ECG-based mental stress assessment using fuzzy computing and associative Petri net

Journal of Medical and Biological Engineering

Volumn 35, Issue 6, 2015, Pages 833-844

  Chiang, Hsiu Sen ^a  

^a NATIONAL TAICHUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Associative Petri net; Electrocardiogram (ECG); Fuzzy theory; Heart rate variability; Mental stress

Indexed keywords

COMPUTATION THEORY; DATA MINING; DIAGNOSIS; ELECTROCARDIOGRAPHY; HEART; NOISE ABATEMENT; PATTERN RECOGNITION; PETRI NETS; PHYSIOLOGY;

CARDIO-VASCULAR DISEASE; FUZZY THEORY; HEART RATE VARIABILITY; MENTAL STRESS; PHYSIOLOGICAL CHARACTERISTICS; PHYSIOLOGICAL CONDITION; TIME AND FREQUENCY DOMAINS; TIME- AND FREQUENCY-DOMAIN ANALYSIS;

FREQUENCY DOMAIN ANALYSIS;

ARTICLE; ASSOCIATIVE PETRI NET; AUTONOMIC NERVOUS SYSTEM; CLINICAL ASSESSMENT; CLINICAL ASSESSMENT TOOL; DATA BASE; DATA MINING; DIAGNOSTIC ACCURACY; ELECTROCARDIOGRAM; FOURIER TRANSFORMATION; FUZZY LOGIC; HEART RATE VARIABILITY; HUMAN; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MENTAL HEALTH; MENTAL STRESS; MINIMIZE ENTROPY PRINCIPLE APPROACH; SINGULAR VALUE DECOMPOSITION; SYSTOLIC BLOOD PRESSURE;

EID: 84957968406     PISSN: 16090985     EISSN: 21994757     Source Type: Journal    
DOI: 10.1007/s40846-015-0095-7     Document Type: Article

References (48)

