Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/620
Title: Apneic Events Detection Using Different Features of Airflow Signals
Authors: Göğüş, Fatma Zehra
Tezel, Gülay
Keywords: Apneic Event Detection
Feature Extraction
Classification
Oner Attribute Eval Feature Selection
Random Forest
Random Forest Algorithm
Hypopnea Events
Sleep-Apnea
Gender Determination
Oximetry
Issue Date: 2019
Publisher: MEHRAN UNIV ENGINEERING & TECHNOLOGY
Abstract: Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons's Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MIT-BIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis.
URI: https://doi.org/10.22581/muet1982.1901.01
https://hdl.handle.net/20.500.13091/620
ISSN: 0254-7821
2413-7219
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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