FUZZY EXPERT SYSTEM FOR SEVERITY PREDICTION OF OBSTRUCTIVE SLEEP APNEA HYPOPNEA SYNDROME

Abstract

Polysomnography (PSG) is standard for both OSAHS diagnosis and severity detection, but it has some disadvantages such as requirement for many equipment, conditions and times to get successful measurements. The aim of the study is to design a fuzzy expert system (FES) to predict the severity degree of obstructive sleep apnea hypopnea syndrome (OSAHS). Pre-operation data of 24 patients who had robotic surgery for treatment of OSAHS are used. We divided the data into two: 14 of them for designing the FES and 10 patient data for testing the model. min SpO₂,, BMI, Mallampati score, and neck circumference (NC) information are used as inputs of the system. The output is fuzzified apnea hypopnea index (AHI). Then, this prediction compared with the actual AHI scores of the patients. Classification accuracy for design step is 100% and correlation between our prediction and AHI is 0.89 after removing 4 patients because of missing data. For the test result, classification accuracy is 100% and value of correlation coefficient is 0.82 after leaving one out due to same reason. Our study shows a possibility of simpler alternative to PSG and proposes fuzziness in standard AHI intervals as different point of view.

Keywords

• Fuzzy expert system,Severity detection,prediction,Obstructive Sleep Apnea Hypopnea Syndrome

References

1. [1] Demir, A., U. Obstrüktif uyku apne sendromu ve obezite. Hacettepe Tıp Dergisi. 38, 177-193 (2007).
2. [2] Lloberes, P., et al. Self-reported sleepiness while driving as a risk factor for traffic accidents in patients with obstructive sleep apnoea syndrome and in non-apnoeic snorers. Respir Med. 94, 971-976 (2000).
3. [3] Evlice, A. T. Obstrüktif uyku apne sendromu. Arşiv Kaynak Tarama Dergisi. 21, 134-150 (2012).
4. [4] Marin, J. M., Carrizo, S. J., Vicente, E., & Agusti, A. G. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. The Lancet. 365, 1046-1053 (2005).
5. [5] Partinen, M., & McNicholas, W. T. Epidemiology, morbidity and mortality of the sleep apnoea syndrome. European Respiratory Monograph. 10, 63-74 (1998).
6. [6] Estévez, Diego Álvarez. Diagnosis of the sleep apnea-hypopnea syndrome: a comprehensive approach through an intelligent system to support medical decision. PhD Thesis. Universidade da Coruña (2012).
7. [7] Quan, S. F., Gillin, J. C., Littner, M. R., & Shepard, J. W. Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. editorials. Sleep. 22, 662-689 (1999).
8. [8] Ruehland, W. R., et al. The new AASM criteria for scoring hypopneas: impact on the apnea hypopnea index. Sleep. 32, 150-157 (2009).

