Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals

Obstructive sleep apnea (OSA) is a sleeping disorder that can cause multiple complications. Our aim is to build an automatic deep learning model for OSA event detection using combined signals from the electrocardiogram (ECG) and thoracic movement signals. We retrospectively obtained 420 cases of PSG data and extracted the signals of ECG, as well as the thoracic movement signal. A deep learning algorithm named ResNeSt34 was used to construct the model using ECG with or without thoracic movement signal. The model performance was assessed by parameters such as accuracy, precision, recall, F1-score, receiver operating characteristic (ROC), and area under the ROC curve (AUC). The model using combined signals of ECG and thoracic movement signal performed much better than the model using ECG alone. The former had accuracy, precision, recall, F1-score, and AUC values of 89.0%, 88.8%, 89.0%, 88.2%, and 92.9%, respectively, while the latter had values of 84.1%, 83.1%, 84.1%, 83.3%, and 82.8%, respectively. The automatic OSA event detection model using combined signals of ECG and thoracic movement signal with the ResNeSt34 algorithm is reliable and can be used for OSA screening.</p

The cell morphology of vocal fold fibroblasts (VFFs).

<p>(A) and differentiated adipose-derived mesenchymal stem cells (dADSCs) (B). Both showed a spindle shape. Scale bar  =  100μm.</p

Cell immunofluorescence for the observation of surface protein expression.

<p>Both vocal fold fibroblasts (VFFs) (A) and differentiated adipose-derived mesenchymal stem cells (dADSCs) (B) can express vimentin and fibronectin. Scale bar  =  50μm.</p

Diagram of the layered structure of the vocal fold (Hoechst staining).

<p>The canine vocal fold includes the epithelium (E), lamina propria (LP) and muscle (M). Scale bar  =  100μm.</p

The impact of the benefit ratio on ACER of different screening strategies based on the results of sensitivity analysis.

<p>Uni.OAE+AABR = universal strategy using OAE plus AABR; Uni.OAE = universal strategy using OAE; Select.OAE+AABR = targeted strategy using OAE plus AABR; Select.OAE = targeted strategy using OAE; ACER = Average Cost-Effectiveness Ratio.</p

The impact of the benefit ratio on ICER of shifting strategies based on the results of sensitivity analysis.

<p>Uni.OAE+AABR = universal strategy using OAE plus AABR; Uni.OAE = universal strategy using OAE; Select.OAE+AABR = targeted strategy using OAE plus AABR; Select.OAE = targeted strategy using OAE; ICER = Incremental Cost-Effectiveness Ratio.</p

The cost in per Disability-Adjusted Life Years (DALYs) of four screening strategies compared with no screening.

<p>Uni.OAE+AABR = universal strategy using OAE plus AABR; Uni.OAE = universal strategy using OAE; Select.OAE+AABR = targeted strategy using OAE plus AABR; Select.OAE = targeted strategy using OAE; Reference = 3 times of GDP per capita (19,700 international dollars).</p

Parameter values and plausible ranges for probability variables used in baseline and sensitivity analysis.

<p>Parameter values and plausible ranges for probability variables used in baseline and sensitivity analysis.</p

Estimation of cases detected and cases intervened by different screening strategies.

<p>Estimation of cases detected and cases intervened by different screening strategies.</p

Decision tree for cost-effectiveness analysis of different screening strategies among all simulated live births in China.

<p>Decision tree for cost-effectiveness analysis of different screening strategies among all simulated live births in China.</p