scholarly journals Sleep apnea classification using ECG-signal wavelet-PCA features

2014 ◽  
Vol 24 (6) ◽  
pp. 2875-2882 ◽  
Author(s):  
Vega Pradana Rachim ◽  
Gang Li ◽  
Wan-Young Chung
Keyword(s):  
Sleep Apnea ◽  
Ecg Signal ◽  
Wavelet Pca

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.

scholarly journals Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7731 ◽  
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Guohao Shen ◽  
Feng Hong
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Feature Representation ◽  
Training Data ◽  
Ecg Signal ◽  
Automatic Feature Extraction ◽  
Adjacent Segments ◽  
The Impact

Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

scholarly journals ECG Signal Analysis on an Embedded Device for Sleep Apnea Detection

2020 ◽  
pp. 377-384
Author(s):  
Rishab Khincha ◽  
Soundarya Krishnan ◽  
Rizwan Parveen ◽  
Neena Goveas
Keyword(s):  
Sleep Apnea ◽  
Signal Analysis ◽  
Ecg Signal ◽  
Embedded Device ◽  
Apnea Detection

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.

Detection of Sleep Apnea Disorder Using an Adaptive Neuro-Fuzzy Classification Method

2020 ◽  
Vol 17 (9) ◽  
pp. 4229-4234
Author(s):  
Jyoti Bali ◽  
Anilkumar V. Nandi ◽  
P. S. Hiremath ◽  
Poornima Patil
Keyword(s):  
Feature Extraction ◽  
Sleep Apnea ◽  
Principal Component ◽  
Feature Reduction ◽  
Fuzzy Classification ◽  
Ecg Signal ◽  
Night Time ◽  
Fuzzy Classifier ◽  
Diagnostic Modality ◽  
Neuro Fuzzy

The proposed work aims at developing a solution for the detection of sleep apnea disorder using ECG signal analysis, which is an established diagnostic modality. Under this work, the standard research resource, ECG-Apnea database from MIT’s Physionet.org., having ECG signal night time recordings, is used. The sequential procedure of Preprocessing, Peak or QRS complex detection, Feature extraction, Feature reduction, and Classification is used. Preprocessing of the ECG signal is performed to free it from noise resulted from baseline wander, power-line interference, and muscle artifacts. Thus, the improved signal quality is estimated in terms of its Signal to Noise Ratio (SNR) and entropy value. QRS detection is implemented using the popular Pan-Tompkins algorithm that provides the reference for the feature extraction process. The performance of the detection algorithm is measured in terms of the average values of accuracy and specificity as 98% and 96%, respectively. Feature extraction algorithm involves the collection of selected 30 feature values related to the time domain and the frequency domain gathered from each of the test recordings of the ECG database, minute-wise for 7 hours. Feature reduction technique is followed to reduce the data size to a set of 20 ECG signal features using Principal Component Analysis (PCA) and avoid redundancy. Hence the trained Adaptive Neuro-Fuzzy Classifier is used on the output feature set derived from PCA to detect the presence or absence of Sleep apnea disorder with an estimated accuracy and specificity as 95% and 96%, respectively.

A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal

2018 ◽  
Vol 294 ◽  
pp. 94-101 ◽  
Author(s):  
Kunyang Li ◽  
Weifeng Pan ◽  
Yifan Li ◽  
Qing Jiang ◽  
Guanzheng Liu
Keyword(s):  
Neural Network ◽  
Sleep Apnea ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Ecg Signal
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