scholarly journals Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning

2021 ◽  
Author(s):  
Yunendah Nur Fuadah ◽  
Ki Moo Lim
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Feature Extraction ◽  
Selection Process ◽  
Normal Sinus Rhythm ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Cardiovascular Disorders ◽  
Ecg Signals

Abstract Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the major causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on electrocardiogram (ECG) signals. Therefore, extracting significant features from ECG signals is the most challenging aspect to represent each condition of the ECG signals. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm that has the capability of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, owing to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this important gap by applying a discrete wavelet transform (DWT) prior to applying the Hjorth descriptor as a feature extraction method. Furthermore, the feature selection process and optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), and artificial neural network (ANN), were investigated to provide the best system performance. This study obtained accuracies of 95 %, 92 %, and 95 % for the k-NN, SVM, and ANN classifiers, respectively. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.

scholarly journals Classification of Atrial Fibrillation and Congestive Heart Failure using Convolutional Neural Network with Electrocardiogram

2021 ◽  
Author(s):  
Yunendah Nur Fu’adah ◽  
Ki Moo Lim
Keyword(s):  
Neural Network ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Convolutional Neural Network ◽  
Classification System ◽  
Normal Sinus Rhythm ◽  
Delayed Diagnosis ◽  
Ecg Signals

Abstract Delayed diagnosis of atrial fibrillation (AF) and congestive heart failure (CHF) can lead to death. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. Several studies have reported promising results using the automatic classification of ECG signals. The performance accuracy needs to be improved considering that an accurate classification system of AF and CHF has the potential to save a patient’s life. An optimal ECG signal classification system for AF and CHF has been proposed in this study using a one-dimensional convolutional neural network (1-D CNN) to improve the performance. A total of 150 datasets of ECG signals were modeled using the1-D CNN. The proposed 1-D CNN algorithm, provided precision values, recall, f1-score, accuracy of 100%, and successfully classified raw data of ECG signals into three conditions, which are normal sinus rhythm (NSR), AF, and CHF. The results showed that the proposed method outperformed the previous methods. This approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

scholarly journals Abnormal ECG Classification using Empirical Mode Decomposition and Entropy

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Suci Aulia ◽  
Sugondo Hadiyoso
Keyword(s):  
Atrial Fibrillation ◽  
Feature Extraction ◽  
Left Bundle Branch Block ◽  
Normal Sinus Rhythm ◽  
Heart Defects ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Specificity And Sensitivity

Heart disease is one of the leading causes of death in the world. Early detection followed by therapy is one of the efforts to reduce the mortality rate of this disease. One of the leading medical instruments for diagnosing heart disorders is the electrocardiogram (ECG). The shape of the ECG signal represents normal or abnormal heart conditions. Some of the most common heart defects are atrial fibrillation and left bundle branch block. Detection or classification can be difficult if performed visually. Therefore in this study, we propose a method for the automatic classification of ECG signals. This method generally consists of feature extraction and classification. The feature extraction used is based on information theory, namely Fuzzy entropy and Shannon entropy, which is calculated on the decomposed signal. The simulated ECG signals are of three types: normal sinus rhythm, atrial fibrillation, and left bundle branch block. Support vector machine and k-Nearest Neighbor algorithms were employed for the validation performance of the proposed method. From the test results obtained, the highest accuracy is 81.1%. With specificity and sensitivity of 79.4% and 89.8%, respectively. It is hoped that this proposed method can be further developed to assist clinical diagnosis.

Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure

2016 ◽  
Author(s):  
Syed Hassan Zaidi ◽  
Imran Akhtar ◽  
Syed Imran Majeed ◽  
Tahir Zaidi ◽  
Muhammad Saif Ullah Khalid
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Sinus Rhythm ◽  
Time Series Data ◽  
Normal Sinus Rhythm ◽  
Series Data ◽  
Normal Sinus

This paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.

scholarly journals ECG Classification using Machine Learning

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.

Robust correlation dimension estimator for heart rate variability

2020 ◽  
Vol 15 (16) ◽  
pp. 62-68
Author(s):  
A.V. Martynenko ◽  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Sinus Rhythm ◽  
Correlation Dimension ◽  
Normal Sinus Rhythm ◽  
Robust Estimator ◽  
Normal Sinus ◽  
Significant Difference ◽  
Limited Length