1. Sharma, N., & Gedeon, T. (2012). Objective measures, sensors and computational techniques for stress recognition and classification: A survey. Computer Methods and Programs in Biomedicine, 108, 1287-1301.
2. Buckle, J. (2003). Clinical aromatherapy: Essential oils in practice (pp. 228-246). New York: Churchill Livingstone.
3. Kopin, I. J., Eisenhofer, G., & Goldstien, D. (1988). Sympathoadrenal medullary system and stress. Advances in Experimental Medicine and Biology, 245, 11-23.
4. Block, J. P., He, Y., Zaslavsky, A. M., Ding, L., & Ayanian, J. Z. (2009). Psychosocial stress and change in weight among US adults. American Journal of Epidemiology, 170, 181-192.
5. Nielsen, N. R., Zhang, Z. F., Kristensen, T. S., Netterstrom, B., Schnohr, P., & Gronbak, M. (2005). Self reported stress and risk of breast cancer: Prospective cohort study. British Medical Journal, 331, 548-550.
6. Freeman, J. B., Garcia, A. M., Fucci, C., Karitani, M., Miller, L., & Leonard, H. L. (2003). Family-based treatment of early-onset obsessive-compulsive disorder. Journal of Child and Adolescent Psychopharmacology, 13, 71-80.
7. K. S. Yoo and W. H. Lee (2011) Mental stress assessment based on pulse photoplethysmography, IEEE 15th International Symposium on Consumer Electronics Electronics, 323-326
8. Ramirez, A. J., Graham, J., Richards, M. A., & Gregory, W. M. (1996). Mental health of hospital consultants: the effects of stress and satisfaction at work. The Lancet, 347, 724-728.
9. Prosser, D., Johnson, S., Kuipers, E., Szmukler, G., Bebbington, P., & Thornicroft, G. (1997). Perceived sources of work stress and satisfaction among hospital and community mental health staff, and their relation to mental health, burnout and job satisfaction. Journal of Psychosomatic Research, 43, 51-59.
10. Okada, Y., Yoto, T. Y., Suzuki, T., Sakuragawa, S., Sakakibara, H., Shimoi, K., & Sugiura, T. (2013). Wearable ECG recorder with acceleration sensors for monitoring daily stress. Journal of Medical and Biological Engineering, 33, 420-426.
11. Tikkanen, P. E., Sellin, L. C., Kinnunen, H. O., & Huikuri, H. V. (1999). Using simulated noise to define optimal QT intervals for computer analysis of ambulatory ECG. Medical Engineering & Physics, 21, 15-25.
12. Ubeyli, E. D. (2007). ECG beats classification using multiclass support vector machines with error correcting output codes. Digital Signal Processing, 17, 675-684.
13. Chandola, T., Britton, A., & Brunner, E. (2008). Work stress and coronary heart disease: what are the mechanisms? European Heart Journal, 29, 640-648.
14. Kumar, M., Weippert, M., Vilbrandt, R., Kreuzfeld, S., & Stoll, R. (2007). Fuzzy evaluation of heart rate signals for mental stress assessment. IEEE Transactions on Fuzzy Systems, 15, 791-808.
15. Ranganathan, G., Rangarajan, R., & Bindhu, V. (2012). Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique. Applied Soft Computing, 12, 1978-1984.
16. Langley, J. N. (1921). The autonomic nervous system, Part I. Cambridge: W. Heffer and Sons.
17. Croiset, G., Nijsen, M. J., & Kamphuis, P. J. (2000). Role of corticotrophin releasing factor, vasopressin and the autonomic nervous system in learning and memory. European Journal of Pharmacology, 405, 225-234.
18. Drolet, G., Dumont, E. C., Gosselin, I., Kinkead, R., Laforest, S., & Trottier, J. F. (2001). Role of endogenous opioid system in the regulation of the stress response. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 25, 729-741.
19. Healey, J. A., & Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 6, 156-166.
20. Von Borell, E., Langbein, J., Despres, G., Hansen, S., Leterrier, C., Marchant-Forde, J., et al. (2007). Heart rate variability as a measure of autonomic regulation of cardiac activity for assessing stress and welfare in farm animals: A review. Physiology & Behavior, 92, 293-316.
21. Zickmann, B., Hofmann, H. C., Pottkamper, C., Knothe, C., Boldt, J., & Hempelmann, G. (1996). Changes in heart rate variability during induction of anesthesia with fentanyl and midazolam. Journal of Cardiothoracic and Vascular Anesthesia, 10, 609-613.
22. Stein, P. K., Hagley, M. T., Cole, P. L., Domitrovich, P. P., Kleiger, R. E., & Rottman, J. N. (1999). Changes in 24 hour heart rate variability during normal pregnancy. American Journal of Obstetrics and Gynecology, 180, 978-985.
23. Achten, J., & Jeukendrup, A. E. (2003). Heart rate monitoring: Applications and limitations. Sports Medicine, 33, 517-555.
24. Burger, A. J., Charlamb, M., & Sherman, H. B. (1999). Circadian patterns of heart rate variability in normals, chronic stable angina and diabetes mellitus. International Journal of Cardiology, 71, 41-48.
