Sleep-disordered breathing (SDB), a common sleep disorder, shows symptoms of shallow breathing or paused breathing during sleep called respiratory events. SDB was conventionally diagnosed based on overnight multi-channel polysomnography (PSG) in clinical treatment. However, this process requires experienced sleep technicians to annotate and is quite labour-intensive. In this study, a novel one-dimensional signal based object detection network was proposed for automatic, high efficiency detection and classification of different kinds of respiratory events from continuous PSG signals. Our method can locate respiratory events in PSG signal data and classify them into four categories for further clinical treatment. The method was further validated on a PSG clinical dataset collected from Beijing Tongren Hospital. Precision, recall and F1-score of 84.9%, 85.1%, 85.0% were achieved for events detection with total accuracy rate reaching 74.9% in classification of detected events. The result shows that one-dimensional signal object detection is a promising method to locate the characteristic waveform and extract signal features. Such method can be applied in other signal feature detection field.
Background: Obstructive sleep apnea (OSA) is a common sleep disorder. However, current diagnostic methods are labor-intensive and require professionally trained personnel. We aimed to develop a deep learning model using upper airway computed tomography (CT) to predict OSA and to warn the medical technician if a patient has OSA while the patient is undergoing any head and neck CT scan, even for other diseases.Methods: A total of 219 patients with OSA [apnea-hypopnea index (AHI) ≥10/h] and 81 controls (AHI <10/h) were enrolled. We reconstructed each patient's CT into 3 types (skeletal structures, external skin structures, and airway structures) and captured reconstructed models in 6 directions (front, back, top, bottom, left profile, and right profile). The 6 images from each patient were imported into the ResNet-18 network to extract features and output the probability of OSA using two fusion methods: Add and Concat.Five-fold cross-validation was used to reduce bias. Finally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated.Results: All 18 views with Add as the feature fusion performed better than did the other reconstruction and fusion methods. This gave the best performance for this prediction method with an AUC of 0.882. Conclusions:We present a model for predicting OSA using upper airway CT and deep learning. The model has satisfactory performance and enables CT to accurately identify patients with moderate to severe OSA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.