Abstract
Sleep disorders are a very common unawareness illness among public. Obstructive Sleep Apnea Syndrome (OSAS) is characterized with decreased oxygen saturation level and repetitive upper respiratory tract obstruction episodes during full night sleep. In the present study, we have proposed a novel data normalization method called Line Based Normalization Method (LBNM) to evaluate OSAS using real data set obtained from Polysomnography device as a diagnostic tool in patients and clinically suspected of suffering OSAS. Here, we have combined the LBNM and classification methods comprising C4.5 decision tree classifier and Artificial Neural Network (ANN) to diagnose the OSAS. Firstly, each clinical feature in OSAS dataset is scaled by LBNM method in the range of [0,1]. Secondly, normalized OSAS dataset is classified using different classifier algorithms including C4.5 decision tree classifier and ANN, respectively. The proposed normalization method was compared with min-max normalization, z-score normalization, and decimal scaling methods existing in literature on the diagnosis of OSAS. LBNM has produced very promising results on the assessing of OSAS. Also, this method could be applied to other biomedical datasets.
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References
AASM. Sleep-Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research. The Report of an American Academy of Sleep Medicine Task Force, SLEEP, Vol. 22(5) (1999)
Eliot, S., Janita, K., Cheryl Black, L., Carole, L.: Marcus. Pulse Transit Time as a measure of arousal and respiratory effort in children with sleep-disorder breathing. Pediatric research 53(4), 580–588 (2003)
Al-Ani, T., Hamam, Y., Novak, D., Pozzo Mendoza, P., Lhotska, L., Lofaso, F., Isabey, D., Fodil, R.: Noninvasive Automatic Sleep Apnea Classification System, Bio. Med. Sim. 2005, Linköping, Sweden, May 26–27 (2005)
Haitham, M., Al-Angari, A., Sahakian, V.: Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome. IEEE Transactions in Biomedical Engineering 54(10), 1900–1904 (2007)
Campo, F.d., Hornero, R., Zamarro´n, C., Abasolo, D.E., A´lvarez, D.: Oxygen saturation regularity analysis in the diagnosis of obstructive sleep apnea. Artificial Intelligence in Medicine 37, 111–118 (2006)
Kwiatkowska, M., Schmittendorf, E.: Assessment of Obstructive Sleep Apnea using Pulse Oximetry and Clinical Prediction Rules: a Fuzzy Logic Approach, BMT (2005)
Polat, K., Yosunkaya, Ş., Güneş, S.: Pairwise ANFIS Approach to Determining the Disorder Degree of Obstructive Sleep Apnea Syndrome. Journal of Medical Systems 32(3), 243–250 (2008)
Mitchell, M.T.: Machine Learning. McGraw-Hill, Singapore (1997)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Akdemir, B., Polat, K., Günes, S.: Prediction of E.Coli Promoter Gene Sequences Using a Hybrid Combination Based on Feature Selection, Fuzzy Weighted Pre-processing, and Decision Tree Classifier. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part I. LNCS (LNAI), vol. 4692, pp. 125–131. Springer, Heidelberg (2007)
Haykin, S.: Neural networks: A comprehensive foundation. Macmillan College Publishing Company, NewYork (1994)
Kara, S., Guven, A.: Neural Network-Based Diagnosing for Optic Nerve Disease from Visual-Evoked Potential. 31, 391–396 (2007)
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Akdemir, B., Güneş, S., Yosunkaya, Ş. (2008). New Data Pre-processing on Assessing of Obstructive Sleep Apnea Syndrome: Line Based Normalization Method (LBNM). In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2008. Communications in Computer and Information Science, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85930-7_25
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DOI: https://doi.org/10.1007/978-3-540-85930-7_25
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