Screening of patients with obstructive sleep Apnea syndrome using C4.5 algorithm based on non linear analysis of respiratory signals during sleep

Aim: To classify patients with possible diagnosis of obstructive sleep apnea syndrome (OSAS) into groups according to the severity of the disease using a decision tree producing algorithm based on nonlinear analysis of 3 respiratory signals instead of the use of full polysomnography. Patients-Methods: Eighty-six consecutive patients referred to the Sleep Unit of a Pulmonology Department underwent full polysomnography and their tests were manually scored. Three nonlinear indices (Largest Lyapunov Exponent-LLE, Detrended Fluctuation Analysis-DFA and Approximate Entropy-APEN) were extracted from two respiratory signals (nasal cannula flow-F and thoracic belt-T). The oxygen saturation signal (SpO₂) was also selected. The above measurements provided data to the C4.5 algorithm using a data mining application. Results: Two decision trees were produced using linear and nonlinear data from 3 respiratory signals. The discrimination between normal subjects and sufferers from OSAS presented an accuracy of 84.9% and a recall of 90.3% using the variables age, sex, DFA from F and Time with SpO₂<90% (T90). The classification of patients into severity groups had an accuracy of 74.2% and a recall of 81.1% using the variables APEN from F, DFA from F and T90. Conclusion: It is possible to have reliable predictions of the severity of OSAS using linear and nonlinear indices from only two respiratory signals during sleep instead of performing full polysomnography. The proposed algorithm could be used for screening patients suspected to suffer from OSAS.