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EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES

Year 2022, Volume: 21 Issue: 41, 156 - 169, 28.06.2022
https://doi.org/10.55071/ticaretfbd.1125431

Abstract

Emotion recognition has attracted more interest by being applied in many application areas from different domains such as medical diagnosis, e-commerce, and robotics. This research quantifies the stimulated short-term effect of emotions on the autonomic nervous system and sympathetic activity. The primary purpose of this study is to investigate the responses of 21 adults by attaching a wearable system to measure physiological data such as an electrocardiogram and electrodermal activity in a controlled environment. Cardiovascular effects were evaluated with heart rate variability indices that included HR, HRV triangular-index, rMSSD (ms), pNN5O (%); frequency analysis of the very low frequency (VLF: 0-0,04 Hz), low frequency (LF: 0,04-0,15 Hz), and high frequency (HF: 0,15-0,4 Hz) components; nonlinear analysis. The sympathetic activity was evaluated with time-varying and time-invariant spectral analysis results of the EDA. The participants who experience calmness had a 4,8% lower heart rate (75,06±16,76 and 78,72±16,52) observed compared to happiness. Negative valance with high-arousal emotions like anger was invariably responded to with a peak in skin conductance level. Besides, negative valance with low-arousal emotions like sadness was allied with a drop in conductance level. Anger, in addition to being the most well-known emotion, elicited coherent time-varying spectral responses.

References

  • Adha, M.S. & Igasaki, T. (2020, July, 20-24). Concurrent model for three negative emotions using heart rate variability in a driving simulator environment. 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. 718–721.
  • Albraikan, A., Tobón, D.P. & El Saddik, A. (2018). Toward user-independent emotion recognition using physiological signals. IEEE Sensors Journal. 19(19), 8402-8412.
  • Balogh, S., Fitzpatrick, D.F., Hendricks, S.E. & Paige, S.R. (1993). Increases in heart rate variability with successful treatment in patients with major depressive disorder. Psychopharmacology Bulletin. 29(2), 201-206.
  • Barrett, H. & Popovi, N. (2015). A meta-synthesis on the effects of combining heart rate variability biofeedback and positive emotion on workplace performance. International Journal of Social Science Studies. 3(5), 61-68.
  • Berntson, G.G., Thomas Bigger, J., Eckberg, D.L., Grossman, P., Kaufmann, P.G., et al. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology. 34 (6), 623–648.
  • Boashash, B. (2015). Time-frequency signal analysis and processing: A comprehensive reference. Academic Press. Cambridge.
  • Cosoli, G., Poli, A., Scalise, L. & Spinsante, S. (2021, May, 17-20). Heart rate variability analysis with wearable devices: Influence of artifact correction method on classification accuracy for emotion recognition. IEEE International Instrumentation and Measurement Technology Conference. Scotland. 1–6.
  • Domínguez-Jiménez, J.A., Campo-Landines, K.C., Martínez-Santos, J.C., Delahoz, E. J. & Contreras-Ortiz, S.H. (2020). A machine learning model for emotion recognition from physiological signals. Biomedical Signal Processing and Control, 55, 1-11.
  • Dzedzickis, A., Kaklauskas, A. & Bucinskas, V. (2020). Human emotion recog- nition: Review of sensors and methods. Sensors, 20(3), 1-40.
  • Gorman, J.M. & Sloan, R.P. (2000). Heart rate variability in depressive and anxiety disorders. American Heart Journal. 140 (4), S77–S83.
  • Kawachi, I., Sparrow, D., Vokonas, P.S. & Weiss, S.T. (1995). Decreased heart rate variability in men with phobic anxiety (data from the normative aging study). The American Journal of Cardiology. 75 (14), 882–885.
  • Klein, E., Cnaani, E., Harel, T., Braun, S. & Ben-Haim, S.A. (1995). Altered heart rate variability in panic disorder patients. Biological Psychiatry. 37(1), 18–24.
  • Pan, J. & Tompkins, W.J. (1985). A real-time qrs detection algorithm. IEEE Transactions on Biomedical Engineering. 3, 230–236.
  • Pincus, S. (1995). Approximate entropy (apen) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science. 5 (1), 110–117.
  • Posada-Quintero, H. F., Reljin, N., Mills, C., Mills, I., Florian, J. P., VanHeest, J.L. & Chon, K.H. (2018). Time-varying analysis of electrodermal activity during exercise. PloS One, 13 (6), 1-12.
  • Sepúlveda, A., Castillo, F., Palma, C. & Rodriguez-Fernandez, M. (2021). Emotion recognition from ECG signals using wavelet scattering and machine learning. Applied Sciences, 11(11), 1-14.
  • Singson, L.N.B., Sanchez, M.T.U.R. & Villaverde, J.F. (2021, March, 20-21). Emotion recognition using short-term analysis of heart rate variability and resnet architecture. 13th International Conference on Computer and Automation Engineering. Australia. 15–18.
  • Takeshita, R., Shoji, A., Hossain, T., Yokokubo, A. & Lopez, G. (2021, November, 17-19). Emotion recognition from heart rate variability data of smartwatch while watching a video. 13th. International Conference on Mobile Computing and Ubiquitous Network. Tokyo. 1–6.
  • Yamuza, M.T.V., Bolea, J., Orini, M., Laguna, P., Orrite, C., Vallverdu, M. & Bailon, R. (2019). Human emotion characterization by heart rate variability analysis guided by respiration. IEEE Journal of Biomedical and Health Informatics, 23 (6), 2446–2454.
  • Yin, G., Sun, S., Yu, D., Li, D. & Zhang, K. (2022). A multimodal framework for large-scale emotion recognition by fusing music and electrodermal activity signals. ACM Transactions on Multimedia Computing, Communications, and Applications. 18(3), 1–23.
  • Yu, S.N. & Chen, S.F. (2015, August, 25-29). Emotion state identification based on heart rate variability and genetic algorithm. 37th Annual International Conference of The IEEE Engineering in Medicine and Biology Society. 538–541.
  • Zhang, J., Yin, Z., Chen, P. & Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion. 59, 103–126.

