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

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

You might also read

Related Articles

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

Sort by

Same author

Voronoi tessellation as a complement or replacement for confidence ellipses in the visualization of data projection and clustering results.

PloS one·2026

Same author

Self-organizing neural network-based generative AI with embedded error inflation control enhances effective knowledge extraction from preclinical studies with reduced sample size.

Pharmacological research·2026

Same author

A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data.

Canadian journal of pain = Revue canadienne de la douleur·2026

Same author

Resolving Interpretation Challenges in Machine Learning Feature Selection With an Iterative Approach in Biomedical Pain Data.

European journal of pain (London, England)·2026

Same author

Integrating AI and Machine Learning Into Pain Research and Therapy.

European journal of pain (London, England)·2025

Same author

Sleep and Aging. A Polysomnographic Follow-Up Study, Some 40 Years Later.

Journal of sleep research·2025

Same journal

Modeling and analysis of forward and inverse kinematics for a flexible Stewart platform.

PloS one·2026

Same journal

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review.

PloS one·2026

Same journal

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

PloS one·2026

Same journal

Key targets and mechanisms by which gut microbiota-derived metabolites regulate Alzheimer's disease through the immune - inflammatory pathway: Based on network pharmacology and molecular docking.

PloS one·2026

Same journal

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications.

PloS one·2026

Same journal

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Optimal distribution-preserving downsampling of large biomedical data sets (opdisDownsampling).

Jörn Lötsch^1,2, Sebastian Malkusch¹, Alfred Ultsch³

¹Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany.

|August 5, 2021

Summary

This summary is machine-generated.

Optimized downsampling methods create data subsets that better represent entire biomedical datasets compared to standard random sampling. This improves data analysis by ensuring samples accurately reflect original data distributions.

More Related Videos

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Related Experiment Videos

Last Updated: Oct 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Area of Science:

Biomedical data science
Computational biology
Data analysis

Background:

Biomedical datasets are growing, challenging computational resources for standard analyses like clustering.
Data reduction via downsampling, often using random uniform class-proportional methods, is a common preprocessing step.

Purpose of the Study:

To optimize data downsampling for better representation of the entire dataset.
To develop a method yielding samples that more accurately reflect original data distributions than standard techniques.

Main Methods:

Developed a method involving repeated random sampling and distribution comparison against the original dataset.
Evaluated the method on artificial and real biomedical data using principal component analysis and autoencoding neural networks.

Main Results:

The proposed method significantly improved the reconstruction of original data from downsampled subsets.
Achieved better fidelity compared to standard random subsampling, with results dependent on sample size and number of cases drawn.
Demonstrated improved data representation for both principal component analysis and autoencoding neural networks.

Conclusions:

Optimal distribution-preserving class-proportional downsampling yields data subsets that better reflect the entire data structure.
The proposed method, using distributional similarity as the sole criterion, does not interfere with subsequent planned analyses.