Evidence of an Association of Obstructive Sleep Apnea with Diabetes and Diabetic Complications

Lin

Advanced Intelligent Systems

et al. 2023

Obstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low‐cost and easy‐to‐use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)‐based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four‐level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy‐to‐use and effective screening tool for OSA accordingly.

Section: Introductionmentioning

confidence: 99%

A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification

Lin

Advanced Intelligent Systems

et al. 2023

IEEJ Transactions Elec Engng

“…The motivation behind this research is that OSA diagnosis typically requires an overnight polysomnography (PSG) examination in a hospital, which is an inconvenient, time-consuming and expensive test. Patients often have to wait a long time for their turn, which poses a significant threat to the health of OSA patients [2]. Therefore, many studies [1,3,4] used ECG signals alone for OSA detection to build an OSA diagnostic system on portable devices, expecting to provide users an OSA screening tool at home with easy-to-use and affordable before their scheduled PSG test.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction with Multiple Receptive Fields Conducted on ECG Signals for Performance Enhancement in OSA Severity Classification

Yeh,

Chen,

Wang

et al. 2023

Obstructive sleep apnea (OSA) is a common sleep disorder, and polysomnography (PSG) is the gold standard for diagnosing OSA. However, patients often have to wait long before receiving the costly PSG test in a hospital. Hence, low‐cost and easy‐to‐use portable screening tools were developed for predicting OSA. Based on our recent study, this paper presents a feature extraction method with multiple receptive fields applied to the input electrocardiography (ECG) signals of a model to improve its performance in OSA severity classification. This work also employs unsegmented ECG signals as input to keep all the advantages from our original approach. The proposed model achieves an overall accuracy of 59.49% for four‐level OSA severity classification, giving an improvement of approximately 2% compared to our original work. The effectiveness of the proposed method is demonstrated. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

IEEJ Transactions Elec Engng

“…Obstructive sleep apnea (OSA) is a prevalent sleep disorder that significantly impacts sleep quality and can lead to various health issues, including cardiovascular diseases, diabetes and dementia [1]. The gold standard for diagnosing OSA is overnight polysomnography (PSG), which measures the apnea-hypopnea index (AHI).…”

Section: Introductionmentioning

confidence: 99%

Apnea‐Hypopnea Index Prediction for Obstructive Sleep Apnea Using Unsegmented SpO₂ Signals and Deep Learning

Chi,

Yeh,

Chen

et al. 2023

This paper presents an apnea‐hypopnea index (AHI) prediction model for obstructive sleep apnea (OSA) by using unsegmented peripheral oxygen saturation (SpO2) signals. This proposal, directly predicting AHI values with respect to overnight unsegmented SpO2 signals, is the first report in the literature, and simply solves the method limitation of our previous study. As well, this approach can provide more features of OSA assessment to users and doctors. Experimental results show that the presented model gives an overall accuracy up to 81.13% for four‐level OSA severity classification, which is higher than our original work and significantly outperforms most counterparts in the literature. This work can be used as an easy‐to‐use and effective screening tool for OSA before undergoing polysomnography (PSG). Moreover, doctors can arrange timely PSG tests for those who require preferential medical care according to the predicted OSA severity. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.