OTONOM SİNİR SİSTEMİ VE SEMPATİK AKTİVİTENİN DUYGU DURUMU ÜZERİNDEKİ ETKİLERİNİN DEĞERLENDİRİLMESİ

Year 2022, Volume: 21 Issue: 41, 156 - 169, 28.06.2022
https://doi.org/10.55071/ticaretfbd.1125431

Abstract

Duygu tanıma, tıbbi teşhis, e-ticaret, robotik gibi farklı alanlarda birçok uygulama şekli ile gerçeklenerek yüksek ilgi görmüştür. Bu araştırma, duyguların otonom sinir sistemi ve sempatik aktivite üzerindeki uyarılmış kısa vadeli etkisini ölçmektedir. Çalışmanın birincil amacı, kontrollü bir ortamda elektrokardiyogram ve elektrodermal aktivite vb. fizyolojik verileri ölçmek için giyilebilir bir sistem kullanan 21 yetişkin katılımcının tepkilerini araştırmaktır. Kardiyovasküler etkiler, HR, HRV üçgen-indeksi, rMSSD (ms), pNN5O (%); çok düşük frekans (VLF: 0-0,04 Hz), düşük frekans (LF: 0,04-0,15 Hz) ve yüksek frekans (HF: 0,15-0,4 Hz) bileşenlerinin frekans analizi; SD1, SD2 ve SD oranının doğrusal olmayan analizi, sempatik aktivite, EDA'nın zamanla değişen ve zamanla değişmeyen spektral analiz sonuçları ile değerlendirildi. Sakinlik hisseden katılımcıların mutluluğa kıyasla %4,8% daha düşük kalp atış hızına (75,06±16,76 ve 78,72±16,52) sahip olduğu gözlemlendi. Öfke gibi yüksek uyarılma seviyesi sahip olumsuz duygularda her zaman cilt iletkenlik değerleri zirve ölçümleri tespit ettik. Ayrıca, üzüntü gibi düşük uyarılma düzeyindeki negatif duygulara sahip olanlar iletkenlik seviyesindeki bir azalma ile bağlantılıydı. Öfke, en iyi tespit edilebilen duygu olmasının yanı sıra, zamanla değişen tutarlı spektral tepkiler ortaya çıkardığı görüldü.