9. [9] Durkin, J. Research review: Application of expert systems in the sciences. The Ohio Journal of Science. 90, 171-179 (1990).
10. [10] Liao, S. H. Expert system methodologies and applications—a decade review from 1995 to 2004. Expert systems with applications. 28, 93-103 (2005).
11. [11] Abbod, M. F., von Keyserlingk, D. G., Linkens, D. A., & Mahfouf, M. Survey of utilisation of fuzzy technology in medicine and healthcare. Fuzzy Sets and Systems. 120, 331-349 (2001).
12. [12] Castanho, M. J. P., Hernandes, F., De Ré, A. M., Rautenberg, S., & Billis, A. Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Systems with Applications. 40, 466-470 (2013).
13. [13] Abdullah, A. A., Zakaria, Z., & Mohamad, N. F. Design and Development of Fuzzy Expert System for Diagnosis of Hypertension. In IEEE Intelligent Systems, Modelling and Simulation (ISMS), 2011 Second International Conference on. 113-117 (2011).
14. [14] Ribeiro, A. C., Silva, D. P., & Araujo, E. Fuzzy breast cancer risk assessment. In Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on 1083-1087 (2014).
15. [15] Neshat, M., Yaghobi, M., Naghibi, M. B., & Esmaelzadeh, A. Fuzzy expert system design for diagnosis of liver disorders. In IEEE Knowledge Acquisition and Modeling, 2008. KAM'08. International Symposium on 252-256 (2008).
16. [16] Lee, C. S., & Wang, M. H. A fuzzy expert system for diabetes decision support application. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on. 41, 139-153 (2011).
17. [17] Keleş, A., Keleş, A., & Yavuz, U. Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Expert Systems with Applications. 38, 5719-5726 (2011).
18. [18] Oladele, T. O., Sadiku, J. S., & Oladele, R. O. Coactive neuro-fuzzy expert system: A framework for diagnosis of malaria. African Journal of Computing & ICTs (AJOCICT). A Publication of the Computer Chapter of the IEEE Nigeria Section. 7, 173-186 (2014).
19. [19] Allahverdi, N., Torun, S., & Saritas, I. Design of a fuzzy expert system for determination of coronary heart disease risk. In Proceedings of the 2007 international conference on Computer systems and technologies. 36 (2007).
20. [20] Keleş, A., & Keleş, A. ESTDD: Expert system for thyroid diseases diagnosis. Expert Systems with Applications. 34, 242-246 (2008).
21. [21] Mahfouf, M., Abbod, M. F., & Linkens, D. A. A survey of fuzzy logic monitoring and control utilisation in medicine. Artificial intelligence in medicine. 21, 27-42 (2001).
22. [22] Lee, M., Ahn, J. M., Min, B. G., Lee, S. Y., & Park, C. H. Total artificial heart using neural and fuzzy controller. Artificial organs. 20, 1220-1226 (1996).
23. [23] Atlas, E., Nimri, R., Miller, S., Grunberg, E. A., & Phillip, M. MD-Logic Artificial Pancreas System A pilot study in adults with type 1 diabetes. Diabetes Care. 33, 1072-1076 (2010).
24. [24] Steimann, F. The interpretation of time-varying data with DIAMON-1. Artificial Intelligence in Medicine. 8, 343-357 (1996).
25. [25] Becker, K., et al. Design and validation of an intelligent patient monitoring and alarm system based on a fuzzy logic process model. Artificial intelligence in medicine. 11, 33-53 (1997).
26. [26] Wolf, M., et al. Improved monitoring of preterm infants by fuzzy logic. Technology and health care. 4, 193-201 (1996).
27. [27] Phillips, M., et al. Prediction of breast cancer using volatile biomarkers in the breath. Breast cancer research and treatment. 99, 19-21 (2006).
28. [28] Seker, H., Odetayo, M. O., Petrovic, D., & Naguib, R. N. A fuzzy logic based-method for prognostic decision making in breast and prostate cancers. Information Technology in Biomedicine, IEEE Transactions on. 7, 114-122(2003).
29. [29] Moret-Bonillo, V., Alvarez-Estévez, D., Fernández-Leal, A., & Hernández-Pereira, E. Intelligent Approach for Analysis of Respiratory Signals and Oxygen Saturation in the Sleep Apnea/Hypopnea Syndrome. The open medical informatics journal. 8, 1-19 (2014).
30. [30] Nazeran, H., Almas, A., Behbehani, K., Burk, J., & Lucas, E. A fuzzy inference system for detection of obstructive sleep apnea. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE. 2, 1645-1648 (2001).
31. [31] Polat, K., Yosunkaya, Ş., & Güneş, S. Pairwise ANFIS approach to determining the disorder degree of obstructive sleep apnea syndrome. Journal of medical systems. 32, 379-387 (2008).
32. [32] Rowley, J. A., Aboussouan, L. S., & Badr, M. S. The use of clinical prediction formulas in the evaluation of obstructive sleep apnea. Sleep. 23, 929-942 (2000).
33. [33] Magalang, U. J., et al. Prediction of the apnea-hypopnea index from overnight pulse oximetry. Chest. 124, 1694-1701 (2003).
34. [34] Marcos, J. V., Hornero, R., Alvarez, D., Aboy, M., & Del Campo, F. Automated prediction of the apnea-hypopnea index from nocturnal oximetry recordings. Biomedical Engineering, IEEE Transactions on. 59, 141-149 (2012).
35. [35] Hajek, P., Fuzzy logic (2010) Available at: http://plato.stanford.edu/entries/logic-fuzzy/ (Accessed: 26th April 2015).
36. [36] Adlassnig, K., P. Fuzzy set theory in medical diagnosis. Systems, Man and Cybernetics, IEEE Transactions on. 16, 260-265 (1986).
37. [37] Bausmer, U., Gouveris, H., Selivanova, O., Goepel, B., & Mann, W. Correlation of the Epworth Sleepiness Scale with respiratory sleep parameters in patients with sleep-related breathing disorders and upper airway pathology. European Archives of Oto-Rhino-Laryngology. 267, 1645-1648 (2010).
38. [38] Sivanandam, S. N., Sumathi, S., & Deepa, S. N. Introduction to Fuzzy Logic using MATLAB Vol. 1 (eds Sivanandam, S. N. et al.) (Springer-Verlag Berlin Heidelberg, 2007).
39. [39] Bloch, K., E. Polysomnography: a systematic review. Technology and health care. 5, 285-305 (1997).
40. [40] Metersky, M. L., & Castriotta, R. J. The effect of polysomnography on sleep position: possible implications on the diagnosis of positional obstructive sleep apnea. Respiration. 63, 283-287 (1996).Bıographıes.