Cited by 46Emotion and Mood RecognitionHeart Rate Variability and Autonomic ControlNon-Invasive Vital Sign Monitoring

Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged poincare plots

Goshvarpour, Atefeh, Abbasi, Ataollah, Goshvarpour, Ateke·2017·Australasian Physical & Engineering Sciences in Medicine

Abstract

Designing an efficient automatic emotion recognition system based on physiological signals has attracted great interests within the research of human-machine interactions. This study was aimed to classify emotional responses by means of a simple dynamic signal processing technique and fusion frameworks. The electrocardiogram and finger pulse activity of 35 participants were recorded during rest condition and when subjects were listening to music intended to stimulate certain emotions. Four emotion categories, including happiness, sadness, peacefulness, and fear were chosen. Estimating heart rate variability (HRV) and pulse rate variability (PRV), 4 Poincare indices in 10 lags were extracted. The support vector machine classifier was used for emotion classification. Both feature level (FL) and decision level (DL) fusion schemes were examined. Significant differences have been observed between lag 1 Poincare plot indices and the other lagged measures. The mean accuracies of 84.1, 82.9, 79.68, and 76.05% were obtained for PRV, DL, FL, and HRV measures, respectively. However, DL outperformed others in discriminating sadness and peacefulness, using SD 1 and total features, correspondingly. In both cases, the classification rates improved up to 92% (with the sensitivity of 95% and specificity of 83.33%). Totally, DL resulted in better performances compared to FL. In addition, the impact of the fusion rules on the classification performances has been confirmed.

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Crossref · 10.1007/s13246-017-0571-1

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  • Goshvarpour et al. found that using HRV and PRV with feature-level and decision-level fusion rules improved classification rates up to 92% (sensitivity 95%, specificity 83.33%).

    “Research by Goshvarpour et al. using HRV and PRV, and fusion rules (feature level, decision level) showed classification rates improved up to 92% (sensitivity: 95%, specificity: 83.33%) [63].”

    From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [63]

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Authors
Goshvarpour, A., Abbasi, A., & Goshvarpour, A.
Affiliations
Sahand University of Technology (IR)
Corresponding author
Abbasi, Ataollah
Venue
Australasian Physical & Engineering Sciences in Medicine
Published
2017-07-17
Volume
40
Issue
3
Pages
617-629
ISSN
  • 0158-9938 (ISSN-L)
  • 1879-5447
OpenAlex
W2735842846
Cited by
46
References
52
Citations by year
2025: 2 · 2024: 3 · 2023: 5 · 2022: 6 · 2021: 10 · 2020: 8 · 2019: 7 · 2018: 5
FWCI
7.12
Type
journal article
Language
en
Open access
closed
Locations
  • Australasian Physical & Engineering Sciences in Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Keywords
emotion recognition, heart rate variability, pulse rate variability, Poincaré plot, feature-level fusion, decision fusion, electrocardiogram, physiological signals, emotional response to music, support vector machine
MeSH terms
Algorithms, Emotions, Female, Heart Rate, Humans, Male, Pulse, Young Adult

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