scholarly journals ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Maryam Faal ◽  
Farshad Almasganj
Keyword(s):  
Sleep Apnea ◽  
Mean Square Error ◽  
Arima Model ◽  
Sleep Apnea Syndrome ◽  
Percentage Error ◽  
Ecg Signal ◽  
Mean Square ◽  
Ecg Signals ◽  
Mean And Variance ◽  
Apnea Syndrome

This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying. So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), where the former captures the linearity property and the latter the nonlinear characteristics of the ECG signal. First, ECG signals are segmented into one-minute segments. The heteroskedasticity property is then examined through various tests such as the ARCH/GARCH test, kurtosis, skewness, and histograms. Next, the ARIMA model is applied to signals as a linear model and EGARCH as a nonlinear model. The appropriate orders of models are estimated by using the Bayesian Information Criterion (BIC). We assess the effectiveness of our model in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The data in this article is obtained from the Physionet Apnea-ECG database. Results show that the ARIMA-EGARCH model performs better than other models for modeling both apneic and normal ECG signals in sleep apnea syndrome.

A Novel Method for the Detection of Sleep Apnea Syndrome Based on Single-Lead ECG Signal

2012 ◽  
Vol 239-240 ◽  
pp. 1079-1083 ◽  
Author(s):  
Yue Wen Tu ◽  
Xiao Min Yu ◽  
Hang Chen ◽  
Shu Ming Ye
Keyword(s):  
Sleep Apnea ◽  
Sleep Apnea Syndrome ◽  
Support Vector ◽  
Ecg Signal ◽  
Respiratory Signal ◽  
Peak Points ◽  
Novel Method ◽  
Apnea Syndrome ◽  
Prevention Of Hypertension ◽  
Electrocardiogram Ecg

The diagnosis of sleep apnea syndrome (SAS) has important clinical significance for the prevention of hypertension, coronary heart disease, arrhythmias, stroke and other diseases. In this paper, a novel method for the detection of SAS based on single-lead Electrocardiogram (ECG) signal was proposed. Firstly, the R-peak points of ECG recordings were pre-detected to calculate RR interval series and ECG-derived respiratory signal (EDR). Then 40 time- and spectral-domain features were extracted and normalized. Finally, support vector machine (SVM) was employed to these features as a classifier to detect SAS events. The performance of the presented method was evaluated using the MIT-BIH Apnea-ECG database, results show that an accuracy of 95% in train sets and an accuracy of 88% in test sets are achievable.

Computer Supported Detection for Obstructive Sleep Apnea Using Electrocardiographic-Acceleration Sensors

2014 ◽  
Vol 556-562 ◽  
pp. 2715-2718
Author(s):  
Chang Man Kim ◽  
Ji Won Baek ◽  
Yoon Nyun Kim ◽  
Hyung Jin Kim ◽  
Tea Hyung Kim ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sleep Apnea Syndrome ◽  
Upper Airway ◽  
Triaxial Accelerometer ◽  
Ecg Signals ◽  
Obstructive Sleep ◽  
Acceleration Sensors ◽  
Apnea Syndrome ◽  
Aided Diagnosis

Obstructive sleep apnea syndrome is a sleep-related breathing disorder that is caused by obstruction of the upper airway. In this research, we explore a computer-aided diagnosis system with portable ECG equipment and tri-accelerometer (x, y, and z-axes) that can automatically analyze biosignals and test for OSA. We developed an approach to record ECG signals and abdominal movements induced by breathing by affixing ECG-enabled electrodes onto a triaxial accelerometer. With the two signals simultaneously measured, the apnea data obtained would be more accurate, relative to cases where a single signal is measured. This would be helpful in diagnosing OSA.

Prevalence of undiagnosed chronic obstructive pulmonary disease in patients with suspected sleep apnea syndrome

Pneumologie ◽  
2017 ◽  
Vol 71 (S 01) ◽  
pp. S1-S125
Author(s):  
EJ Soto Hurtado ◽  
P Gutiérrez Castaño ◽  
MD Almenara Escribano ◽  
J de la Cruz Rios
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Sleep Apnea ◽  
Pulmonary Disease ◽  
Sleep Apnea Syndrome ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Apnea Syndrome

Postoperative monitoring of children with obstructive sleep apnea syndrome

2014 ◽  
Vol 155 (18) ◽  
pp. 703-707 ◽  
Author(s):  
Pálma Benedek ◽  
Gabriella Kiss ◽  
Eszter Csábi ◽  
Gábor Katona
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep Apnea Syndrome ◽  
Preoperative Evaluation ◽  
Sleep Apnea Syndrome ◽  
Study Patient ◽  
Severe Obstructive Sleep Apnea ◽  
Obstructive Sleep ◽  
Significant Difference ◽  
Apnea Syndrome

Introduction: Treatment of pediatric obstructive sleep apnea syndrome is surgical. The incidence of postoperative respiratory complications in this population is 5–25%. Aim: The aim of the authors was to present the preoperative evaluation and monitoring procedure elaborated in Heim Pál Children Hospital, Budapest. Method: 142 patients were involved in the study. Patient history was obtained and physical examination was performed in all cases. Thereafter, polysomnography was carried out, the severity of the obstructive sleep apnea syndrome was determined, and the patients underwent tonsilloadenotomy. Results: 45 patients with mild, 50 patients with moderate and 47 patients with severe obstructive sleep apnea syndrome were diagnosed. There was no complication in patients with mild disease, while complications were observed in 6 patients in the moderate group and 24 patients in the severe group (desaturation, apnea, stridor, stop breathing) (p<0.000). In patients with severe obstructive sleep apnea syndrome, no significant difference was noted in preoperative apnoea-hypapnea index (p = 0.23) and in nadir oxygen saturation values (p = 0.73) between patients with and without complication. Conclusions: Patients with severe obstructive sleep apnea syndrome should be treated in hospital where pediatric intensive care unit is available. Orv. Hetil., 2014, 155(18), 703–707.

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