Evidence map›Paper›PMID 26286628›Full record

ArticleScientific reports2015

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization.

Hung-Chih Chiu, Yen-Hung Lin, Men-Tzung Lo, Sung-Chun Tang, Tzung-Dau Wang, Hung-Chun Lu, Yi-Lwun Ho, Hsi-Pin Ma, Chung-Kang Peng

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In one paragraph

Article in Scientific reports, 2015. 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. Trial
  2. Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022
    Article
  3. Article
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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

9 authors.

Hung-Chih ChiuDepartment of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan.
Yen-Hung LinDepartment of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
Men-Tzung Lo1] Institute of Translational and Interdisciplinary Medicine and Department of Biomedical Sciences and Engineering, National Central University, Chungli, Taiwan [2] Research Center for Adaptive Data Analysis, National Central University, Taoyuan, Taiwan.
Sung-Chun TangDepartment of Neurology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
Tzung-Dau WangDepartment of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
Hung-Chun LuDepartment of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
Yi-Lwun HoDepartment of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
Hsi-Pin MaDepartment of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan.
Chung-Kang Peng1] Research Center for Adaptive Data Analysis, National Central University, Taoyuan, Taiwan [2] Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.

Indexed as

Signal Processing, Computer-AssistedAlpha RhythmCardiac CatheterizationDemographyElectrocardiographyElectroencephalographyFemaleHeartHumansMaleMiddle AgedMyocardial RevascularizationNeurotransmitter AgentsRegression AnalysisStress, PsychologicalNeurotransmitter Agents

Identifiers

PMID26286628
PMCPMC4541158

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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.