Evidence map›Paper›PMID 22059697›Full record

ArticleBiomedical engineering online2011

Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination.

Paolo Melillo, Marcello Bracale, Leandro Pecchia

Abstract read
In one paragraph

Article in Biomedical engineering online, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed, 2 pooled it
–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

79 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  6. Assessing Stress Level Scores Against Wearables-Driven Physiological Measurements.Stress and health : journal of the International Society for the Investigation of Stress · 2025
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19 more citing papers are in PubMed but not listed here.

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.

Paolo MelilloDepartment of Biomedical, Electronic and Telecommunication Engineering, University of Naples "Federico II", Via Claudio 21, Naples, Italy. paolo.melillo@unina.it
Marcello Bracale
Leandro Pecchia

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study investigates the variations of Heart Rate Variability (HRV) due to a real-life stressor and proposes a classifier based on nonlinear features of HRV for automatic stress detection.

methods42 students volunteered to participate to the study about HRV and stress. For each student, two recordings were performed: one during an on-going university examination, assumed as a real-life stressor, and one after holidays. Nonlinear analysis of HRV was performed by using Poincaré Plot, Approximate Entropy, Correlation dimension, Detrended Fluctuation Analysis, Recurrence Plot. For statistical comparison, we adopted the Wilcoxon Signed Rank test and for development of a classifier we adopted the Linear Discriminant Analysis (LDA).

resultsAlmost all HRV features measuring heart rate complexity were significantly decreased in the stress session. LDA generated a simple classifier based on the two Poincaré Plot parameters and Approximate Entropy, which enables stress detection with a total classification accuracy, a sensitivity and a specificity rate of 90%, 86%, and 95% respectively.

conclusionsThe results of the current study suggest that nonlinear HRV analysis using short term ECG recording could be effective in automatically detecting real-life stress condition, such as a university examination.

Indexed as

Heart RateNonlinear DynamicsUniversitiesEducational MeasurementHumansStress, PsychologicalStudents

Identifiers

PMID22059697
PMCPMC3305918

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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.