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 amplitude and quality

Pulse amplitude and quality

Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

Discrete-Time Fourier Series

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...

Qualitative Analysis

Qualitative Analysis

For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
For instance, group IV...

Qualitative Analysis

Qualitative Analysis

Qualitative analysis is the process of identifying elements, ions, or compounds in an unknown sample. It is the first and most fundamental type of analysis based on the hierarchy of analytical goals. This hierarchy is significant as it provides a structured approach to scientific research, with qualitative analysis serving as the initial step, providing essential information before moving on to quantitative or other forms of analysis.
There are two main approaches to qualitative analysis:...

You might also read

Related Articles

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

Sort by

Same author

Calcium/calmodulin-dependent protein kinase II links ER stress with Fas and mitochondrial apoptosis pathways.

The Journal of clinical investigation·2009

Same author

Cripto-1 overexpression is involved in the tumorigenesis of nasopharyngeal carcinoma.

BMC cancer·2009

Same author

Range of motion and orientation of the lumbar facet joints in vivo.

Spine·2009

Same author

[Silencing of COX-2 in nasopharyngeal carcinoma cells with a shRNAmir lentivirus vector].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University·2009

Same author

The risk of melamine-induced nephrolithiasis in young children starts at a lower intake level than recommended by the WHO.

Pediatric nephrology (Berlin, Germany)·2009

Same author

Adult scoliosis in patients over sixty-five years of age: outcomes of operative versus nonoperative treatment at a minimum two-year follow-up.

Spine·2009

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

[The quality evaluation of dynamic spectrum data].

Gang Li¹, Hui-quan Wang, Zhe Zhao

¹Tianjin Key Laboratory of Biomedical Detecting Technique & Instruments, Tianjin University, Tianjin 300072, China. ligang59@tju.edu.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu

|December 9, 2010

Summary

This summary is machine-generated.

A new quality evaluation index, the number of stable wavelengths, enhances non-invasive blood component measurement accuracy. This method improves prediction stability and precision for clinical applications.

More Related Videos

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Jun 6, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Published on: January 5, 2024

Area of Science:

Biomedical Engineering
Spectroscopy
Data Science

Background:

Non-invasive blood component measurement using dynamic spectrum methods faces challenges in precision due to various influencing factors.
Establishing a robust quality evaluation criterion for dynamic spectrum data is crucial for improving model stability and prediction accuracy.

Purpose of the Study:

To develop and validate a quality evaluation index for dynamic spectrum data to enhance non-invasive blood component measurement.
To improve the precision and stability of predictive models for blood analytes.

Main Methods:

Analysis of 110 in vivo dynamic spectrum samples, with 60 identified as satisfactory.
Development of a quality evaluation index based on the number of stable wavelengths.
Utilizing a Backpropagation (BP) artificial neural network to build a calibration model for total cholesterol, glucose, and hemoglobin against dynamic spectrum data.

Main Results:

The number of stable wavelengths was proposed as a quality evaluation index for dynamic spectrum data.
The BP artificial neural network model demonstrated improved prediction results compared to the control group.
Average relative errors for total cholesterol, glucose, and hemoglobin decreased significantly from 13.8%, 15.8%, and 5.4% to 6.5%, 6.5%, and 2.1%, respectively.

Conclusions:

The number of stable wavelengths is an effective quality evaluation index for dynamic spectrum data.
Evaluating dynamic spectrum data quality leads to more reliable predictions for blood components.
This approach accelerates the potential clinical application of non-invasive blood component monitoring.