RespirAnalyzer: an R package for analyzing data from continuous monitoring of respiratory signals

Teng Zhang, Xinzheng Dong, Dandan Wang, Chen Huang, Xiaohua Douglas Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: The analysis of data obtained from continuous monitoring of respiratory signals (CMRS) holds significant importance in improving patient care, optimizing sports performance, and advancing scientific understanding in the field of respiratory health. Results: The R package RespirAnalyzer provides an analytic tool specifically for feature extraction, fractal and complexity analysis for CMRS data. The package covers a wide and comprehensive range of data analysis methods including obtaining inter-breath intervals (IBI) series, plotting time series, obtaining summary statistics of IBI series, conducting power spectral density, multifractal detrended fluctuation analysis (MFDFA) and multiscale sample entropy analysis, fitting the MFDFA results with the extended binomial multifractal model, displaying results using various plots, etc. This package has been developed from our work in directly analyzing CMRS data and is anticipated to assist fellow researchers in computing the related features of their CMRS data, enabling them to delve into the clinical significance inherent in these features.

Original languageEnglish
Article numbervbae003
JournalBioinformatics Advances
Volume4
Issue number1
DOIs
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Funding

This work was supported by the National Institutes of Health [UL1TR001998, 1U01DK135111, OT2HL161847 to X.D.Z.]; and the DRC at Washington University [P30 DK020579 to X.D.Z.]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

FundersFunder number
National Institutes of Health (NIH)UL1TR001998, OT2HL161847, 1U01DK135111
National Institutes of Health (NIH)
The George Washington UniversityP30 DK020579
The George Washington University
Diabetes Research Connection

    ASJC Scopus subject areas

    • Structural Biology
    • Molecular Biology
    • Genetics
    • Computer Science Applications

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