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

You might also read

Related Articles

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

Sort by

Same author

A continuous network physiology analysis of brain-heart interactions in epileptic seizures.

Scientific reports·2026

Same author

Toward future diagnostics of Parkinson's disease: a perspective on multimodal motor assessment and personalized digital twins.

Frontiers in aging neuroscience·2026

Same author

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

Computers in biology and medicine·2026

Same author

Translational perspectives on brain-heart interplay: From methodologies to clinical applications.

Computers in biology and medicine·2026

Same author

Collection of Ambulatory Electrocardiogram and Behavioral Data for the Identification of Digital Biomarkers for Heart Failure (CATCH-ECG): Protocol for a Prospective Cohort Study.

JMIR research protocols·2025

Same author

Leveraging Self-Supervised Learning Methods for Remote Screening of Subjects with Paroxysmal Atrial Fibrillation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Search research articles

Related Experiment Video

Updated: Jul 12, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

Extraction of fetal electrocardiogram using H(infinity) adaptive algorithms.

Sadasivan Puthusserypady¹

¹Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576. elespk@nus.edu.sg

Medical & Biological Engineering & Computing

|August 22, 2007

Summary

This summary is machine-generated.

Two new adaptive algorithms based on H(infinity) principles effectively extract fetal electrocardiogram (fECG) signals from maternal abdominal recordings, outperforming traditional methods for improved fetal health monitoring.

More Related Videos

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Related Experiment Videos

Last Updated: Jul 12, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Area of Science:

Biomedical Engineering
Signal Processing
Maternal-Fetal Medicine

Background:

Noninvasive fetal electrocardiogram (fECG) monitoring is crucial for assessing fetal well-being.
Abdominal fECG signals are heavily obscured by maternal electrocardiogram (mECG) interference.
Robust signal extraction techniques are needed to overcome noise and model uncertainties.

Purpose of the Study:

To propose two novel adaptive algorithms for extracting fECG from trans-abdominal recordings.
To leverage H(infinity) principles for enhanced robustness against noise and model uncertainties.
To compare the performance of the proposed algorithms against the least-mean-square (LMS) algorithm.

Main Methods:

Development of two adaptive algorithms grounded in H(infinity) control theory.
Application of algorithms to both simulated and real multichannel electrocardiogram (ECG) data.
Comparative analysis of proposed H(infinity) algorithms versus the standard LMS algorithm.

Main Results:

The proposed H(infinity) based algorithms demonstrated superior performance in fECG extraction.
Effective suppression of maternal ECG interference was achieved.
Algorithms showed robustness in the presence of signal uncertainties and noise.

Conclusions:

H(infinity) adaptive algorithms offer a significant advancement for noninvasive fECG signal extraction.
These methods provide a more reliable approach to fetal health assessment compared to LMS.
The findings support the clinical utility of H(infinity) techniques in obstetric monitoring.