25. Niskanen, J. P., Tarvainen, M. P., Ranta-aho, P. O., & Karjalainen, P. A. (2004). Software for advanced HRV analysis. Computer Methods and Programs in Biomedicine, 76, 73-81.
26. Thayer, J. F., Yamamoto, S. S., & Brosschot, J. F. (2010). The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International Journal of Cardiology, 141, 122-131.
27. Hayes, M. H. (1996). Statistical digital signal processing and modelling. New York: John Wiley & Sons.
28. Dorukl, A., Turkbay, T., Yelbo, Z., Ciyiltepe, M., Iyisoy, A., Sutcigil, L., & Ozflahin, A. (2008). Autonomic nervous system imbalance in young adults with developmental stuttering. Bulletin of Clinical Psychopharmacology, 18, 274-281.
29. Saul, J. P. (1990). Beat-to-beat variations of heart rate reflect modulation of cardiac autonomic outflow. News in Physiological Sciences, 5, 32-37.
30. Pagani, M., Rimoldi, O., Pizzinelli, P., Furlan, R., Crivellaro, W., Liberati, D., et al. (1991). Assessment of the neural control of the circulation during psychological stress. Journal of the Autonomic Nervous System, 35, 33-42.
31. Satish, S. C., Rao, M. D., Renae, A. H., Jerry, M. S., & Mindi, J. C. (1998). Psychological and physical stress induce differential effects on human colonic motility. American Journal of Gastroenterology, 93, 985-990.
32. Leor-Librach, R. J., Eliash, S., Kaplinsky, E., & Bobrovsky, B. Z. (2003). Very low-frequency heart rate variability wave amplitude and sympathetic stimulation-characterization and modeling. IEEE Transactions on Biomedical Engineering, 50, 797-803.
33. Shusterman, V., & Barnea, O. (2005). Sympathetic nervous system activity in stress and biofeedback relaxation. IEEE Engineering in Medicine and Biology Magazine, 24, 52-57.
34. Karthikeyan, P., Murugappan, M., & Yaacob, S. (2011). ECG signals based mental stress assessment using wavelet transform. Proceedings of the 2011 IEEE International Conference on Control System, Computing and Engineering, pp. 258-262.
35. Mokhayeri, F., & Akbarzadeh-T, M. R. (2011). Mental stress detection based on soft computing techniques. Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, pp. 430-433.
36. De Santos Sierra, A., Sanchez Avila, C., Bailador del Pozo, G., & Guerra Casanova J. (2011). Stress detection by means of stress physiological template. Proceedings of the 2011 Third World Congress on Nature and Biologically Inspired Computing, pp. 131-136.
37. Adnane, M., Jiang, Z., Mori, N., & Matsumoto, Y. (2011). An automated program for mental stress and apnea/hypopnea events detection. Proceedings of the 2011 7th International Workshop on Systems, Signal Processing and their Applications, pp. 59-62.
38. Physionet Dataset: Stress Recognition in Automobile Drivers (2015). http://physionet.nlm.nih.gov/pn3/drivedb/.
39. Friesen, G., Jannett, T., Jadallah, M., Yates, S., Quint, S., & Nagle, H. (1990). A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Transactions on Biomedical Engineering, 37, 85-98.
40. Jung, W. H., & Lee, S. G. (2012). An R-peak detection method that uses an SVD filter and a search back system. Computer Methods and Programs in Biomedicine, 108, 1121-1132.
41. So, H. H., & Chan, K. L. (1997). Development of QRS detection method for real-time ambulatory cardiac monitor. Proceedings of IEEE 19th Annual International Conference. Engineering in Medicine and Biology Society, pp. 289-292.
42. Christensen, R. (1980). Entropy minimax sourcebook volume III: Computer implementation. Lincoln, Massachusetts: Entropy Limited.
43. Ross, T. J. (2010). Fuzzy logic with engineering applications. Hoboken: John Wiley & Sons Ltd.
44. Shih, D. H., Chiang, H. S., & Lin, B. (2007). A generalized associative petri net for reasoning. IEEE Transactions on Knowledge and Data Engineering, 19, 1241-1251.
45. Agrawal, R., Imielinski, T., & Swami, A. (1993). Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5, 914-925.
46. Deng, Y., Hsu, D. F., Wu, Z. & Chu, C. H. (2012a). Feature selection and combination for stress identification using correlation and diversity, pervasive systems. Proceedings of the 12th International Symposium on Algorithms and Networks, pp. 37-43.
47. Deng, Y., Wu, Z., Chu, C. H., & Yang, T. (2012b). Evaluating feature selection for stress identification. Proceedings ofthe IEEE 13th International Conference on Information Reuse and Integration, pp. 584-591.
48. Wang, J. S., Lin, C. W., & Yang, Y. T. (2013). A k-nearest- neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition. Neurocomputing, 116, 136-143.