Introduction. Non-linear methods of analysis have found widespread use in the Heart Rate Variability (HRV) technology, when the long-term HRV records are available. Using one of the effective nonlinear methods of analysis of HRV correlation dimension D2 for the standard 5-min HRV records is suppressed by unsatisfactory accuracy of available methods in case of short records (usually, doctors have about 500 RRs during standard 5-min HRV record), as well as complexity and ambiguity of choosing additional parameters for known methods of calculating D2. The purpose of the work. Building a robust estimator for calculating correlation dimension D2 with high accuracy for limited se-ries of RR-intervals observed in a standard 5-minute HRV record, i. e. with N  500. As well as demonstrating the capabilities of the D2 formula on a well known attractors (Lorenz, Duffing, Hennon and etc.) and in applications for Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF) and Atrial Fibrillation (AF). Materials and Methods. We used MIT-BIH long-term HRV records for normal sinus rhythm, congestive heart failure and atrial fibrillation. In order to analyze the accuracy of new robust estimator for D2, we used the known theoretical values for some famous attractors (Lorenz, Duffing, Hennon and etc.) and the most popular Grassberger-Procaccia (G-P) algorithm for D2. The results of the study. We have shown the effectiveness of the developed D2 formula for time series of limited length (N = 500–1000) by some famous attractors (Lorenz, Duffing, Hennon and etc.) and with the most popular Grassberger-Procaccia (G-P) algorithm for D2. It was demonstrated statistically significant difference of D2 for normal sinus rhythm and congestive heart failure by standard 5 min HRV segments from MIT-BIH database. The promised technology for early prediction of atrial fibrillation episodes by current D2 algorithm was shown for standard 5 min HRV segments from MIT-BIH Atrial Fibrillation database. Conclusion. Robust correlation dimension D2 estimator suggested in the article allows for time series of limited length (N ≈ 500) to calculate D2 value that differs at mean from a precise one by 5 ± 4%, as demonstrated for various well known attractors (Lorenz, Duffing, Hennon and etc.). We have shown on the standard 5-min segments from MIT-BIH database of HRV records: - the statistically significant difference of D2 for cases of normal sinus rhythm and congestive heart failure; - D2 drop significantly for the about 30 min. before of AF and D2 growth drastically under AF there was shown for HRV records with Atrial Fibrillation (AF) episodes. The suggested robust correlation dimension D2 estimator is perfect suitable for real time HRV monitoring as accurate, fast and non-consuming for computing resources. Key words: Hearth rate variability; Correlation dimension; Congestive heart failure; Atrial fibrillation.

scholarly journals A Slantlet based Statistical Features Extraction for Classification of Normal, Arrhythmia, and Congestive Heart Failure in Electrocardiogram

2021 ◽  
Vol 5 (1) ◽  
pp. 71-81
Author(s):  
Sawza Saadi Saeed ◽  
Raghad Zuhair Yousif
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Heart Disease ◽  
Computer Aided Design ◽  
Normal Sinus Rhythm ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
P Value ◽  
Statistical Features

Intelligent and automated systems for diagnosing heart disease play a key role in treatment of heart disease and hence mitigating the possibility of heart disease, heart failure or sudden death. Thus, a Computer-Aided Design CAD system can provide a doctors with a clue about the category of patient heart disease, which might be Normal Sinus Rhythm, Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF) electrocardiogram (ECG) signal. In this work a novel Slantlet transform (SLT) statistical features have been extracted and selected for 900 ECG segments taken from MIT-BIH ARR Database equally from three classes mentioned above for heart dieses classification through ECG signals. Based on the superiority of SLT in time localization as compared to the traditional wavelet transform, 12 out of 14 statistical features have been successfully passed the ANOVA test with P-value of 10−3. Then after, the relevant features are provided to three well-known classifiers (Support Vector Machine [SVM], K-nearest neighbor, and Naive Bayes). The performance tests show that Attaining 99.254% classification average AUC it can be achieved using SLT transform based features along with SVM classifier, which is a set of related supervised machine learning algorithm used for regression and classification with high generalization ability. It performs classification on two group problems. SVM classifier determines the best hyperplane which distinguishes between each positive and negative training sample.

scholarly journals Classification of ECG signals for detection of arrhythmia and congestive heart failure based on continuous wavelet transform and deep neural networks

2021 ◽  
Vol 22 (3) ◽  
pp. 1520
Author(s):  
Rashidah Funke Olanrewaju ◽  
S. Noorjannah Ibrahim ◽  
Ani Liza Asnawi ◽  
Hunain Altaf
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Deep Neural Networks ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Continuous Wavelet ◽  
Ecg Signals ◽  
Normal Sinus ◽  
The Common ◽  
2D Images

According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.

scholarly journals Automatic detection of Atrial Fibrillation

2021 ◽  
Author(s):  
◽  
Greg Hayes
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Health Sector ◽  
Wearable Device ◽  
P Wave ◽  
Support Vector ◽  
Wave Form ◽  
Discrete Wavelet ◽  
Rr Interval ◽  
The Individual

