Evidence map›Paper›PMID 35704121›Full record

ArticleApplied psychophysiology and biofeedback2022

Dynamic Phase Extraction: Applications in Pulse Rate Variability.

Christopher H Li, Franklin S Ly, Kegan Woodhouse, John Chen, Zhuowei Cheng, Tyler Santander, Nirmit Ashar, Elyes Turki, Henry T Yang, Michael Miller and 2 more

Abstract read
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In one paragraph

Article in Applied psychophysiology and biofeedback, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 54% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Christopher H LiDepartment of Physics, University of California, Santa Barbara, Santa Barbara, USA. christopher02px2017@gmail.com.ORCID 0000-0003-4758-9839
Franklin S LyDepartment of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, USA.
Kegan WoodhouseDepartment of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, USA.
John ChenDepartment of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, USA.
Zhuowei ChengDepartment of Computer Science, University of California, Santa Barbara, Santa Barbara, USA.
Tyler SantanderDepartment of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, USA.
Nirmit AsharDepartment of Computer Science, University of California, Santa Barbara, Santa Barbara, USA.
Elyes TurkiDepartment of Physics, University of California, Santa Barbara, Santa Barbara, USA.
Henry T YangDepartment of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, USA.
Michael MillerDepartment of Psychological & Brain Sciences, University of California, Santa Barbara, Santa Barbara, USA.
Linda PetzoldDepartment of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, USA.
Paul K HansmaDepartment of Physics, University of California, Santa Barbara, Santa Barbara, USA.
University of California, Santa Barbara · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulse rate variability is a physiological parameter that has been extensively studied and correlated with many physical ailments. However, the phase relationship between inter-beat interval, IBI, and breathing has very rarely been studied. Develop a technique by which the phase relationship between IBI and breathing can be accurately and efficiently extracted from photoplethysmography (PPG) data. A program based on Lock-in Amplifier technology was written in Python to implement a novel technique, Dynamic Phase Extraction. It was tested using a breath pacer and a PPG sensor on 6 subjects who followed a breath pacer at varied breathing rates. The data were then analyzed using both traditional methods and the novel technique (Dynamic Phase Extraction) utilizing a breath pacer. Pulse data was extracted using a PPG sensor. Dynamic Phase Extraction (DPE) gave the magnitudes of the variation in IBI associated with breathing [Formula: see text] measured with photoplethysmography during paced breathing (with premature ventricular contractions, abnormal arrhythmias, and other artifacts edited out). [Formula: see text] correlated well with two standard measures of pulse rate variability: the Standard Deviation of the inter-beat interval (SDNN) (ρ = 0.911) and with the integrated value of the Power Spectral Density between 0.04 and 0.15 Hz (Low Frequency Power or LF Power) (ρ = 0.885). These correlations were comparable to the correlation between the SDNN and the LF Power (ρ = 0.877). In addition to the magnitude [Formula: see text], Dynamic Phase Extraction also gave the phase between the breath pacer and the changes in the inter-beat interval (IBI) due to respiratory sinus arrythmia (RSA), and correlated well with the phase extracted using a Fourier transform (ρ = 0.857). Dynamic Phase Extraction can extract both the phase between the breath pacer and the changes in IBI due to the respiratory sinus arrhythmia component of pulse rate variability ([Formula: see text], but is limited by needing a breath pacer.

Indexed as

Respiratory Sinus ArrhythmiaSignal Processing, Computer-AssistedElectrocardiographyHeart RateHumansPhotoplethysmographyRespiratory RateLock-in amplifierPhotoplethysmographyPulse rate variabilityRespiratory sinus arrhythmia

Identifiers

PMID35704121
OpenAlexW4282925827

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.