Evidence map›Paper›PMID 36433336›Full record

ArticleSensors (Basel, Switzerland)2022

Novel Generalized Low-Pass Filter with Adjustable Parameters of Exponential-Type Forgetting and Its Application to ECG Signal.

Ivo Petráš

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

1 author.

Ivo PetrášFaculty of BERG, Technical University of Košice, Němcovej 3, 042 00 Košice, Slovakia.ORCID 0000-0002-9250-6986

Funding

Army Research Office W911NF-22-1-0264Slovak Grant Agency for Science (VEGA) 1/0365/19Slovak Research and Development Agency APVV-14-0892Slovak Research and Development Agency APVV-18-0526
6 · The paper itself

Abstract

In this paper, a novel form of the Gaussian filter, the Mittag-Leffler filter is presented. This new filter uses the Mittag-Leffler function in the probability-density function. Such Mittag-Leffler distribution is used in the convolution kernel of the filter. The filter has three parameters that may adjust the curve shape due to the filter-forgetting factor. Illustrative examples present the main advantages of the proposed filter compared to classical Gaussian filtering techniques, as well as real ECG-signal denoising. Some implementation notes, along with the Matlab function, are also presented.

Indexed as

AlgorithmsAminosalicylic AcidElectrocardiographyLikelihood FunctionsNormal DistributionStaining and LabelingAminosalicylic AciddenoisingECGexponential-type forgettingGaussian filterGaussian functionMittag–Leffler filterMittag–Leffler function

Identifiers

PMID36433336
PMCPMC9694153

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.