<p>Atrial Fibrillation is an abnormal arrhythmia of the heart and is a growingconcern in the health sector affecting 1% of the population. The incidenceof atrial fibrillation increases with age and has been found to be more detri-mental to long term cardiac health than previously thought. Sufferers arefive times more likely to experience a stroke than others. Often, atrial fib-rillation is asymptomatic and is frequently discovered only when a patient visits a hospital for other reasons. The detection of paroxysmal atrial fib-rillation can be difficult. Holter monitors are used to record the ECG overlong periods of time, but the resulting recording still needs to be analysed.This can be a time consuming task and one prone to errors. If a miniature,low-power, wearable device could be designed to detect and record whena heart experiences atrial fibrillation, then health professionals would havemore timely information to carry out better, more cost effective courses of treatment. This thesis presents progress towards development of such a device. Atrial fibrillation is characterised by random RR interval, missing Pwave and presence of atrial activity. The detection of the P wave and atrialactivity can be unreliable due to low signal levels and differences in wave-form morphology between subjects. The random RR interval appears tobe a more reliable method of detection. By analysing the ECG signal inboth the frequency and time domains, feature sets can be extracted for thedetection process. In this research, the Discrete Wavelet Transform is used to generate several sub-bands for analysing wave form morphology, and anumber of RR interval metrics are created for analysing the rhythm. All features are further processed and presented to a support vector machine classification stage for the ultimate detection of atrial fibrillation. Forty eight files from the MITDB database of the PhysioNet online ECG reposi-tory were downloaded and processed to form separate training and test-ing data sets. Overall classification accuracy for normal sinus rhythm was93% sensitivity and 95% specificity, and for atrial fibrillation, 95% sensitiv-ity and 93% specificity. These results were found to be sensitive to the ECG morphology of the individual subjects. This means that the system either needs to be trained on a greater number of ECG morphologies or perhaps trained on the morphology of the individual under investigation. Putting this issue aside, the research to date shows that it is reasonable to expect a small, low powered, wearable device, to be capable of automatically detecting whena heart experiences atrial fibrillation.</p>

scholarly journals Automatic detection of Atrial Fibrillation

2021 ◽  
Author(s):  
◽  
Greg Hayes
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Health Sector ◽  
Wearable Device ◽  
P Wave ◽  
Support Vector ◽  
Wave Form ◽  
Discrete Wavelet ◽  
Rr Interval ◽  
The Individual

<p>Atrial Fibrillation is an abnormal arrhythmia of the heart and is a growingconcern in the health sector affecting 1% of the population. The incidenceof atrial fibrillation increases with age and has been found to be more detri-mental to long term cardiac health than previously thought. Sufferers arefive times more likely to experience a stroke than others. Often, atrial fib-rillation is asymptomatic and is frequently discovered only when a patient visits a hospital for other reasons. The detection of paroxysmal atrial fib-rillation can be difficult. Holter monitors are used to record the ECG overlong periods of time, but the resulting recording still needs to be analysed.This can be a time consuming task and one prone to errors. If a miniature,low-power, wearable device could be designed to detect and record whena heart experiences atrial fibrillation, then health professionals would havemore timely information to carry out better, more cost effective courses of treatment. This thesis presents progress towards development of such a device. Atrial fibrillation is characterised by random RR interval, missing Pwave and presence of atrial activity. The detection of the P wave and atrialactivity can be unreliable due to low signal levels and differences in wave-form morphology between subjects. The random RR interval appears tobe a more reliable method of detection. By analysing the ECG signal inboth the frequency and time domains, feature sets can be extracted for thedetection process. In this research, the Discrete Wavelet Transform is used to generate several sub-bands for analysing wave form morphology, and anumber of RR interval metrics are created for analysing the rhythm. All features are further processed and presented to a support vector machine classification stage for the ultimate detection of atrial fibrillation. Forty eight files from the MITDB database of the PhysioNet online ECG reposi-tory were downloaded and processed to form separate training and test-ing data sets. Overall classification accuracy for normal sinus rhythm was93% sensitivity and 95% specificity, and for atrial fibrillation, 95% sensitiv-ity and 93% specificity. These results were found to be sensitive to the ECG morphology of the individual subjects. This means that the system either needs to be trained on a greater number of ECG morphologies or perhaps trained on the morphology of the individual under investigation. Putting this issue aside, the research to date shows that it is reasonable to expect a small, low powered, wearable device, to be capable of automatically detecting whena heart experiences atrial fibrillation.</p>

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