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-l...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.展开更多
基金The work of this paper is financially supported by NSF of Guangdong Province(No.2019A1515010833)the Fundamental Research Funds for the Central Universities(No.2020ZYGXZR089)the Social Science Research Base of Guangdong Province-Research Center of Network Civilization in New Era of SCUT.
文摘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.
