Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

Javadi, Mehrdad; Ebrahimpour, Reza; Sajedin, Atena; Faridi, Soheil; Zakernejad, Shokoufeh

doi:10.1371/journal.pone.0024386

Cited by 49 publications

(20 citation statements)

References 53 publications

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“…With classifier ensembles, this instability can be used to improve the robustness of heartbeat classification. Ensemble learning techniques are commonly used in pattern classification, and they have also been applied in heartbeat classification [29-32]. To achieve satisfactory ensemble classification, the component classifiers used need to perform well and vary a great deal [33].…”

Section: Discussionmentioning

confidence: 99%

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

et al. 2014

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BackgroundThe inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB).MethodsBased on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated.ResultsResults showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast.ConclusionsA hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice.

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Section: Discussionmentioning

confidence: 99%

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

et al. 2014

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“…Better performance depends on features and the methods of classification, early approaches are mostly based on Artificial Neural Networks (ANNs) [4], which have been used in a great number of medical diagnostic decision systems [5,6]. The most popular neural network is Multi-Layer Perceptron (MLP), fuzzy logic [7], Support Vector Machines SVMs used for Examining feature extraction techniques for ECG classification, Ant colony optimization, k-nearest neighbor [8].…”

Section: Related Workmentioning

confidence: 99%

New Hybrid (SVMs -CSOA) Architecture for classifying Electrocardiograms Signals

Abed¹,

Alasad²

2015

ijarai

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Abstract-a medical test that provides diagnostic relevant information of the heart activity is obtained by means of an ElectroCardioGram (ECG). Many heart diseases can be found by analyzing ECG because this method with moral performance is very helpful for shaping human heart status. Support Vector Machines (SVM) has been widely applied in classification. In this paper we present the SVM parameter optimization approach using novel metaheuristic for evolutionary optimization algorithms is Cat Swarm Optimization Algorithm (CSOA). The results obtained assess the feasibility of new hybrid (SVMs -CSOA) architecture and demonstrate an improvement in terms of accuracy.

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“…Electrocardiogram (ECG), as an adjunct tool in cardiovascular diseases management, is used to non-invasively monitor the electrical activity of the heart [2]. To capture frequent occurrence of arrhythmias, medical practitioners record ECG activity for several hours.…”

Section: Introductionmentioning

confidence: 99%

“…Classifier combination is also used in ECG heartbeat classification to improve accuracy [5]. The final decision regarding classifier combination is achieved by considering the decisions of members or aggregating the decisions of one or a few of the members [2].…”

Section: Introductionmentioning

confidence: 99%

Fast multi-scale feature fusion for ECG heartbeat classification

Yang

Wang

et al. 2015

EURASIP J. Adv. Signal Process.

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Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and t-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.

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Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

Cited by 49 publications

References 53 publications

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

New Hybrid (SVMs -CSOA) Architecture for classifying Electrocardiograms Signals

Fast multi-scale feature fusion for ECG heartbeat classification

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