References

  • Adha, M.S. & Igasaki, T. (2020, July, 20-24). Concurrent model for three negative emotions using heart rate variability in a driving simulator environment. 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. 718–721.
  • Albraikan, A., Tobón, D.P. & El Saddik, A. (2018). Toward user-independent emotion recognition using physiological signals. IEEE Sensors Journal. 19(19), 8402-8412.
  • Balogh, S., Fitzpatrick, D.F., Hendricks, S.E. & Paige, S.R. (1993). Increases in heart rate variability with successful treatment in patients with major depressive disorder. Psychopharmacology Bulletin. 29(2), 201-206.
  • Barrett, H. & Popovi, N. (2015). A meta-synthesis on the effects of combining heart rate variability biofeedback and positive emotion on workplace performance. International Journal of Social Science Studies. 3(5), 61-68.
  • Berntson, G.G., Thomas Bigger, J., Eckberg, D.L., Grossman, P., Kaufmann, P.G., et al. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology. 34 (6), 623–648.
  • Boashash, B. (2015). Time-frequency signal analysis and processing: A comprehensive reference. Academic Press. Cambridge.
  • Cosoli, G., Poli, A., Scalise, L. & Spinsante, S. (2021, May, 17-20). Heart rate variability analysis with wearable devices: Influence of artifact correction method on classification accuracy for emotion recognition. IEEE International Instrumentation and Measurement Technology Conference. Scotland. 1–6.
  • Domínguez-Jiménez, J.A., Campo-Landines, K.C., Martínez-Santos, J.C., Delahoz, E. J. & Contreras-Ortiz, S.H. (2020). A machine learning model for emotion recognition from physiological signals. Biomedical Signal Processing and Control, 55, 1-11.
  • Dzedzickis, A., Kaklauskas, A. & Bucinskas, V. (2020). Human emotion recog- nition: Review of sensors and methods. Sensors, 20(3), 1-40.
  • Gorman, J.M. & Sloan, R.P. (2000). Heart rate variability in depressive and anxiety disorders. American Heart Journal. 140 (4), S77–S83.
  • Kawachi, I., Sparrow, D., Vokonas, P.S. & Weiss, S.T. (1995). Decreased heart rate variability in men with phobic anxiety (data from the normative aging study). The American Journal of Cardiology. 75 (14), 882–885.
  • Klein, E., Cnaani, E., Harel, T., Braun, S. & Ben-Haim, S.A. (1995). Altered heart rate variability in panic disorder patients. Biological Psychiatry. 37(1), 18–24.
  • Pan, J. & Tompkins, W.J. (1985). A real-time qrs detection algorithm. IEEE Transactions on Biomedical Engineering. 3, 230–236.
  • Pincus, S. (1995). Approximate entropy (apen) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science. 5 (1), 110–117.
  • Posada-Quintero, H. F., Reljin, N., Mills, C., Mills, I., Florian, J. P., VanHeest, J.L. & Chon, K.H. (2018). Time-varying analysis of electrodermal activity during exercise. PloS One, 13 (6), 1-12.
  • Sepúlveda, A., Castillo, F., Palma, C. & Rodriguez-Fernandez, M. (2021). Emotion recognition from ECG signals using wavelet scattering and machine learning. Applied Sciences, 11(11), 1-14.
  • Singson, L.N.B., Sanchez, M.T.U.R. & Villaverde, J.F. (2021, March, 20-21). Emotion recognition using short-term analysis of heart rate variability and resnet architecture. 13th International Conference on Computer and Automation Engineering. Australia. 15–18.
  • Takeshita, R., Shoji, A., Hossain, T., Yokokubo, A. & Lopez, G. (2021, November, 17-19). Emotion recognition from heart rate variability data of smartwatch while watching a video. 13th. International Conference on Mobile Computing and Ubiquitous Network. Tokyo. 1–6.
  • Yamuza, M.T.V., Bolea, J., Orini, M., Laguna, P., Orrite, C., Vallverdu, M. & Bailon, R. (2019). Human emotion characterization by heart rate variability analysis guided by respiration. IEEE Journal of Biomedical and Health Informatics, 23 (6), 2446–2454.
  • Yin, G., Sun, S., Yu, D., Li, D. & Zhang, K. (2022). A multimodal framework for large-scale emotion recognition by fusing music and electrodermal activity signals. ACM Transactions on Multimedia Computing, Communications, and Applications. 18(3), 1–23.
  • Yu, S.N. & Chen, S.F. (2015, August, 25-29). Emotion state identification based on heart rate variability and genetic algorithm. 37th Annual International Conference of The IEEE Engineering in Medicine and Biology Society. 538–541.
  • Zhang, J., Yin, Z., Chen, P. & Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion. 59, 103–126.
There are 22 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Fatma Patlar Akbulut 0000-0002-9689-7486

Publication Date June 28, 2022
Submission Date June 2, 2022
Published in Issue Year 2022 Volume: 21 Issue: 41

Cite

APA Patlar Akbulut, F. (2022). EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES. İstanbul Commerce University Journal of Science, 21(41), 156-169. https://doi.org/10.55071/ticaretfbd.1125431
AMA Patlar Akbulut F. EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES. İstanbul Commerce University Journal of Science. June 2022;21(41):156-169. doi:10.55071/ticaretfbd.1125431
Chicago Patlar Akbulut, Fatma. “EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES”. İstanbul Commerce University Journal of Science 21, no. 41 (June 2022): 156-69. https://doi.org/10.55071/ticaretfbd.1125431.
EndNote Patlar Akbulut F (June 1, 2022) EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES. İstanbul Commerce University Journal of Science 21 41 156–169.
IEEE F. Patlar Akbulut, “EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES”, İstanbul Commerce University Journal of Science, vol. 21, no. 41, pp. 156–169, 2022, doi: 10.55071/ticaretfbd.1125431.
ISNAD Patlar Akbulut, Fatma. “EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES”. İstanbul Commerce University Journal of Science 21/41 (June 2022), 156-169. https://doi.org/10.55071/ticaretfbd.1125431.
JAMA Patlar Akbulut F. EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES. İstanbul Commerce University Journal of Science. 2022;21:156–169.
MLA Patlar Akbulut, Fatma. “EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES”. İstanbul Commerce University Journal of Science, vol. 21, no. 41, 2022, pp. 156-69, doi:10.55071/ticaretfbd.1125431.
Vancouver Patlar Akbulut F. EVALUATING THE EFFECTS OF THE AUTONOMIC NERVOUS SYSTEM AND SYMPATHETIC ACTIVITY ON EMOTIONAL STATES. İstanbul Commerce University Journal of Science. 2022;21(41):156-69.