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

Year 2017, Volume: 2 Issue: 2, 37 - 43, 01.12.2017

Can Zoroglu Serkan Turkeli

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

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