal monitoring.