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

Aggregates Classification01:29

Aggregates Classification

1.0K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.0K
Stratified Sampling Method01:16

Stratified Sampling Method

11.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
11.7K
Classification of Signals01:30

Classification of Signals

1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
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...
11.0K
Sampling Methods: Overview01:06

Sampling Methods: Overview

3.7K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
3.7K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.3K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Testing the biobehavioral regulation of negative emotion as a mechanism of change in transdiagnostic youth psychotherapy: study protocol for a randomized controlled trial.

Trials·2026
Same author

Predicting Wrist Posture during Occupational Tasks Using Inertial Sensors and Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2023
Same author

Enhanced Perception for Autonomous Driving Using Semantic and Geometric Data Fusion.

Sensors (Basel, Switzerland)·2022
Same author

Detecting subject-specific fatigue-related changes in lifting kinematics using a machine learning approach.

Ergonomics·2022
Same author

Machine Learning in Modeling of Mouse Behavior.

Frontiers in neuroscience·2021
Same author

Detection of COVID-19 from chest x-ray images using transfer learning.

Journal of medical imaging (Bellingham, Wash.)·2021
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: May 1, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.0K

Aggregate features in multisample classification problems.

Robert Varga, S Marie Matheson, Andrew Hamilton-Wright

    IEEE Journal of Biomedical and Health Informatics
    |April 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study improves electromyographic (EMG) data classification by using aggregate features. This novel approach enhances accuracy over traditional methods and includes a confidence measure for clinical decision support.

    More Related Videos

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    21.0K
    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.6K

    Related Experiment Videos

    Last Updated: May 1, 2026

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.0K
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    21.0K
    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.6K

    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Signal Processing

    Background:

    • Multisample data classification presents challenges in accurately analyzing complex datasets.
    • Traditional methods like majority vote may not fully capture the nuances of such data.
    • Electromyographic (EMG) data analysis requires robust classification techniques for clinical applications.

    Purpose of the Study:

    • To evaluate a novel classification method for multisample problems using aggregate features.
    • To compare the proposed method's accuracy against traditional classification techniques.
    • To introduce and measure a confidence measure for classification failure prediction.

    Main Methods:

    • Utilizing aggregate features from multisample data for per-sample classification.
    • Implementing a novel classification approach for electromyographic (EMG) data.
    • Developing and applying a confidence measure (hazard function) for classification failure.

    Main Results:

    • The proposed method demonstrated superior classification accuracy compared to traditional approaches.
    • The aggregate feature approach effectively improved classification performance for multisample problems.
    • The confidence measure provided a quantifiable assessment of per-sample classification reliability.

    Conclusions:

    • Aggregate features enhance per-sample classification accuracy for multisample data, including EMG.
    • The developed confidence measure is valuable for assessing classification reliability.
    • This method shows promise for improving clinical decision support systems.