Near Real-time Stress Prediction for Patients with Disturbed Allostatic Load
Resumo
Stress is one relevant cause of diseases nowadays, and prolonged exposure to stress can cause a disturbance in the allostatic load. Alternatives have been sought to deal with this situation and verify the impact of this allostatic load disorder. Wearable sensors are an option for automatically identifying acute stress since they can measure signs such as electrocardiogram, heart rate, electroencephalogram, electromyogram, or galvanic skin response. All these signals have intrinsic characteristics in a normal state and change if associated with stress occurrence. The literature presents Machine Learning Approaches and Deep Learning Models as alternatives to pattern detection in physiological signals. Nevertheless, we identify a gap regarding the allostatic load impact identification and the real-time classification when using these models. this article aims to acquire data in stress induction experiments in clinical and non-clinical patients, train a machine learning model, and, in sequence, carry out a new experiment to evaluate the classification in near real-time. The classification experiment presented results with accuracy above 92.72%. When it comes to real-time classification experiments we obtained an accuracy of 78.93%. Evaluating participants in experiments divided into clinical and non-clinical groups, a decrease of 5% in precision was identified. Based on the results obtained, we verified that the allostatic load can present challenges for real-time stress classification.
Referências
Ali, F., El-Sappagh, S., Islam, S. M. R., Ali, A., Muhammad, A., Imran, M., and Kwak, K.-S. (2021). An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems, 114:23 – 43.
Can, Y. S., Arnrich, B., and Ersoya, C. (2019). Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. Journal of Biomedical Informatics, 92.
Corrigan, S. L., Roberts, S., Warmington, S., Drain, J., and Main, L. C. (2021). Monitoring stress and allostatic load in first responders and tactical operators using heart rate variability: a systematic review. BMC Public Health, 21.
Costa, C. A. d., Pasluosta, C. F., Eskofier, B., Silva, D. B. d., and Righi, R. d. R. (2018). Internet of health things: Toward intelligent vital signs monitoring in hospital wards. Artificial Intelligence In Medicine, 89:61 – 69.
de Souza, A., Melchiades, M. B., Rigo, S. J., and Ramos, G. d. O. (2022). Mostress: a sequence model for stress classification. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–8.
Farias, S. M. d. C., Teixeira, O. L. d. C., Moreira, W., Aparecida, M., de Oliveira, F., and Pereira, M. O. (2011). Caracterização dos sintomas físicos de estresse na equipe de pronto atendimento. Revista da Escola de Enfermagem da USP, 45(3).
Guidi, J., Lucente, M., Sonino, N., and Fava, G. A. (2021). Allostatic load and its impact on health: A systematic review. Psychother Psychosom, 90(1):11 – 27.
Hammad, M., Abd El-Latif, A. A., Hussain, A., Abd El-Samie, F. E., Gupta, B. B., Ugail, H., and Sedik, A. (2022). Deep learning models for arrhythmia detection in iot healthcare applications. Computers and Electrical Engineering, 100:108011.
Iqbal, S. M. A., Mahgoub, I., Du, E., Leavitt, M. A., and Asghar, W. (2021). Advances in healthcare wearable devices. Flexible Electronics, 5(9).
Mbunge, E., Muchemwaa, B., Jiyane, S., and Batani, J. (2021). Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies. Global Health Journal.
Mukati, N., Namdev, N., Dilip, R., Hemalatha, N., Dhiman, V., and Sahu, B. (2021). Healthcare assistance to covid-19 patient using internet of things (iot) enabled technologies. Materials Today: Proceedings.
Sharma, A., Badea, M., Tiwari, S., and Marty, J. L. (2021). Wearable biosensors: An alternative and practical approach in healthcare and disease monitoring. Molecules, 26(748).
Wang, S., Li, L. Z., Lu, Z., Li, S., and Rehkopf, D. (2022). Work schedule control and allostatic load biomarkers: Disparities between and within gender. Social Indicators Research, 163(3):1249–1267.
