Evidence map›Paper›PMID 35784185›Full record

ArticleFrontiers in neuroinformatics2022

Matlab Open Source Code: Noise-Assisted Multivariate Empirical Mode Decomposition Based Causal Decomposition for Causality Inference of Bivariate Time Series.

Yi Zhang, Guan Wang, Ziwen Li, Mingjun Xie, Branko Celler, Steven Su, Peng Xu, Dezhong Yao

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroinformatics, 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
0.5field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  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

8 authors at 3 institutions in 2 countries.

Yi ZhangSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China.
Guan WangSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China.
Ziwen LiGlasgow College, University of Electronic Science and Technology of China, Chengdu, China.
Mingjun XieSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China.
Branko CellerBiomedical Systems Laboratory, University of New South Wales, Sydney, NSW, Australia.
Steven SuCenter for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, Australia.
Peng XuKey Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Dezhong YaoKey Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
University of Electronic Science and Technology of China · CNUniversity of Technology Sydney · AUUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Causality inference has arrested much attention in academic studies. Currently, multiple methods such as Granger causality, Convergent Cross Mapping (CCM), and Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) are introduced to solve the problem. Motivated by the researchers who uploaded the open-source code for causality inference, we hereby present the Matlab code of NA-MEMD Causal Decomposition to help users implement the algorithm in multiple scenarios. The code is developed on Matlab2020 and is mainly divided into three subfunctions:

Indexed as

bivariate time seriesCausal Decompositioncausality inferenceCCMempirical mode decompositionGranger causalityNA-MEMD Causal Decomposition

Identifiers

PMID35784185
PMCPMC9243260
OpenAlexW4282958627

What OpenQuestion 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 OpenQuestion graph.