Evidence map›Paper›PMID 34484651›Full record

ArticleJournal of healthcare engineering2021

Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning.

Nilima Salankar, Deepika Koundal, Saeed Mian Qaisar

Abstract read
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. Article
  6. Article
  7. Complex Brain-Heart Mapping in Mental and Physical Stress.IEEE journal of translational engineering in health and medicine · 2023
    Article
  8. 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

3 authors.

Nilima SalankarDepartment of Virtualization, School of Computer Science, University of Petroleum & Energy Studies (UPES), Dehradun, India.ORCID 0000-0001-6349-9433
Deepika KoundalDepartment of Virtualization, School of Computer Science, University of Petroleum & Energy Studies (UPES), Dehradun, India.ORCID 0000-0003-1688-8772
Saeed Mian QaisarElectrical and Computer Engineering Department, Effat University, Jeddah 22332, Saudi Arabia.ORCID 0000-0002-4268-3482

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this pandemic situation, importance and awareness about mental health are getting more attention. Stress recognition from multimodal sensor based physiological signals such as electroencephalogram (EEG) and electrocardiography (ECG) signals is a very cost-effective way due to its noninvasive nature. A dataset, recorded during the mental arithmetic task, consisting of EEG + ECG signals of 36 participants is used. It contains two categories of performance, namely, "Good" (nonstressed) and "Bad" (stressed) (Gupta et al. 2018 and Eraldeír et al. 2018). This paper presents an effective approach for the recognition of stress marker at frontal, temporal, central, and occipital lobes. It processes the multimodality physiological signals. The variational mode decomposition (VMD) strategy is used for data preprocessing and for the decomposition of signals into various oscillatory mode functions. Poincare plots (PP) are derived from the first eight variational modes and features from these plots have been extracted such as mean, area, and central tendency measure of the elliptical region. The statistical significance of the extracted features with

Indexed as

ElectroencephalographySignal Processing, Computer-AssistedAlgorithmsHumansMachine LearningSupport Vector Machine

Identifiers

PMID34484651
PMCPMC8413068

What OpenQuestion holds

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Registered trials

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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.