Quantification of vascular function changes under different emotion states: A pilot study

Article English OPEN
Xia, Yirong ; Yang, Licai ; Mao, Xueqin ; Zheng, Dingchang ; Liu, Chengyu (2016)

Recent studies have indicated that physiological parameters change with different emotion states. This study aimed to quantify the changes of vascular function at different emotion and sub-emotion states. Twenty young subjects were studied with their finger photoplethysmographic (PPG) pulses recorded at three distinct emotion states: natural (1 minute), happiness and sadness (10 minutes for each). Within the period of happiness and sadness emotion states, two sub-emotion states (calmness and outburst) were identified with the synchronously recorded videos. Reflection index (RI) and stiffness index (SI), two widely used indices of vascular function, were derived from the PPG pulses to quantify their differences between three emotion states, as well as between two sub-emotion states. The results showed that, when compared with the natural emotion, RI and SI decreased in both happiness and sadness emotions. The decreases in RI were significant for both happiness and sadness emotions (both P< 0.01), but the decreases in SI was only significant for sadness emotion (P< 0.01). Moreover, for comparing happiness and sadness emotions, there was significant difference in RI (P< 0.01), but not in SI (P= 0.9). In addition, significant larger RI values were observed with the outburst sub-emotion in comparison with the calmness one for both happiness and sadness emotions (both P< 0.01) whereas significant larger SI values were observed with the outburst sub-emotion only in sadness emotion (P< 0.05). Moreover, gender factor hardly influence the RI and SI results for all three emotion measurements. This pilot study confirmed that vascular function changes with diffenrt emotion states could be quantified by the simple PPG measurement.
  • References (12)
    12 references, page 1 of 2

    Eyben, F.; Wollmer, M.; Poitschke, T.; Schuller, B.; Blaschke, C.; Farber, B.; Nguyen Thien, N., Emotion on the road-necessity, acceptance, and feasibility of affective computing in the car. Advances in Human-Computer Interaction 2010, Volume 2010, ID 263593.

    Wu, Y.W.; Wang, T.T.; Chu, X.N., Affective modeling and recognition of learning emotion: Application to e-learning. J Software 2009, 4, 859-866.

    Arias Tapia, S.A.; Ratte, S.; Gomez, A.H.F.; Gonzalez Eras, A.; Barbosa, J.; Torres, J.C.; Reategui Rojas, R.; Valdiviezo Diaz, P.; Guaman Bastidas, F.; Riofrio Calderon, G.E., et al., First contribution to complex emotion recognition in patients with alzheimer's disease. In Ambient assisted living and daily activities, Pecchia, L.; Chen, L.L.; Nugent, C.; Bravo, J., Eds. Springer International Publishing: 2014; Vol. 8888, pp 341-347.

    Wang, J.F.; Chen, B.W.; Fan, W.K.; Li, C.H., Emotion-aware assistive system for humanistic care based on the orange computing concept. Appl Comput Intell Soft Comput 2012, Volume 2012, ID 183610.

    In Usage of emotion recognition in military health care: Detecting emotional change under stress, Defense Science Research Conference and Expo, Singapore, 2011; IEEE: Singapore, pp 1-5.

    Neoha, S.C.; Zhang, L.; Mistrya, K.; Hossainb, M.A.; Limc, C.P.; Aslama, N.; Kinghorna, P., Intelligent facial emotion recognition using a layered encoding cascade optimization model. Appl Soft Comput 2015, 34, 72-93.

    Zhang, L.; Jiang, M.; Faridc, D.; Hossaina, M.A., Intelligent facial emotion recognition and semantic-based topic detection for a humanoid robot. Expert Syst Appl 2013, 40, 5160-5168.

    Koolagudi, S.G.; Krothapalli, S.R., Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features. Int J Speech Technol 2012, 15, 495-511.

    Blaiech, H.; Neji, M.; Wali, A.; Alimi, A.M. In Emotion recognition by analysis of eeg signals, 13th International Conference on Hybrid Intelligent Systems (HIS), Gammarth, 2013; IEEE: Gammarth, pp 312-318.

    Agrafioti, F.; Hatzinakos, D.; Anderson, A.K., Ecg pattern analysis for emotion detection. IEEE Trans Affect Comput 2012, 3, 102-115.

  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    29
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    Anglia Ruskin Research Online - IRUS-UK 0 29
Share - Bookmark