scholarly journals Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

2021 ◽  
Vol 17 (02) ◽  
pp. 106
Author(s):  
Mohand Lokman Ahmad Al-dabag ◽  
Haider Th. Salim ALRikabi ◽  
Raid Rafi Omar Al-Nima
Keyword(s):  
Atrial Fibrillation ◽  
Computational Cost ◽  
Extraction Methods ◽  
Support Vector ◽  
Ecg Signal ◽  
Heart Problem ◽  
Ecg Signals ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Low Computational Cost

One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.

scholarly journals Detection of Atrial Fibrillation Based on Optimized Electrocardiogram (Ecg) Recordings

2019 ◽  
Vol 9 (2S3) ◽  
pp. 198-203
Keyword(s):  
Atrial Fibrillation ◽  
Genetic Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Public Dataset ◽  
Electrocardiogram Ecg ◽  
Heart Arrhythmias ◽  
Typical Process

Atrial fibrillation (AF) is one among the foremost common heart arrhythmias. It is terribly tough to discover unless a precise arrhythmia episode happens throughout the exploration. If the diagnosis and the treatment is delayed the Atrial fibrillation can lead to heart strokes and causes death, therefore automatic detection of AF is an urgent need. The analysis of ECG recordings is considered as one of the typical process of detecting AF. The ECG signals analysed by considering normal rhythm (N), other arrhythmias (O) and Atrial Fibrillation(A) and noises. In this paper the proposed technique is validated by considering open accessible public dataset. In the proposed method initially pre-processing of ECG signal is performed, next extraction of features, optimizing the features using genetic algorithm (GA) and finally classifying using support vector machine (SVM) classifier. The proposed algorithm achieves overall accuracy of 95.8% and by considering top 10 features the rate of accuracy is 96.8% which is better compared to the existing algorithm with an SNR of dB. The experimental results are performed using MATLAB and uggest that by availing the short ECG recording also the detection of AF is obtained accurately.

scholarly journals Towards the Development of Nonlinear Approaches to Discriminate AF from NSR Using a Single-Lead ECG

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 531
Author(s):  
Jieun Lee ◽  
Yugene Guo ◽  
Vasanth Ravikumar ◽  
Elena G. Tolkacheva
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Multiscale Entropy ◽  
Short Term ◽  
Embedding Method ◽  
Ecg Signals ◽  
Normal Sinus ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Insight Into

Paroxysmal atrial fibrillation (Paro. AF) is challenging to identify at the right moment. This disease is often undiagnosed using currently existing methods. Nonlinear analysis is gaining importance due to its capability to provide more insight into complex heart dynamics. The aim of this study is to use several recently developed nonlinear techniques to discriminate persistent AF (Pers. AF) from normal sinus rhythm (NSR), and more importantly, Paro. AF from NSR, using short-term single-lead electrocardiogram (ECG) signals. Specifically, we adapted and modified the time-delayed embedding method to minimize incorrect embedding parameter selection and further support to reconstruct proper phase plots of NSR and AF heart dynamics, from MIT-BIH databases. We also examine information-based methods, such as multiscale entropy (MSE) and kurtosis (Kt) for the same purposes. Our results demonstrate that embedding parameter time delay ( τ ), as well as MSE and Kt values can be successfully used to discriminate between Pers. AF and NSR. Moreover, we demonstrate that τ and Kt can successfully discriminate Paro. AF from NSR. Our results suggest that nonlinear time-delayed embedding method and information-based methods provide robust discriminating features to distinguish both Pers. AF and Paro. AF from NSR, thus offering effective treatment before suffering chaotic Pers. AF.

scholarly journals P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

2020 ◽  
Vol 10 (3) ◽  
pp. 976
Author(s):  
Rana N. Costandy ◽  
Safa M. Gasser ◽  
Mohamed S. El-Mahallawy ◽  
Mohamed W. Fakhr ◽  
Samir Y. Marzouk
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
P Wave ◽  
Ecg Signal ◽  
P Waves ◽  
Ecg Signals ◽  
Wave Detection ◽  
Automated Algorithm ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

scholarly journals Classification of Heart Rate Time Series Using Machine Learning Algorithms

2021 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Heart Patients ◽  
Extraction Step ◽  
Ecg Arrhythmia ◽  
Electrocardiogram Ecg ◽  
The Given

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.

