Electrocardiogram Extraction Research Articles

Classification and analysis of cardiac arrhythmia based on incremental support vector regression on IOT platform

The electrocardiogram (ECG) is a diagnostic device capable of monitoring normal or irregular heart function. The entire ECG beat can be categorized into five different forms of beat arrhythmias (N, S, V, F, U). Quick and precise diagnosis of forms of arrhythmia is critical for identifying the heart problem and provides the proper treatment to the patient. In this paper, Discrete Wavelet Transform and Higher Order Statistics techniques has been used for analyzing and determining the ECG signals and implement it on an IOT-based platform. This system is based on three categories: The first approach involves inputting the ECG data; the second approach involves extracting the ECG beats with their respective amplitude from the base line. Wavelet transform function, and higher order statistics are used to eliminate noise and unwanted signal components and thus to extract ECG features. The third approach is to classify the ECG beats based on the Incremental Support vector regression classifier. After classification ECG beat is transmitted to the controller section for signal processing are given to controller section (Arduino Uno). The process can be implemented by employing the statistical feature for the feature extraction from the ECG signal. Compared to other approaches, the method provided by Incremental Support vector regression to identify the ECG beats and predict arrhythmia can provide successful detection of arrhythmias. The basic concept of the proposed system is to provide patients with reliable health care by using cloud data compliance to allow doctors to use this information and to provide a fast and feasible service. The findings show that the proposed algorithm is successful in predicting cardiac arrhythmias, with a 98% that is higher than other approaches.

Systematic Approach to Processing and Analysis Diagnostic Indicators of Electrocardiograms Based on Labview

Introduction. Cardiovascular disease occupies an important place throughout the world, which necessitates the development of more effective modern means of diagnosis and treatment. The primary diagnosis of heart disease is based on analysis and processing of an electrocardiogram (ECG). Despite the fact that there are many methods and algorithms for ECG analysis and processing, one of the urgent problems of cardiology remains to obtain the most complete information about heart electric potential, respectively, the behavior of the waves P, Q, R, S and T.Aim. Development of algorithms and software for processing and analysis of electrocardiograms (ECGs), as well as calculation of heart rate and detection of arrhythmias based on Labview.Materials and methods. The methods for removing noise using the wavelet transform method to eliminate baseline deviation ,to extract ECG signs ,to calculate heart rate and to detect arrhythmias based on Labview have been adopted as a mathematical apparatus for processing and analyzing ECGs.Results. Organizing of the ECG database, developing algorithms for converting the ECG file of the database into a useful format for Labview, processing of the ECG signal with removing noise from the original ECG signal, extracting signs for obtaining ECG diagnostic indicators, calculating heart rate and detecting arrhythmias.Conclusion. An analysis of the results demonstrates that systematic approaches to evaluating ECG signals allow to avoid one-way decisions and to integrate different methods into an integrated system of ideas of the state. The implementation of the proposed algorithms using Labview programming system ensures the removal of noise and artifacts, the extraction of the necessary ECG signs, the calculation of heart contractions and the detection of arrhythmias.

Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis.

In this Letter, a robust technique is presented to detect and classify different electrocardiogram (ECG) noises including baseline wander (BW), muscle artefact (MA), power line interference (PLI) and additive white Gaussian noise (AWGN) based on signal decomposition on mixed codebooks. These codebooks employ temporal and spectral-bound waveforms which provide sparse representation of ECG signals and can extract ECG local waves as well as ECG noises including BW, PLI, MA and AWGN simultaneously. Further, different statistical approaches and temporal features are applied on decomposed signals for detecting the presence of the above mentioned noises. The accuracy and robustness of the proposed technique are evaluated using a large set of noise-free and noisy ECG signals taken from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH polysmnographic database and Fantasia database. It is shown from the results that the proposed technique achieves an average detection accuracy of above 99% in detecting all kinds of ECG noises. Furthermore, average results show that the technique can achieve an average sensitivity of 98.55%, positive productivity of 98.6% and classification accuracy of 97.19% for ECG signals taken from all three databases.

