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

被引:34
作者
Goshvarpour, Atefeh [1 ]
Abbasi, Ataollah [1 ]
Goshvarpour, Ateke [1 ]
机构
[1] Sahand Univ Technol, Computat Neurosci Lab, Dept Biomed Engn, Fac Elect Engn, POB 51335-1996, Tabriz, Iran
关键词
Emotion; Classification; Lagged Poincare plot; Fusion; NONLINEAR DYNAMICS; SIGNALS; CLASSIFICATION; EXPRESSIONS; MUSIC;
D O I
10.1007/s13246-017-0571-1
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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 SD1 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.
引用
收藏
页码:617 / 629
页数:13
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