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New Data Pre-processing on Assessing of Obstructive Sleep Apnea Syndrome: Line Based Normalization Method (LBNM)

  • Bayram Akdemir
  • Salih Güneş
  • Şebnem Yosunkaya
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)

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.

Keywords

Obstructive Sleep Apnea Syndrome Data Scaling Line Based Normalization Method C4.5 Decision Tree Classifier Levenberg Marquart Artificial Neural Network 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Bayram Akdemir
    • 1
  • Salih Güneş
    • 1
  • Şebnem Yosunkaya
    • 2
  1. 1.Department of Electrical and Electronics EngineeringSelcuk UniversityKonyaTurkey
  2. 2.Faculty of Medicine, Sleep LaboratorySelcuk UniversityKonyaTurkey

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