Single channel high noise level ECG deconvolution using optimized blind adaptive filtering and fixed-point convolution kernel compensation

An electrocardiogram (ECG) is used to record the electrical activity of the heart. However, ECG signals are susceptible to the noise from various sources which increases the probability of misinterpretation and can affect the diagnostic process. Traditional noise cancellation techniques, which uses finite and deterministic coefficient, are not efficient, since the ECG signals are non-stationary. Thus, adaptive filters are commonly utilized on such signal as they can adjust their coefficient according to the changing nature of non-stationary signal. Adaptive algorithms still have a disadvantage that they require the model of noise or desired signal. In this paper, a novel algorithm is introduced based on fixed-point convolution kernel compensation for finding a model for using an adaptive filter; then a recursive least square method is used for completing steps of deconvolution of the ECG signal. The deconvolution method can be used for denoising ECG signals in very low signal to noise ratio circumstances and also can be used in blind source separation applications such as separation of fetal ECG from maternal ECG. ECG signals were utilized in this study are taken from the MIT-BIH Arrhythmia database for showing the performance of the algorithm on denoising applications. The results demonstrate that the proposed algorithm renders a much-improved performance in removing the noise from ECG signals, especially in a scenario where signal to noise ratio is negative. Moreover, the noninvasive fetal ECG dataset (NI-FECG) provided by Physionet is also used for fetal ECG extraction by a single thoracic channel. By comparing fetal ECG extraction methods in the literature and the proposed method, it reveals that the proposed method can extract the QRS complex of fetal ECG by a single thoracic channel as accurate as other methods which use abdominal channels.

Home-based mobile fetal/maternal electrocardiogram acquisition and extraction with cloud assistance.

Electrocardiogram (ECG) monitoring of the fetus during pregnancy, before and during labor, can provide crucial information for the assessment of fetal well-being and development, as well as labor progress. An out-of-clinics fetal ECG monitoring system may pave the way for instant diagnosis, suggesting immediate intervention, which could help reduce the fetal mortality rate. In this paper, we present an unobtrusive fetal maternal ECG monitoring system which can operate in the home setting. The acquisition of the mother's abdominal ECG is done using the non-contact electrode approach. The extraction of the fetal ECG from the combined fetal/maternal ECG signal is investigated using both Fast Independent Component Analysis (FastICA) and RobustICA algorithms. An accelerometer is integrated for motion artifact detection which would help reduce interferences due to movement. The device also is connected to a cloud server, allowing doctors to access the data in real time.

Application of SVM Algorithm for Fetal ECG Extraction from a Single Maternal Abdominal Record

The electrical activity of the foetal cardiac muscles is known as foetal ECG (FECG), and it can provide crucial details on the health of the fetus's heart. A pregnant woman's belly can be used to non-invasively capture this signal during pregnancy. However, since additional sources of noise, including the maternal ECG generally overpower the FECG recording, it would be ineffective. However, a clean FECG may be retrieved from the abdominal recording if it is correctly processed, and FECG can be used to evaluate the functioning of the foetal heart. In order to extract foetal electrocardiogram (ECG) from a single abdomen record, a unique two-tier approach is presented in this work. The abdominal signal is processed through a smoothing filter in the first layer of the proposed approach in order to determine the maternal ECG's estimated value. Findings on synthetic and actual abdominal ECG data demonstrate that the intended technique can extract foetal ECG with signal quality equivalent or superior to that retrieved by multichannel based mechanisms. The anticipated maternal ECG is then nonlinearly matched with the abdominal signal through polynomial networks.

Diagnosing Heart Disease using Wavelet Transformation and Adaptive Neuro Fuzzy Inference System (ANFIS) Based on Electrocardiagram (ECG)

Heart disease is the leading cause of death in the world. Heart disease is called the silent killer, because it often occcurs suddenly. Therefore, periodic cardiac examination is very necessary to reduce cases of death from heart disease.Heart disease can be known through electrocardiogram (ECG) examination. This study aims to explain the process of diagnosing heart disease through ECG using wavelet transformation and Adaptive Neuro Fuzzy Inference System (ANFIS).The process of diagnosing heart disease begins with cutting ECG signal consisting of 9-11 waves into one ECG wave, then decomposition and extraction are performed using wavelet transformation to obtain 6 parameters. The parameters will be used as input in ANFIS model. Data obtained from ECG extraction are divided into 70% training data and 30% testing data The output from the ANFIS model is a diagnosis of heart diseases, such as left bundle branch block (LBBB), right bundle branch block (RBBB), and normal. ANFIS learning is divided into 6 stages, namely clustering data with Fuzzy C-Means method, computing the degree of membership of each data, determining fixed neurons, looking for normalized firing strength, calculating the consequent parameter values, and determining network output.The results of the study obtained the best ANFIS model with 10 clusters. The level of accuracy, specificity, and sensitivity for training data is 100%, 100%, and 100%, respectively and for the testing data, the level of accuracy, specificity, and sensitivity is 100%, 100%, and 100%, respectively.

ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient

Electrocardiogram (ECG) is a method used by physicians to detect cardiac disease. Requirements for batch processing and accurate recognition of clinical data have led to the applications of deep-learning methods for feature extraction, classification, and denoising of ECGs; however, deep learning requires large amounts of data and multi-feature integration of datasets, with most available methods used for ECGs incapable of extracting global features or resulting in unstable, low quality training. To address these deficiencies, we proposed a novel generative adversarial architecture called RPSeqGAN using a training process reliant upon a sequence generative adversarial network (SeqGAN) algorithm that adopts the policy gradient (PG) in reinforcement learning. Based on clinical records collected from the MIT-BIH arrhythmia database, we compared our proposed model with three deep generative models to evaluate its stability by observing the variance of their loss curves. Additionally, we generated ECGs with five periods and evaluated them according to six metrics suitable for time series. The results indicate that the proposed model showed the highest stability and data quality.

Performance comparison of MeRMaId-ICA and Np-ICA in composite abdominal electrocardiogram separation

Blind source separation strives to disintegrate a multivariate composite non-invasive abdominal electrocardiogram signal into independent non-Gaussian signals such as maternal and foetal electrocardiograms. This paper proffers two separation algorithms especially for foetal electrocardiogram (FECG) extraction as it has a great role in diagnosing the foetal heart deformities namely minimum Renyi's mutual information called MeRMaId algorithm and non-parametric independent component analysis (Np-ICA) algorithm. Both the algorithms are experimentally evaluated on synthetic and real abdominal data. Performance juxtaposition of these two algorithms is done by scrutinising the signal to noise ratio at assorted noise levels and signal to interference ratio at assorted amplitude levels, and computing correlation coefficient (ρ) of the original and the estimated maternal and foetal electrocardiograms.

Fetal ECG extraction exploiting joint sparse supports in a dual dictionary framework

Electrocardiogram (ECG) signals are vital tools in assessing the health of the mother and the fetus during pregnancy. Extraction of fetal ECG (FECG) signal from the mother's abdominal recordings requires challenging signal processing tasks to eliminate the effects of the mother's ECG (MECG) signal, noise and other distortion sources. The availability of ECG data from multiple electrodes provides an opportunity to leverage the collective information in a collaborative manner. We propose a new scheme for extracting the fetal ECG signals from the abdominal ECG recordings of the mother using the multiple measurement vectors approach. The scheme proposes a dual dictionary framework that employs a learned dictionary for eliminating the MECG signals through sparse domain representation and a wavelet dictionary for the noise reduced sparse estimation of the fetal ECG signals. We also propose a novel methodology for inferring a single estimate of the fetal ECG source signal from the individual sensor estimates. Simulation results with real ECG recordings demonstrate that the proposed scheme provides a comprehensive framework for eliminating the mother's ECG component in the abdominal recordings, effectively filters out noise and distortions, and leads to more accurate recovery of the fetal ECG source signal compared to other state-of-the-art algorithms.

Wireless Capacitive-Based ECG Sensing for Feature Extraction and Mobile Health Monitoring

In this paper, the concept of a wireless wearable device capable of measuring electrocardiogram (ECG) and respiration rate (RR) through the use of non-contact capacitive-based electrodes was designed and implemented. Both ECG and RR were measured using only the active electrodes and an analog conditioning circuit. The device utilizes Bluetooth low energy for low-power wireless communication to the remote server. The measured data is used to calculate heart rate variability, RR, and extract ECG related features. It was found that the use of non-contact active chest electrodes is a viable approach for measurement. The system focuses on user comfort and the minimization of the ratio of the number of wearable sensors to sensed physiological parameters.

