scholarly journals SVM-Based Sleep Apnea Identification Using Optimal RR-Interval Features of the ECG Signal

2016 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Laiali Almazaydeh ◽  
Khaled Elleithy ◽  
Miad Faezipour ◽  
Helen Ocbagabir
Keyword(s):  
Sleep Apnea ◽  
Ecg Signal ◽  
Rr Interval

scholarly journals Nocturnal Arrhythmias and Heart‐Rate Swings in Patients With Obstructive Sleep Apnea Syndrome Treated With Beta Blockers

2020 ◽  
Vol 9 (21) ◽  
Author(s):  
Carolina Lombardi ◽  
Andrea Faini ◽  
Davide Mariani ◽  
Federica Gironi ◽  
Paolo Castiglioni ◽  
...  
Keyword(s):  
Heart Rate ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Beta Blockers ◽  
Sleep Apnea Syndrome ◽  
Real Life ◽  
Similar Frequency ◽  
Rr Interval ◽  
Obstructive Sleep ◽  
Induced Changes

Background The higher cardiovascular variability and the increased prevalence of arrhythmias in patients with obstructive sleep apneas may contribute to their higher rate of fatal events during sleep. In this regard, the use of beta blockers (BB) is debated because they may induce bradyarrhythmias and alter the pattern of heart rate changes induced by apneas. Thus, the aim of our study is to quantify peri‐apneic heart‐rate swings and prevalence of nocturnal bradyarrhythmias in BB‐treated and BB‐naïve patients with obstructive sleep apnea. Methods and Results Our real‐life, retrospective, cohort study analyzed data from patients with obstructive sleep apnea after a basal cardiorespiratory polysomnography. Among 228 eligible participants, we enrolled 78 BB‐treated and 88 BB‐naïve patients excluding those treated with antiarrhythmic drugs or pacemakers, or with uninterpretable ECG traces during polysomnography. In each patient, type and frequency of arrhythmias were identified and peri‐apneic changes of RR intervals were evaluated for each apnea. BB‐treated patients were older and with more comorbidities than BB‐naïve patients, but had similar obstructive sleep apnea severity, similar frequency of arrhythmic episodes, and similar prevalence of bradyarrhythmias. Apnea‐induced heart‐rate swings, unadjusted for age, showed lower RR interval changes in BB‐treated (133.5±63.8 ms) than BB‐naïve patients (171.3±87.7 ms, P =0.01), lower RR interval increases during apneas (58.5±28.5 versus 74.6±40.2 ms, P =0.01), and lower RR interval decreases after apneas (75.0±42.4 versus 96.7±55.5 ms, P <0.05). Conclusions BB appear to be safe in patients with obstructive sleep apnea because they are not associated with worse episodes of nocturnal bradyarrhythmias and even seem protective in terms of apnea‐induced changes of heart rate.

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 Development of Low-Cost ECG System for Comprehensive Monitoring Heart Function

2021 ◽  
Author(s):  
prihatin oktivasari ◽  
Ishartati Ishartati ◽  
Riandini Riandini ◽  
Amy Hamidah Salman ◽  
Freddy Haryanto ◽  
...  
Keyword(s):  
Heart Function ◽  
Low Cost ◽  
Integrated System ◽  
Ecg Signal ◽  
Analog To Digital ◽  
Rr Interval ◽  
Time Operation ◽  
The Status ◽  
Real Time Operation ◽  
Research And Education

Abstract A simple system, a low-cost, fully automated, and design for monitoring RR interval Electrocardiography (ECG) signal described in this paper. The platform, named Simple Low-Cost Electrocardiography System (SLES), is capable of monitoring RR interval and R peaks in 3 lead standards. The system is in .exe format, so it can be easily installed on a computer. The system's goal is to design a fully integrated system for measuring a characteristic of Heart Rate Variability (HRV) parameters for various applications in heart signal research and education. The ECG signal is analog filtered and amplified and processed from analog to digital. Eventually, the ECG signal will be shown on the monitor after digital filtering. The data obtained from the ECG will accurately reflect the status of human heart health. The system has the benefits of small volume, low power consumption, low cost, and real-time operation. All design and development reports, files, and system software will be given non-commercial use online on https://github.com/oktivasari.

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.

scholarly journals Phonocardiographic Signal and Electrocardiographic Signal Analysis for the Detection of Cardiovascular Diseases

2018 ◽  
Vol 15 (1) ◽  
pp. 79-89 ◽  
Author(s):  
V. Kalaivani ◽  
R. Lakshmi Devi ◽  
V. Anusuyadevi
Keyword(s):  
Cardiovascular Diseases ◽  
Heart Sound ◽  
Physiological State ◽  
Heart Sounds ◽  
Sound Signal ◽  
Ecg Signal ◽  
Short Term ◽  
Rr Interval ◽  
Short Term Variability ◽  
Heart Sound Signal

The main objective is to develop a novel method for the heart sound analysis for the detection of cardiovascular diseases. It can be considered as one of the important phases in the automated analysis of PCG signals. Heart sounds carry information about mechanical activity of the cardiovascular system. This information includes specific physiological state of the subject and the short term variability related to the respiratory cycle. The interpretation of sounds and extraction of changes in the physiological state while maintaining the short term variability are still an open problem and is subject of this paper. The system deals with the process of de-noising of the heart sound signal(PCG) and the signal is decomposed into several sub-bands and the de-noised heart sound signal is segmented into the basic heart sounds S1 and S2, along with the systolic and diastolic interval.. Also, the ECG signal is de-noised. Meanwhile, the R-peaks are identified from the ECG signal and RR interval is obtained. Extraction of features are done from both the heart sound signal and the ECG signal. From the features, the R-peaks are identified from the ECG signal and RR interval is obtained. The attribute selection is to find the best attribute values that can be used for the classification process. Finally, using classification technique, cardiac diseases are detected. This work is implemented by using MATLAB software.

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.

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