Open AccessAttention emotion recognition via ECG signals
Author(s) -
Mao Aihua,
Du Zihui,
Lu Dayu,
Luo Jie
Publication year - 2022
Publication title -
quantitative biology
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.15302/j-qb-021-0267
Subject(s) - preprocessor , feature selection , computer science , pattern recognition (psychology) , anger , artificial intelligence , random forest , noise (video) , speech recognition , wavelet , interference (communication) , focus (optics) , data pre processing , feature extraction , feature (linguistics) , emotion classification , psychology , social psychology , computer network , channel (broadcasting) , linguistics , physics , philosophy , optics , image (mathematics)
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.