A Nanowatt Real-Time Cardiac Autonomic Neuropathy Detector

This paper presents an electrocardiogram (ECG) processor on chip for full ECG feature extraction and cardiac autonomic neuropathy (CAN) classification. Full ECG extraction is performed using absolute value curve length transform (A-CLT) for $\text{QRS}_{\text{peak}}$ detection and using low-pass differentiation for other ECG features such as $\text{QRS}_{\text{on}}$, $\text{QRS}_{\text{off}}$, Pwave, and Twave. The proposed QRS detector attained a sensitivity of 99.37% and predictivity of 99.38%. The extracted $\text{QRS}_{\text{peak}}$ to $\text{QRS}_{\text{peak}}$ intervals (RR intervals) along with QT intervals enable CAN severity detection, which is a cardiac arrhythmia usually seen in diabetic patients leading to increased risk of sudden cardiac death. This paper presents the first hardware real-time implementation of CAN severity detector that is based on RR variability and QT variability analysis. RR variability metrics are based on mean RR interval and root mean square of standard differences of the RR intervals. The proposed architecture was implemented in 65-nm technology and consumed 75nW only at 0.6V, when operating at 250Hz. Ultralow power dissipation of the system enables it to be integrated into wearable healthcare devices.

Extraction of ECG signal with adaptive filter for hearth abnormalities detection

This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

Interview

Dr Xueyun Wei from Jiangsu University of Science and Technology, China, talks to Electronics Letters about the work behind the paper ‘Application of kernel PCA for foetal electrocardiogram estimation’, page 340. Dr Xueyun Wei I joined a team of scientific researchers who, using both advanced signal processing and detection technology, focus on biomedical signal processing and methods that can help to diagnose diseases. At the moment, my research focuses on the non-invasive detection of foetal electrocardiogram (ECG). Foetal ECG monitoring using abdominal ECG signals involves the detection of signals with low amplitudes that are produced by the foetal heartbeat. These signals are often overwhelmed by large interference and noise. The maternal ECG is the predominant source of interference, which is the most important consideration that must be accounted for when estimating foetal ECG signals. Various techniques have been employed to address foetal ECG estimation, including correlation techniques, adaptive filtering, wavelet analysis, blind source separation, neural networks and singular value decomposition. When employing an analysis method, such as principal component analysis, the powerful maternal ECG is considered to be the principal component as it dominates the foetal ECG and other background noises. Utilising the correlation between the maternal components in different lead ECG signals, allows maternal components from abdominal signals to be removed and foetal ECG to be estimated. In our Letter, we have reported a new method to estimate the antepartum foetal ECG signal using kernel principal component analysis (kPCA). We show that this method can reveal nonlinearities in the principal components of signals measured with different leads, thereby allowing the maternal components in different lead signals to be extracted. When compared to the popular independent component analysis method, experimental data from the proposed method has shown strong nonlinear analysis ability for foetal ECG estimation. The work reported in our Letter shows strong nonlinearity between the maternal components of every collected signal due to observed diversity in the signal propagation path. Kernel PCA can be considered to be a non-linear form of PCA that can extract the non-linear principal component from multi-dimensional data. Thus, this method can be applied to ECG signals that have been recorded using multiple leads to eliminate the maternal ECG components and accurately estimate foetal ECG signals. Selecting an appropriate kernel function and parameters based on multi-dimensional nonlinear data is the only challenge associated with this method. Although many signal processing methods have been used to extract foetal ECG information from the abdominal ECG signal, these methods still experience problems that limit their clinical application, such as low extraction accuracy and reliability difficulties. Most of the existing research methods use the linear mixed model or the local linear mixed model, which lack the ability to perform extensive nonlinear analysis. This work provides a new nonlinear un-mixing model for foetal ECG extraction, which could be used for clinical applications in the near future. We are planning to apply this method in appropriate technology as soon as possible. Changes in the foetal heart function are due to the regulating function of the central nervous system. Therefore, foetal heart monitoring plays a very important role in foetal disease/deficiency diagnosis. I am also studying big data issues associated with biomedical signals. At the moment, data storage capacities and modern technology has exceeded the intellectual ability of people. This issue is particularly prevalent for contemporary medical service data, which is ever-increasing. However, traditional statistical techniques and data managerial tools cannot meet current demands. The demand for quantitative diagnosis and precise treatment is increasing in modern medicine. Therefore, the application of biomedical signal processing is a field that is rapidly expanding. This field is full of challenging and innovative opportunities. The excitement it brings to researchers is like climbing Mount Qomolangma (Everest), making people full of passion and expectation.

