Evidence map›Paper›PMID 37237622›Full record

ArticleBioengineering (Basel, Switzerland)2023

A New Algorithm for Estimating a Noiseless, Evenly Sampled, Heart Rate Modulating Signal.

Enrico M Staderini, Harish Kambampati, Amith K Ramakrishnaiah, Stefano Mugnaini, Andrea Magrini, Sandro Gentili

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Enrico M StaderiniHealthy World Association Switzerland, 1400 Yverdon-les-Bains, Switzerland.ORCID 0000-0002-4770-3040
Harish KambampatiDoctorate School in Industrial Engineering, "Tor Vergata" University of Rome, 00133 Roma, Italy.ORCID 0000-0003-2162-9548
Amith K RamakrishnaiahDepartment of Electronics Engineering, "Tor Vergata" University of Rome, 00133 Roma, Italy.
Stefano MugnainiOccupational Medicine Section, Department of Biomedicine and Prevention, "Tor Vergata" University of Rome, 00133 Roma, Italy.
Andrea MagriniOccupational Medicine Section, Department of Biomedicine and Prevention, "Tor Vergata" University of Rome, 00133 Roma, Italy.
Sandro GentiliOccupational Medicine Section, Department of Biomedicine and Prevention, "Tor Vergata" University of Rome, 00133 Roma, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart rate variability (HRV) is commonly intended as the variation in the heart rate (HR), and it is evaluated in the time and frequency domains with various well-known methods. In the present paper, the heart rate is considered as a time domain signal, at first as an abstract model in which the HR is the instantaneous frequency of an otherwise periodic signal, such as with an electrocardiogram (ECG). In this model, the ECG is assumed to be a frequency modulated signal, or carrier signal, where HRV or HRV(t) is the time-domain signal which is frequency modulating the carrier ECG signal around its average frequency. Hence, an algorithm able to frequency demodulate the ECG signal to extract the signal HRV(t) is described, with possibly enough time resolution to analyse fast time-domain variations in the instantaneous HR. After exhaustive testing of the method on simulated frequency modulated sinusoidal signals, the new procedure is eventually applied on actual ECG tracings for preliminary nonclinical testing. The purpose of the work is to use this algorithm as a tool and a more reliable method for the assessment of heart rate before any further clinical or physiological analysis.

Indexed as

heart rate variabilitysignal analysis

Identifiers

PMID37237622
PMCPMC10215776

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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.