Feature extraction and characterization of cardiovascular arrhythmia and normal sinus rhythm from ECG signals using LabVIEW

Abstract Background: Myocardial infarction, or heart attack, is caused by a blockage of a coronary artery, which prevents blood and oxygen from accessing the heart properly. Arrhythmias are a form of CVD that refers to irregular variations in the normal heart rhythm, such as the heart beating too quickly or too slowly. Arrhythmias include Atrial Fibrillation(AF),Premature Ventricular Contraction(PVC), Ventricular Fibrillation(VF), and Tachycardia are just a few examples of arrhythmias. It aggravates if not detected and treated on time i.e., on-time /proper diagnosis of arrhythmias may minimize the risk of death. It is very labor-intensive to externally evaluate ECG signals, due to their small amplitude. Furthermore, the analysis of ECG signals is arbitrary and can differ between experts. As a consequence, a computer-aided diagnostic device that is more objective and reliable is needed. Methods: In the recent era, Machine Learning based approaches to detect arrhythmias has been established proficiently. In this view, we proposed a hybrid Deep Learning-based model to detect three types of arrhythmias on MIT-BIH arrhythmia databases. In particular, this paper makes two-fold contributions. First, we translated 1D ECG signals into 2D Scalogram images. When one-dimensional ECG signals are turned into two-dimensional ECG images, noise filtering and feature extraction are no longer necessary. This is notable since certain ECG beats are ignored by noise filtering and feature extraction. Then, based on experimental evidence, we suggest combining two models, 2D-CNN-LSTM, to detect three forms of arrhythmias: Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). Results: The experimental findings indicate that the model attained 99\% accuracy for "normal sinus rhythm," 100\% accuracy for "cardiac arrhythmias," and 99\% accuracy for "congestive heart failures," with an overall classification accuracy of 98.6\%. The sensitivity and specificity were 98.33\% and 98.35\%, respectively. The proposed model, in particular, will aid doctors in correctly detecting arrhythmia during routine ECG screening. Conclusion: As compared to the other State-of-the-art methods our proposed model outperformed and will greatly minimise the amount of intervention required by doctors.

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ECG Classification using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d6989.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 2492-2494

Keyword(s):

Heart Failure ◽

Cardiovascular Disease ◽

Congestive Heart Failure ◽

Sinus Rhythm ◽

Normal Sinus Rhythm ◽

Original Data ◽

Ecg Signals ◽

Normal Sinus ◽

Electrocardiogram Signals

Recently, the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a important part in the clinical diagnosis of cardiovascular disease. Convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features using wavelet .The classification of ARR (Arrhythmia), CHF (Congestive Heart Failure), and NSR (Normal Sinus Rhythm) signals. The experimental results contains on ARR signals from the MIT-BIH arrhythmia,CHF signals from the BIDMC Congestive Heart Failure and NSR signals from the MIT-BIH Normal Sinus Rhythm Databases show that the proposed method achieves a promising classification accuracy of 90.63%, significantly outperforming several typical ECG classification methods.

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AN EFFICIENT ALGORITHM FOR R PEAKS DETECTION OF ELECTROCARDIOGRAM SIGNALS

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519421500512 ◽

2021 ◽

Vol 21 (07) ◽

Author(s):

SHUJUAN WANG ◽

JUNFEN CHENG ◽

FANCHUANG LI ◽

YANZHONG WANG ◽

WANG LIU ◽

...

Keyword(s):

Sinus Rhythm ◽

Normal Sinus Rhythm ◽

Decision Rules ◽

Detection Algorithm ◽

Detection Methods ◽

Band Pass Filter ◽

Accurate Analysis ◽

Pass Filter ◽

Ecg Signals ◽

Normal Sinus

Efficient [Formula: see text] peaks detection is the key to the accurate analysis of electrocardiogram (ECG) signals which is a benefit to the early detection of cardiovascular diseases. In recent years, many effective [Formula: see text] peaks detection methods have been proposed, however, the false detection rate is relatively high when the noisy ECG signal is involved. Based on the property of MTEO that it could enhance the features of signal, a novel [Formula: see text] peaks detection algorithm is proposed in this paper to deal with ECG signals with low SNR. The algorithm includes two stages. In the first stage, a band-pass filter is used for eliminating noise, then the first-order forward differentiation and MTEO are used to transform the ECG signals, at last, the output of MTEO is smoothed with a Moving Averaging filter. In the second stage, the adaptive thresholds method and efficient decision rules are applied to detect the true [Formula: see text] peaks. The efficiency and robustness of the proposed method are substantiated on MIT-BIH Arrhythmia Database (MITDB), Fantasia Database and MIT-BIH Normal Sinus Rhythm Database. The testing of the proposed method on the MITDB showed the following results: Sensitivity [Formula: see text], Positive predictivity [Formula: see text] and Accuracy [Formula: see text]. On Fantasia Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. On MIT-BIH Normal Sinus Rhythm Database involvement, [Formula: see text], [Formula: see text] and [Formula: see text]. Compared with other [Formula: see text] peaks detection methods, the proposed algorithm is simple, efficient and robust.

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Nonlinear Characteristics of Electrocardiograph Signals Based on Fractal

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20183620287 ◽

2018 ◽

Vol 36 (2) ◽

pp. 287-293 ◽

Cited By ~ 2

Author(s):

Yan Liu ◽

Dongxiao Ding

Keyword(s):

Sinus Rhythm ◽

Common Property ◽

Normal Sinus Rhythm ◽

Ecg Signal ◽

Ecg Signals ◽

Complementary Method ◽

Nonlinear Properties ◽

Non Invasive ◽

Normal Sinus ◽

Fractal Spectrum

In view of the nonlinear properties of Electrocardiograph (ECG) signal, the application of fractal methods from nonlinear system theory for the analysis of ECG signals has gained increasing interest.In this study, analysis of the objects are ECG signals of four sinus rhythms. Some important phenomena and conclusions have been captured and drawn after analyzing with and plotting the graphics of multi-fractal spectrum and auto-correlation functions. Additionally, the Hurst(H) parameters illustrate that self-similarity is a common property of the ECG signals, but the smaller H of the normal sinus rhythm(NSR) cause the obvious randomness of NSR. The further research of multi-fractal spectrum shows that the ECG signals all present local singular characteristics, but there are inconsistencies in the same type of sinus rhythm ECG signal. While, the inconsistency led to obvious classification, especially in NSR. As the conclusion, the results can be used as an effective complementary method for non-invasive diagnosis and early warning of heart disease.

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Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

Scientific Reports ◽

10.1038/s41598-021-02179-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Pietro Melzi ◽

Ruben Tolosana ◽

Alberto Cecconi ◽

Ancor Sanz-Garcia ◽

Guillermo J. Ortega ◽

...

Keyword(s):

Atrial Fibrillation ◽

Sinus Rhythm ◽

Normal Sinus Rhythm ◽

Area Under The Curve ◽

Heart Rhythm ◽

Demographic Groups ◽

Ecg Signals ◽

Normal Sinus ◽

Visualization Techniques ◽

Feature Visualization

AbstractAtrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.

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