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

What is a Frequency Distribution00:51

What is a Frequency Distribution

A frequency is the number of times a value of the data occurs. The sum of all the frequency values represents the total number of students included in the sample. It is commonly used to group data of quantitative types. Frequency distributions can be displayed in a table, histogram, line graph, dot plot, or pie chart, just to name a few. A histogram is a graphical representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to...
Downsampling01:20

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...
Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Relative Frequency Distribution00:55

Relative Frequency Distribution

A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
Construction of Frequency Distribution01:15

Construction of Frequency Distribution

A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is best to...

You might also read

Related Articles

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

Sort by
Same author

Reply to: Is the role of eosinophils in refractory chronic cough truly dismissed? Mepolizumab reduced blood and sputum eosinophils, but not eosinophil-derived neurotoxin.

The European respiratory journal·2026
Same author

Characterizing Cough and Its Response to Therapy in Hypersensitivity Pneumonitis.

Chest·2026
Same author

Ventilator Control Variables: Pressure Control, Volume Control, and Adaptive Targeting of Pressure Control.

NeoReviews·2026
Same author

Assessing the reliability of upper airway sampling for microbiological surveillance in cystic fibrosis.

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society·2025
Same author

Cough sound spectro-temporal analysis and automated detection using Vision Transformers.

Digital health·2025
Same author

Mepolizumab for the treatment of refractory chronic cough in patients with eosinophilic airways disease (MUCOSA): a randomised, double-blind, parallel-group, placebo-controlled trial.

The European respiratory journal·2025

Related Experiment Video

Updated: May 16, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Data reduction for cough studies using distribution of audio frequency content.

Antony Barton1, Patrick Gaydecki, Kimberley Holt

  • 1Respiratory Research Group, Manchester Academic Health Sciences Centre, University of Manchester, University Hospital of South Manchester, ERC Building, Second floor, Manchester, M23 9LT, United Kingdom. jacky.smith@manchester.ac.uk.

Cough (London, England)
|December 13, 2012
PubMed
Summary

An algorithm significantly reduces audio recording lengths for cough analysis by removing inactive periods. This method preserves cough data, making manual cough counting more efficient and accurate for research and treatment assessment.

More Related Videos

Precision Induction and Distinction of Coughing and Sneezing Reflexes in Mice
09:30

Precision Induction and Distinction of Coughing and Sneezing Reflexes in Mice

Published on: October 3, 2025

Related Experiment Videos

Last Updated: May 16, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Precision Induction and Distinction of Coughing and Sneezing Reflexes in Mice
09:30

Precision Induction and Distinction of Coughing and Sneezing Reflexes in Mice

Published on: October 3, 2025

Area of Science:

  • Medical acoustics
  • Respiratory medicine
  • Digital signal processing

Background:

  • Objective quantification of coughing via audio recordings is a developing field for understanding cough and treatment efficacy.
  • Manual cough counting is accurate but labor-intensive, necessitating methods to shorten audio records before analysis.
  • Reducing record length while preserving coughs is crucial for efficient clinical research.

Purpose of the Study:

  • To evaluate an algorithm designed to reduce audio recording lengths for cough analysis.
  • To determine if the algorithm can shorten recordings without compromising cough quantification accuracy.

Main Methods:

  • Twenty subjects (healthy, chronic cough, COPD, asthma) underwent 24-hour ambulatory audio recording.
  • Recordings were segmented into 15-minute intervals for analysis.
  • An algorithm utilizing median audio frequency and power removed inactive segments, followed by re-counting coughs.

Main Results:

  • The algorithm substantially reduced median recording length to 62.4 minutes.
  • Minimal erroneous removal of coughs occurred (median 0.0 coughs/h).
  • Variability in cough counts was comparable to manual counting, with a maximum 1.0% coughs/h missed in asthmatics.

Conclusions:

  • A system using median audio frequency effectively reduces recording lengths.
  • This method significantly shortens data without compromising the integrity of cough quantification.
  • The algorithm offers a practical solution for efficient cough analysis in clinical studies.