A Fourier-Bessel Expansion-Based Method for Automated Detection of Atrial Fibrillation From Electrocardiogram Signals

2019 ◽  
pp. 248-277
Author(s):  
Ashish Sharma ◽  
Shivnarayan Patidar
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Signal ◽  
Clinical Environment ◽  
Test Dataset ◽  
Ecg Signals ◽  
Rr Interval ◽  
Electrocardiogram Signals ◽  
Detection And Identification ◽  
Bagged Decision Trees ◽  
Electrocardiogram Ecg

This chapter presents a new methodology for detection and identification of cardiovascular diseases from a single-lead electrocardiogram (ECG) signal of short duration. More specifically, this method deals with the detection of the most common cardiac arrhythmia called atrial fibrillation (AF) in noisy and non-clinical environment. The method begins with appropriate pre-processing of ECG signals in order to get the RR-interval and heart rate (HR) signals from them. A set of indirect features are computed from the original and the transformed versions of RR-interval and HR signals along with a set of direct features that are obtained from ECG signals themselves. In all, 47 features are computed and subsequently they are fed to an ensemble system of bagged decision trees for classifying the ECG recordings into four different classes. The proposed method has been evaluated with 2017 PhysioNet/CinC challenge hidden test dataset (phase II subset) and the final F1 score of 0.81 is obtained.

Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification

2017 ◽  
Vol 30 ◽  
pp. 1-11 ◽  
Author(s):  
Khudhur A. Alfarhan ◽  
Mohd Yusoff Mashor ◽  
Abdul Rahman Mohd Saad ◽  
Hayder A. Azeez ◽  
Mustafa M. Sabry
Keyword(s):  
Feature Extraction ◽  
Autoregressive Model ◽  
Research Work ◽  
Window Size ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signals ◽  
Library Support ◽  
Electrocardiogram Ecg

Arrhythmia, a common form of heart disease, can be detected from an electrocardiogram (ECG) signal. This research work presents a comparative study between five feature extraction methods applied separately on two window sizes for detecting three ECG pulse types, namely normal and two arrhythmia variations. The library support vector machine (LIBSVM) was used to classify the three classes of the ECG pulses. The ECG signals were obtained from MIT-BIH database. The ECG dataset was normalized and filtered to remove any noise and after that the signals were windowed into two window sizes (long window and short window). Five approaches were used to extract the features from the ECG signals. These approaches are scalar Autoregressive model coefficients, Haar discrete wavelet transform (DWT), Daubechies (db) DWT, Biorthogonal (bior) DWT, and principal components analysis (PCA). Each approach was applied separately on the two window sizes. The results of the classification show that scalar Autoregressive model coefficients, Haar, db, and bior are better approaches to catch the ECG features for short window than the long window. However, PCA gave the closest and highest results for the two window sizes than other approaches. That mean the PCA is the better feature extraction approach for both window sizes.

scholarly journals The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

2021 ◽  
Vol 11 (13) ◽  
pp. 5908
Author(s):  
Raquel Cervigón ◽  
Brian McGinley ◽  
Darren Craven ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Atrial Fibrillation ◽  
Characteristic Curve ◽  
Set Partitioning ◽  
Constrained Systems ◽  
Battery Life ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Signal Compression ◽  
Ecg Signals ◽  
Cardiac Devices

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.

scholarly journals Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1568
Author(s):  
Junmo Kim ◽  
Geunbo Yang ◽  
Juhyeong Kim ◽  
Seungmin Lee ◽  
Ko Keun Kim ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Physical State ◽  
Acceptance Rate ◽  
Support Vector ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
Ecg Signals ◽  
Using Data ◽  
False Acceptance

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

scholarly journals ECG Beats Classification Using Mixture of Features

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Manab Kumar Das ◽  
Samit Ari
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Ectopic Beat ◽  
Extraction Techniques ◽  
Efficient System ◽  
Ecg Signals ◽  
Temporal Features ◽  
S Transform ◽  
Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

scholarly journals Two Electrode ECG And EOG System For Monitoring Applications

2019 ◽  
Vol 8 (11) ◽  
pp. 262-265
Keyword(s):  
Reference Electrode ◽  
Cost Effective ◽  
Electrode System ◽  
Ecg Signal ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Arm Position ◽  
The Stability ◽  
Electrocardiogram Ecg ◽  
Total Gain

Heart and Eye are two vital organs in the human system. By knowing the Electrocardiogram (ECG) and Electro-oculogram (EOG), one will be able to tell the stability of the heart and eye respectively. In this project, we have developed a circuit to pick the ECG and EOG signal using two wet electrodes. Here no reference electrode is used. EOG and ECG signals have been acquired from ten healthy subjects. The ECG signal is obtained from two positions, namely wrist and arm position respectively. The picked-up biomedical signal is recorded and heart rate information is extracted from ECG signal using the biomedical workbench. The result found to be promising and acquired stable EOG and ECG signal from the subjects. The total gain required for the arm position is higher than the wrist position for the ECG signal. The total gain necessary for the EOG signal is higher than the ECG signal since the ECG signal is in the range of millivolts whereas EOG signal in the range of microvolts. This two-electrode system is stable, cost-effective and portable while still maintaining high common-mode rejection ratio (CMRR).

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