Evidence map›Paper›PMID 34372252›Full record

ReviewSensors (Basel, Switzerland)2021

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review.

Muhammad Anas Hasnul, Nor Azlina Ab Aziz, Salem Alelyani, Mohamed Mohana, Azlan Abd Aziz

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 1 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

37 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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  12. Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025
    Review
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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

5 authors.

Muhammad Anas HasnulFaculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia.ORCID 0000-0002-2765-3234
Nor Azlina Ab AzizFaculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia.
Salem AlelyaniCenter for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia.ORCID 0000-0002-4571-9073
Mohamed MohanaCenter for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia.
Azlan Abd AzizFaculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia.

Funding

King Khalid University R.G.P.2/100/41Ministry of Higher Education, Malaysia FRGS/1/2019/ICT02/MMU/02/15
6 · The paper itself

Abstract

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

Indexed as

Artificial IntelligenceElectrocardiographyAlgorithmsDelivery of Health CareEmotionsHumansProspective Studiesaffective computingelectrocardiogram (ECG)emotion recognition systemhealthcare

Identifiers

PMID34372252
PMCPMC8348698

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.