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

You might also read

Related Articles

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

Sort by
Same author

Cumulative exposure and longitudinal exposure pattern of C-reactive protein-triglyceride-glucose index combined with Chinese visceral adiposity index (CTI-CVAI) and the risk of new-onset cardiovascular disease in middle-aged and older Chinese adults: a prospective cohort study based on the China Health and Retirement Longitudinal Survey (CHARLS).

Cardiovascular diabetology·2026
Same author

From nonlinear neuronal dynamics to AI-optimized VLSI hardware: multiplier-free FPGA implementation of memristive FN-HR coupled neural networks for intelligent systems.

Cognitive neurodynamics·2026
Same author

StrokeDiffNet: quantifying DWI-FLAIR mismatch via a common feature space for time since stroke classification.

Medical & biological engineering & computing·2026
Same author

Schiff-Base Hybrid Porous Polymers as Additive-Free Photocatalysts for Efficient Photodegradation of Dyes and Antibiotics under Visible Light.

ACS applied materials & interfaces·2026
Same author

Associations of cumulative exposure and dynamic trajectories of the triglyceride-total cholesterol-body weight index with new-onset cardiometabolic multimorbidity in middle-aged and older Chinese adults: evidence from a nationwide prospective cohort study.

Cardiovascular diabetology·2026
Same author

HBP-net for robust remote heart rate estimation using heartbeat probability.

iScience·2026

Related Experiment Video

Updated: May 8, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

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

Published on: April 26, 2024

An approach to machine learning-based non-invasive hemoglobin estimation using multi-wavelength PPG signal features.

Bukao Ni1, Chaochao Wang2, Yanhong Yang3

  • 1Department of Critical Care Medicine, Wenzhou Central Hospital, Affiliated to Wenzhou Medical University, Wenzhou, Zhejiang, China.

Frontiers in Physiology
|May 7, 2026
PubMed
Summary

Non-invasive hemoglobin (Hb) measurement using photoplethysmography (PPG) signals and machine learning shows promise. This method accurately predicts Hb levels, offering a less invasive alternative to traditional blood tests.

Keywords:
feature extractionhemoglobinneural networksnon-invasive measurementphotoplethysmographyred blood cells

More Related Videos

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care
14:28

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care

Published on: May 10, 2024

A Rapid and Chemical-free Hemoglobin Assay with Photothermal Angular Light Scattering
05:18

A Rapid and Chemical-free Hemoglobin Assay with Photothermal Angular Light Scattering

Published on: December 7, 2016

Related Experiment Videos

Last Updated: May 8, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

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

Published on: April 26, 2024

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care
14:28

Non-Invasive Monitoring of Microvascular Oxygenation and Reactive Hyperemia using Hybrid, Near-Infrared Diffuse Optical Spectroscopy for Critical Care

Published on: May 10, 2024

A Rapid and Chemical-free Hemoglobin Assay with Photothermal Angular Light Scattering
05:18

A Rapid and Chemical-free Hemoglobin Assay with Photothermal Angular Light Scattering

Published on: December 7, 2016

Area of Science:

  • Biomedical Engineering
  • Medical Diagnostics
  • Machine Learning in Healthcare

Background:

  • Traditional hemoglobin (Hb) measurement involves invasive blood sampling, causing patient discomfort.
  • Non-invasive methods for Hb monitoring are in demand to improve patient experience and streamline clinical workflows.
  • Photoplethysmography (PPG) offers a potential non-invasive approach using light signals.

Purpose of the Study:

  • To explore the efficacy of photoplethysmography (PPG) signals for non-invasive hemoglobin (Hb) measurement.
  • To develop and validate a machine learning model for predicting Hb levels from PPG data.
  • To provide a patient-friendly alternative to traditional blood-based Hb testing.

Main Methods:

  • Collected raw PPG data (red and infrared light signals) from 68 subjects.
  • Extracted statistical features (mean, kurtosis, skewness) from PPG signals.
  • Utilized a Multilayer Perceptron (MLP) neural network with extracted features and demographic data for Hb prediction.

Main Results:

  • The MLP neural network achieved a Mean Relative Error (MRE) of less than 2.46% in predicting Hb levels.
  • The study demonstrates the feasibility of using PPG-derived features for accurate Hb estimation.
  • Feature extraction and machine learning effectively processed PPG data for clinical application.

Conclusions:

  • PPG signals, combined with feature extraction and artificial neural networks, provide a viable non-invasive method for Hb measurement.
  • This approach has significant implications for clinical diagnostics, offering a more comfortable and efficient monitoring solution.
  • The study highlights the potential of machine learning in advancing accessible and patient-centric healthcare technologies.