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Received: 5 December 2019 Accepted: 26 February 2020 The diagnosis of obstructive sleep apnea hypopnea syndrome (OSASH) and making decision of treatment necessity with positive airway pressure (PAP) therapy are time consuming and costly processes. There were different approaches in literature to accomplish these processes successfully and as soon as possible by using physiological signals with selected feature extraction and machine learning techniques. To reach fastest and true result, selection of optimal physiological signal(s), feature extraction and learning techniques is important. This study aimed to identify apnea hypopnea index (AHI) subgroups of 120 subjects and thus diagnose of OSASH and determine the need for PAP therapy by applying Multifractal Detrended Fluctuation Analysis (MDFA) as a feature extraction technique to only single channel nasal cannula airflow signals. After the extracted features from airflow signals with MDFA were gone through feature selection phase, the selected features were evaluated in Random Forest classifier. With the implementation of all processes, OSAHS patients were discriminated from healthy subjects with 95.83% accuracy, 96.88% sensitivity and 93.75% specificity. 93.75% sensitivities and 93.75%, 100% and 96.88% specificities were obtained for 15≤AHI (PAP therapy necessary), 5≤AHI<15 (require additional information for PAP therapy decision) and AHI <5 (not require PAP therapy) subgroups, respectively.

Identification of Apnea-Hypopnea Index Subgroups Based on Multifractal Detrended Fluctuation Analysis and Nasal Cannula Airflow Signals