SVM based methods for arrhythmia classification in ECG

Kohli, Narendra; Verma, Nishchal K.; Roy, Abhishek

doi:10.1109/iccct.2010.5640480

Cited by 39 publications

(13 citation statements)

References 8 publications

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“…Methods such as support vector machines (SVM) have been tested to classify ECG arrhythmia detection. Kohli et al [ 8 ] compared three popular SVM algorithms, one-against-one, one-against-all, and fuzzy decision function, and finally concluded that the one-against-one method performs better results when distinguishing the cardiac arrhythmia and grouping them into the correct class. Considering that the artificial neural network (ANN) has the flaw of converging to a local minimum and is prone to overfitting, Walsh [ 9 ] used the support vector machine algorithm to classify the ECG data as it tends towards an optimal margin separation, as the search space constraints define a convex set.…”

Section: Literature Reviewmentioning

confidence: 99%

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

Zhang

Chen

et al. 2021

Journal of Healthcare Engineering

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The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors’ consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.

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Section: Literature Reviewmentioning

confidence: 99%

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

Zhang

Chen

et al. 2021

Journal of Healthcare Engineering

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“…Furthermore, ANOVA was used to select important characteristics for the classification of heart beats, which outperformed by using 10 fold cross validation with an accuracy of 98.49%. Nasiri et al [27] proposed SVM classifier for the classification of arrhythmias and further SVM is optimized by genetic algorithm by applying best parameter for tuning discriminator function that helps in optimizing SVM.Besides above literature [28] [29][30] also applied SVM for arrhythmia classification. Kumaraswamy et al [31]MIT-BIH arrhythmia database was used to propose a classifier for the classification of heartbeats that are useful for the detection of arrhythmias using Random Forest Tree classifier and Discrete Cosine Transform (DCT) for discovering R-R intervals as features.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning based approach for ECG Heartbeat Arrhythmia Classification

Madan

Singh

et al. 2021

Preprint

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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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“…Contemporary work over the ECG datasets is carried out in [41] using the SVM based methods for detection of arrhythmia with a selection of features based on the principal component analysis [41], [42]. An effective model of classification for arrhythmia patients was proposed that relies on SVM and KNN for handling training model and to enhance accuracy measure, which is attained based on the combination of F-score and SFS (Sequential forward search) in handling the selection features [43].…”

Section: Related Workmentioning

confidence: 99%

Regression Heuristics by Optimal Tridimensional Features of Electrocardiogram for Arrhythmia Detection

2019

IJEAT

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Computer aided predictive analytics are vital in noncommunicable diseases. In particular, early diagnosis of arrhythmia (heart related disease) is crucial to prevent sudden deaths due to heart failure. The critical context to prevent deaths caused by arrhythmia is early prediction of the arrhythmia scope. The clinical experts widely consider the Electro Cardio Gram (ECG) report as primary parameter to scale the scope of arrhythmia. However, the diagnosis accuracy of clinical experts is highly correlate on their expertise. Unlike the other domains, the sensitivity that is the accuracy in disease-prone is very much crucial in clinical practices. Particularly, the accuracy and sensitivity are more vital in computer-aided heart disease prediction methods. Hence, the recent research contributions are quantifying the possibilities of optimizing machine-learning approaches to achieve significance in computer-aided methods to perform predictive analysis on arrhythmia detection. Regarding this context, this manuscript is defining a Regression Heuristics by Tridimensional Features of the electrocardiogram reports, which has intended to perform arrhythmia prediction. The experimental study evincing the significance of the proposed model that scaled against the contemporary methods.

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SVM based methods for arrhythmia classification in ECG

Cited by 39 publications

References 8 publications

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

Hybrid Deep Learning based approach for ECG Heartbeat Arrhythmia Classification

Regression Heuristics by Optimal Tridimensional Features of Electrocardiogram for Arrhythmia Detection

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