Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11271
Title: Deep Feature Extraction and Early Prediction of Obstructive Sleep Apnea Events
Authors: Nasifoglu, Huseyin
Eroğul, Osman
Keywords: prediction
obstructive sleep apnea
deep features
convolutional neural networks
ensemble classifiers
Publisher: Sciendo
Abstract: Obstructive sleep apnea (OSA) is a common sleeping breathing disorder characterized by interruptions in breathing or obstructions in the airway. An early prediction of OSA may help in avoiding the disorder’s symptoms by identifying events before they happen. In this regard, we proposed a methodology for OSA prediction using both convolutional neural networks and traditional machine learning approaches. For this purpose, 30-second pre-apnea and non-apnea segments of electrocardiogram (ECG) recordings were extracted for various leading times and 2D scalogram images representing the time-frequency characteristics were generated from each segment. Deep features extracted from scalogram images using a modified residual network were fed separately into a support vector machine and two ensemble classifiers, namely random subspace k-nearest neighbors (kNN) and random subspace discriminant classifiers. The subspace kNN classifier outperformed other classifiers and achieved performance results up to 86,97% accuracy, 88,19% sensitivity, 84,26% specificity, and 85,70% positive predictive value. These results suggest that using machine learning approaches to classify deep features of single lead ECG scalogram images may improve prediction performance. Ultimately, the proposed method can be used as an useful approximation to identify impending OSA events
URI: https://sciendo.com/article/10.2478/ebtj-2021-0030
https://hdl.handle.net/20.500.11851/11271
Appears in Collections:Biyomedikal Mühendisliği Bölümü / Department of Biomedical Engineering

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