A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification

Moavenian, Majid; Khorrami, Hamid

doi:10.1016/j.eswa.2009.09.021

Cited by 137 publications

(42 citation statements)

References 21 publications

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“…The first set is composed of all heartbeats of records: 101,106,108,109,112,114,115,116,118,119,122,124,201,203,205,207,208,209,215, 220, 223 and 230, called Dataset 1 (DS1). While the second is composed of all heartbeats of records: 100,103,105,11,113,117,121,123,200,202,210,212,213,214,219,221,222,228,231,232,233 and 234, called Dataset 2 (DS2).…”

Section: Heartbeats Selection Problem For Evaluation Of Methodsmentioning

confidence: 99%

“…Huang et al [42] used the SVM in a hierarchical manner with a maximum voting strategy and report significantly improvements. [119] proposed the use of a new kernel function for capturing data from SVM. In that work, it was used the same methodology for comparing the results obtained from a SVM and a Multilayer Perceptron Artificial Neural Network (MLP-ANN).…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

See 1 more Smart Citation

ECG-based heartbeat classification for arrhythmia detection: A survey

Luz

Schwartz

Cámara-Chávez

et al. 2016

Computer Methods and Programs in Biomedicine

734

340

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An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.

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Section: Heartbeats Selection Problem For Evaluation Of Methodsmentioning

confidence: 99%

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

ECG-based heartbeat classification for arrhythmia detection: A survey

Luz

Schwartz

Cámara-Chávez

et al. 2016

Computer Methods and Programs in Biomedicine

734

340

View full text Add to dashboard Cite

show abstract

“…SVM employs structural risk minimization (SRM) principle to achieve better generalization ability, so that it provides higher performance than traditional empirical risk minimization (ERM) based learning machines, e.g., neural networks [28]. There are two main categories for SVM, which are support vector classification (SVC) and SVR.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

Chen

Liu

et al. 2015

Int J of Soc Robotics

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An intention understanding model based on twolayer fuzzy support vector regression is proposed in humanrobot interaction, where fuzzy c-means clustering is used to classify the input data, and intention understanding is mainly obtained by emotion, with identification information such as age, gender, and nationality. It aims to realize the transparent communication by understanding customers' order intentions at a bar, in such a way that the social relationship between bar staffs and customers becomes smooth. To demonstrate the aptness of intention understanding model, experiments are designed in term of relationship between emotion-age-gender-nationality and order intention. Results show that the proposal obtains an intention understanding accuracy of 70 %/72 %/80 % with clusters number C = 2/3/6 (according to different genders/ages/nationalities), which is 23 %/26 %/33 % and 35.5 %/37.5 %/45.5 % higher than that of support vector regression (SVR) and back propagation neural networks (BPNN), respectively; the computational time of proposal is about 0.976 s/0.935 s/0.67 s with clusters number C = 2/3/6, while 1.889 s for SVR and 3.505 s for BPNN. Additionally, the preliminary application experiment is performed in the developing human-robot interaction system, called mascot robot system, where the experiment is performed in a scenario of "drinking at a bar", result shows that the bar lady robot obtains an accuracy of 77.8 % for understanding customers' order intentions and receives a satisfaction evaluation of "satisfied". According to the preliminary application, the proposal is being extended to an ordering system in the bar for business communication.

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“…Among these methods, neural networks have been treated as a powerful classifier to deal with ECG arrhythmia classification problems [18]. For example, Haseena et al [11] proposed a fuzzy C-mean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats and the overall classification accuracy of 97.54% was achieved.…”

Section: Introductionmentioning

confidence: 99%