Um Modelo Sensı́vel ao Contexto para Avaliação da Saúde Mental por meio da Variabilidade da Frequência Cardı́aca
Resumo
O método tradicional de avaliação da saúde mental, realizado nor- malmente por um psicoterapeuta, mostra ı́ndices de imprecisão relevantes. Este trabalho apresenta o modelo RevitalMe, que analisa a frequência cardı́aca a fim de contribuir ao método tradicional. O modelo proporciona ao psicoterapeuta informações do dia a dia do indivı́duo, estabelecendo uma correlação entre a saúde mental e os lugares frequentados, por meio da sensibilidade ao contexto. A avaliação do modelo foi realizada com a implementação e uso de um protótipo aplicado ao estresse, que apresenta F1-Score de 88% na classificação do estado do indivı́duo entre “estressado” e “não estressado”. A utilidade percebida do modelo é de 83% de acordo com 5 psicoterapeutas.
Referências
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Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., and Suri, J. S. (2006). Heart rate variability: a review. Medical & Biological Engineering & Computing, 44(12):1031–1051. http://dx.doi.org/10.1007/s11517-006-0119-0
Taelman, J., Vandeput, S., Spaepen, A., and Van Huffel, S. (2008). Influence of mental stress on heart rate and heart rate variability. In IFMBE Proceedings, volume 22, pages 1366–1369. http://dx.doi.org/10.1007/978-3-540-89208-3_324
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Webb, A. K. and Parks, P. D. (2016). Psychophysiological monitoring: An approach for the diagnosis and treatment of mental health disorders. IEEE Pulse, 7(1):31–34. http://dx.doi.org/10.1109/MPUL.2015.2498518
WHO (2001). The world health report 2001 - mental health: new understanding, new hope. Technical report.
WHO (2017). Depression and other common mental disorders: Global health estimates. Technical report.
Choi, K.-H., Kim, J., Kwon, O. S., Kim, M. J., Ryu, Y. H., and Park, J.-E. (2017). Is heart rate variability (hrv) an adequate tool for evaluating human emotions? a focus on the use of the international affective picture system (iaps). Psychiatry Research, 251:192–196. http://dx.doi.org/10.1016/j.psychres.2017.02.025
Da Rosa, J. H., Barbosa, J. L., and Ribeiro, G. D. (2016). Oracon: An adaptive model for context prediction. Expert Systems with Applications, 45:56–70. http://dx.doi.org/10.1016/j.eswa.2015.09.016
Dobbins, C. and Fairclough, S. (2017). A mobile lifelogging platform to measure anxiety and anger during real-life driving. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 327–332. http://dx.doi.org/10.1109/PERCOMW.2017.7917583
Gjoreski, M., Gjoreski, H., Luštrek, M., and Gams, M. (2016). Continuous stress detection using a wrist device – in laboratory and real life. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp ’16, pages 1185–1193. ACM Press. http://dx.doi.org/10.1145/2968219.2968306
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000). Physiobank, physiotoolkit, and physionet : Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215–e220.
Healey, J. and Picard, R. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 6(2):156–166.
Hovsepian, K., Al’Absi, M., Ertin, E., Kamarck, T., Nakajima, M., and Kumar, S. (2015). cstress: Towards a gold standard for continuous stress assessment in the mobile environment. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15, volume 2015, pages 493–504. ACM Press. http://dx.doi.org/10.1145/2750858.2807526
Marangunic, N. and Granic, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1):81–95. http://dx.doi.org/10.1007/s10209-014-0348-1
Mayya, S., Jilla, V., Tiwari, V. N., Nayak, M. M., and Narayanan, R. (2015). Continuous monitoring of stress on smartphone using heart rate variability. In 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), pages 1–5. http://dx.doi.org/10.1109/BIBE.2015.7367627
Peltola, M. (2012). Role of editing of r-r intervals in the analysis of heart rate variability. Frontiers in Physiology, 3:148. http://dx.doi.org/10.3389/fphys.2012.00148
Quintana, D. S., Alvares, G. A., and Heathers, J. A. J. (2016). Guidelines for reporting articles on psychiatry and heart rate variability (graph): recommendations to advance research communication. Translational Psychiatry, 6(5):e803–e803. http://dx.doi.org/10.1038/tp.2016.73
Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., and Suri, J. S. (2006). Heart rate variability: a review. Medical & Biological Engineering & Computing, 44(12):1031–1051. http://dx.doi.org/10.1007/s11517-006-0119-0
Taelman, J., Vandeput, S., Spaepen, A., and Van Huffel, S. (2008). Influence of mental stress on heart rate and heart rate variability. In IFMBE Proceedings, volume 22, pages 1366–1369. http://dx.doi.org/10.1007/978-3-540-89208-3_324
Tal, A. and Torous, J. (2017). The digital mental health revolution: Opportunities and risks. Psychiatric Rehabilitation Journal, 40(3):263–265. http://dx.doi.org/10.1037/prj0000285
Webb, A. K. and Parks, P. D. (2016). Psychophysiological monitoring: An approach for the diagnosis and treatment of mental health disorders. IEEE Pulse, 7(1):31–34. http://dx.doi.org/10.1109/MPUL.2015.2498518
WHO (2001). The world health report 2001 - mental health: new understanding, new hope. Technical report.
WHO (2017). Depression and other common mental disorders: Global health estimates. Technical report.
Publicado
11/06/2019
Como Citar
BAVARESCO, Rodrigo Simon; BARBOSA, Jorge Luis Victória.
Um Modelo Sensı́vel ao Contexto para Avaliação da Saúde Mental por meio da Variabilidade da Frequência Cardı́aca. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 58-69.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6242.
