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

Sampling Distribution

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...

Bandpass Sampling

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

Maxwell-Boltzmann Distribution: Problem Solving

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by

Same author

Continuous infusion of 5-fluorouracil with versus without low-dose, consecutive administration of cisplatin in advanced colorectal cancer. A prospective randomized phase II study.

Journal of experimental & clinical cancer research : CR·2007

Same author

Genetic coadaptation of the amylase gene system in Drosophila melanogaster: evidence for the selective advantage of the lowest AMY activity and of its epistatic genetic background.

The Journal of heredity·2005

Same author

Charcot-Marie-Tooth families in Japan with MPZ Thr124Met mutation.

Journal of neurology, neurosurgery, and psychiatry·2004

Same author

Serotonin 2C receptor gene Cys23Ser polymorphism: a candidate genetic risk factor of migraine with aura in Japanese population.

Acta neurologica Scandinavica·2004

Same author

The microbial synergy of Peptostreptococcus micros and Prevotella intermedia in a murine abscess model.

Oral microbiology and immunology·2004

Same author

Genome-wide identification of NBS genes in japonica rice reveals significant expansion of divergent non-TIR NBS-LRR genes.

Molecular genetics and genomics : MGG·2004

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

High-speed Continuous-wave Stimulated Brillouin Scattering Spectrometer for Material Analysis

High-speed Continuous-wave Stimulated Brillouin Scattering Spectrometer for Material Analysis

Published on: September 22, 2017

Wigner distribution analysis of BSPM for optimal sampling.

S Usui¹, H Araki

¹Toyohashi Univ. of Technol.

IEEE Engineering in Medicine and Biology Magazine : the Quarterly Magazine of the Engineering in Medicine & Biology Society

|January 1, 1990

Summary

Body surface potential mapping (BSPM) uses ECGs to analyze body potentials. This study introduces methods to reduce leads for clinical use while preserving crucial spatiotemporal information.

More Related Videos

Mechanical Mapping of Spheroids Using Brillouin Spectroscopy

Mechanical Mapping of Spheroids Using Brillouin Spectroscopy

Published on: December 12, 2025

Related Experiment Videos

Last Updated: Jul 7, 2026

High-speed Continuous-wave Stimulated Brillouin Scattering Spectrometer for Material Analysis

High-speed Continuous-wave Stimulated Brillouin Scattering Spectrometer for Material Analysis

Published on: September 22, 2017

Mechanical Mapping of Spheroids Using Brillouin Spectroscopy

Mechanical Mapping of Spheroids Using Brillouin Spectroscopy

Published on: December 12, 2025

Area of Science:

Biomedical Engineering
Cardiovascular Electrophysiology
Signal Processing

Background:

Body surface potential mapping (BSPM) records electrocardiograms (ECGs) across the body surface.
Current BSPM methods require numerous leads, posing challenges for routine clinical application.
Maintaining spatiotemporal information is critical for effective BSPM analysis.

Purpose of the Study:

To analyze local spatial potential changes in BSPM using the Wigner distribution.
To determine optimal lead arrangements and the minimum number of leads required for BSPM.
To develop methods for reducing redundant leads and reconstructing BSPM from selected leads.

Main Methods:

Historical overview of body surface potential mapping (BSPM) and its features.
Analysis of local spatial potential changes using the Wigner distribution for space-frequency resolution.
Development of algorithms for lead reduction and BSPM reconstruction.

Main Results:

The Wigner distribution effectively analyzes spatial potential changes in BSPM.
A method for reducing redundant leads was successfully developed.
A technique for reconstructing BSPM from an optimized set of leads was established.

Conclusions:

BSPM has potential as a clinical tool with optimized lead configurations.
Lead reduction is feasible without significant loss of spatiotemporal information.
The proposed methods facilitate the clinical implementation of BSPM.