Comparison of neural network, ANFIS, and SVM classifiers for PVC arrhythmia detection

Gharaviri, Ali; Dehghan, F.; Teshnelab, Mohammad; Moghaddam, Hamid Abrishami

doi:10.1109/icmlc.2008.4620504

Cited by 19 publications

(8 citation statements)

References 8 publications

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“…Comparisons between the different PVC artificial networks classifier as well as the influence of some classification factors, such as features selection and the training algorithms, can be found in Refs. [57][58][59].…”

Section: Neural Network Approachesmentioning

confidence: 98%

“…In fact, RP technique had the best convergence speed as well as very good accuracy values. Another comparative study was carried out by Gharaviri et al [58]. Authors compared three classification techniques including adaptive network-based fuzzy interface system (ANFIS) classifier, neural network classifier and support vector machine (SVM) classifier.…”

Section: Algorithms Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Ectopic beats detection and correction methods: A review

Dib

Reguig

2015

Biomedical Signal Processing and Control

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Section: Neural Network Approachesmentioning

confidence: 98%

Section: Algorithms Evaluationmentioning

confidence: 99%

Ectopic beats detection and correction methods: A review

Dib

Reguig

2015

Biomedical Signal Processing and Control

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“…They may be able to capture changes in the heart rhythm, such as sinus rhythm versus fibrillation, in which the complexes exhibit different morphologies. Some works focus on time interval features to characterize the dynamics of ECG phenomena such as QRS duration, QT interval or heart rate, defined as the number of beats per unit of time [22,23,31,46,55]. Morphological features include the coefficients of the Hermite transform, the wavelet transform or the discrete cosine transform [29,32] that aim to model the ECG beat instead of extracting features from the raw data.…”

Section: Feature Extraction and Dimensionality Reductionmentioning

confidence: 99%

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Lyon

Mincholé

Martínez

et al. 2018

J. R. Soc. Interface.

175

102

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Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

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“…It is based on the vapnikchervonenkis (VC) SLT Structural Risk Minimization inductive theory and Structural Risk Minimization (SRM), based on the experience is superior to traditional Risk Minimization principle of neural network method ,so it can guarantee the solution are obtained, the global optimal solution can be a good way to solve nonlinear, small sample, the practical problems such as extra dimension [1][2][3]. It has simple structure, global optimal capability.…”

Section: Introductionmentioning

confidence: 99%

The Application of Ant Colony Algorithm in Combined Power Load Forecasting

Fan

Hong

2008

2008 International Conference on MultiMedia and Information Technology

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The ant colony algorithm also named ACO method, Artificial ants have memory function, it in the process of movement through a pheromone path to release, finally found a road from their nests to food source through the shortest, called the catalytic processes of ants, which is a positive feedback mechanism, it has good robustness, fast convergence speed, easy to get the global optimal solution. Based on random increase and nonlinear wave --residual series, grey prediction can reflect the increasing and support vector machine can show the nonlinear relationship. The improving ACO method can make the optimization weight to achieve the goal of accuracy, consistency to the prediction values, finally precision of the series can be improved obviously. Through computation of power load in a province, the experimental results show that this method can greatly improve the accuracy of the load forecasting.

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Comparison of neural network, ANFIS, and SVM classifiers for PVC arrhythmia detection

Cited by 19 publications

References 8 publications

Ectopic beats detection and correction methods: A review

Ectopic beats detection and correction methods: A review

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

The Application of Ant Colony Algorithm in Combined Power Load Forecasting

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