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Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

邮发代号 80-971

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Quantitative Biology  2022, Vol. 10 Issue (3): 276-286   https://doi.org/10.15302/J-QB-021-0267
  本期目录
Attention emotion recognition via ECG signals
Aihua Mao1(), Zihui Du1, Dayu Lu2, Jie Luo3()
1. School of Computer Science & Engineering, South China University of Technology, Guangzhou 510006, China
2. School of Medicine, South China University of Technology, Guangzhou 510006, China
3. School of Fine Art and Artistic Design, Guangzhou University, Guangzhou 510006, China
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Abstract：

Background: Physiological signal-based research has been a hot topic in affective computing. Previous works mainly focus on some strong, short-lived emotions (e.g., joy, anger), while the attention, which is a weak and long-lasting emotion, receives less attraction. In this paper, we present a study of attention recognition based on electrocardiogram (ECG) signals, which contain a wealth of information related to emotions.

Methods: The ECG dataset is derived from 10 subjects and specialized for attention detection. To relieve the impact of noise of baseline wondering and power-line interference, we apply wavelet threshold denoising as preprocessing and extract rich features by pan-tompkins and wavelet decomposition algorithms. To improve the generalized ability, we tested the performance of a variety of combinations of different feature selection algorithms and classifiers.

Results: Experiments show that the combination of generic algorithm and random forest achieve the highest correct classification rate (CCR) of 86.3%.

Conclusion: This study indicates the feasibility and bright future of ECG-based attention research.

Key words： affective computing    attention recognition    ECG signals
收稿日期: 2021-02-05      出版日期: 2022-10-08
Corresponding Author(s): Aihua Mao,Jie Luo   
 引用本文:   
. [J]. Quantitative Biology, 2022, 10(3): 276-286.
Aihua Mao, Zihui Du, Dayu Lu, Jie Luo. Attention emotion recognition via ECG signals. Quant. Biol., 2022, 10(3): 276-286.
 链接本文:  
https://academic.hep.com.cn/qb/CN/10.15302/J-QB-021-0267
https://academic.hep.com.cn/qb/CN/Y2022/V10/I3/276
Fig.1  
Fig.2  
Method Raw (%) PCA (%) GA (%) Relief (%)
SVM 76.33±4.75 76.80±4.28 77.48±5.40 77.74±5.15
MLP 67.68±4.39 64.19±1.58 70.05±2.93 62.73±6.64
KNN 81.14±4.45 75.68±4.50 82.30±5.09 84.58±4.61
CART 68.28±5.49 57.19±5.87 68.91±6.77 69.72±5.96
RandomForest 81.90±4.59 67.64±5.34 82.27±5.11 82.25±5.14
Naivebayes 61.92±3.85 58.45±4.64 60.28±5.48 61.24±3.63
Tab.1  
Fig.3  
Fig.4  
Fig.5  
Fig.6  
Classifier SVM KNN RandomForest CART MLP Naivebayes CNN
CCR 80.24% 84.72% 86.28% 70.70% 70.05% 61.92% 80.2%
Tab.2  
Fig.7  
Fig.8  
Fig.9  
Fig.10  
Fig.11  
Fig.12  
Morphological features P, Q, R, S, T wave, and PQ-, QS- and ST-intervals Average, median, standard deviation, minimum, maximum and fluctuation
Statistics features Low frequency of signals Average, median, standard deviation, minimum and maximum
Tab.3  
Fig.13  
Fig.14  
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