National instruments labVIEW and video imaging technique for health status monitoring

It is essential to monitor military health status regularly for reducing the risk of cardiovascular diseases (CVD). Physiological parameters such as heart beat rate, blood pressure and electrocardiogram (ECG) features are the most vital indicators for CVD. The standard ECG machines normally used to obtain heart beat rate and ECG features in health facilities are bulky, expensive and the heart’s rhythms and activities are recorded on a piece of ECG paper which requires professional cardiologist for interpretation. In this study, National Instruments (NI) LabVIEW Biomedical Workbench with Data Acquisition Device (DAQ) is proposed as an alternative method to receive, save, handle and extract ECG features because of its simplicity, transferability and reliability.

Automatic identifying of maternal ECG source when applying ICA in fetal ECG extraction

Independent component analysis (ICA) is usually used as a preliminary step for maternal electrocardiogram (ECG) QRS detection in fetal ECG extraction. When applying ICA to do this, a troublesome problem arises from how to automatically identify the separated maternal ECG component. In this paper we proposed a method called PRCH (short for Peak to peak entropy, R-R interval entropy, Correlation coefficient and Heart rate) for the automatic identifying. In the method, we defined four kinds of features, including amplitude, instantaneous heart rate, morphology and average heart rate, to characterize a signal, and determined some decision parameters through machine learning. Experiments and comparison with other three existed methods were given. Through taking metric F1 for evaluation, it showed that the proposed PRCH method has the highest identifying accuracy and generalization capability.

A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording.

Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs) is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC) is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG) signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD) and smooth window (SW) techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications.

An Approach of Neural Network For Electrocardiogram Classification

ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.

Foetal ECG extraction by support vector regression

A new method to extract the foetal electrocardiogram (ECG) using an abdominal ECG signal and a thoracic ECG signal of the mother is presented. The nonlinear mapping of the maternal ECG component from thoracic signal to an abdominal signal is approximated by the support vector regression (SVR) method. The foetal ECG signal is then extracted by subtracting the mapped thoracic signal from an abdominal signal. In an SVR algorithm, the nonlinear regression problem in an original signal space is transformed into a linear regression problem in high-dimensional Hilbert space by kernel technology. It avoids the ‘overfitting’ problem and could obtain good results for the small sample training set. The validity of the proposed method is validated by the real ECG signals, and compared with the existing method.

Fusion of detected multi-channel maternal electrocardiogram (ECG) R-wave peak locations.

BackgroundAlmost all promising non-invasive foetal ECG extraction methods involve accurately determining maternal ECG R-wave peaks. However, it is not easy to robustly detect accurate R-wave peaks of the maternal ECG component in an acquired abdominal ECG since it often has a low signal-to-noise ratio (SNR), sometimes containing a large foetal ECG component or other noises and interferences. This paper discusses, under the condition of acquiring multi-channel abdominal ECG signals, how to improve the robustness of maternal ECG R-wave peak detection.MethodsOn the basis of summarising the current single channel ECG R-wave peak detection methods, the paper proposed a specific fusion algorithm of detected multi-channel maternal ECG R-wave peak locations. The proposed entire algorithm was then tested using two databases; one database, created by us, was composed of 343 groups of 8-channel data collected from 78 pregnant women, and the other one, called the challenge database, was from the Physionet/Computing in Cardiology Challenge 2013, including 175 groups of 4-channel data. When using these databases, each group of data was classified into two parts, called the training part and the validation test part respectively; the training part was the first 8.192 s of each group of data and the validation test part was the next 8.192 s.ResultsTo show the results, three evaluation parameters—sensitivity (Se), positive predictive value (PPV) and F1—are used. The validation test results for the database we collected are Se = 99.93 %, PPV = 99.98 %, and F1 = 99.95 %, while the results for the challenge database are Se = 99.91 %, PPV = 99.86 %, and F1 = 99.88 %.ConclusionThe results of the test show that the robustness of our proposed whole fusion algorithm was superior to that of other outstanding algorithms for maternal R-wave detection, and is much better than that of single channel maternal R-wave detection algorithms.