Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm
Neural Computing and ApplicationsPublished 13 May 2005
Nurettin Acır
Citations81
SJR quartileQ1
SJR score1.10
SNIP1.61
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Experimental results show that not only the fast L SSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than thestandard backpropagation multilayer perceptron network.
Abstract
In this paper, we present a new system for the classification of electrocardiogram (ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network.
Keywords
Computer ScienceMedicineNeuroscience
Neural Networks: A Comprehensive Foundation
29,816 Citations1998Simon Haykin
Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Computers in PhysicsTen Lectures on Wavelets
15,386 Citations1992Ingrid Daubechies, Christopher Heil
This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Adaptive Filter Theory
12,680 Citations1986S. Haykin
This work focuses on Adaptive Filters Using Infinite-Duration Impulse Response Structures, a Stochastic Processes and Models that combines Kalman Filters as the Unifying Bases for RLS Filters with Estimation Theory and Complex Variables.
Neural Processing LettersLeast Squares Support Vector Machine Classifiers
9,375 Citations1999Johan A. K. Suykens, Joos Vandewalle
A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
New England Journal of MedicinePrimer on Certain Elements of Medical Decision Making
1,160 Citations1975Barbara J. McNeil, Emmett Keeler +1 more
Principals of statistical decision theory and information theory suggest technics for objectively determining these cutoff points, depending upon whether the physician is concerned with health costs, with financial costs, or with the information content of the test.
Proceedings of the IEEEA review of wavelets in biomedical applications
739 Citations1996Michaël Unser, Akram Aldroubi
The wavelet properties that are the most important for biomedical applications are described and an interpretation of the the continuous wavelet transform (CWT) as a prewhitening multiscale matched filter is provided.
IEEE Transactions on Biomedical EngineeringECG beat recognition using fuzzy hybrid neural network
516 Citations2001S. Osowski, Trần Hoài Linh
The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution and show that the method may find practical application in the recognition and classification of different type heart beats.
IEEE Transactions on Biomedical EngineeringReal-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network
465 Citations1999Kentaro Minami, Hiroyuki Nakajima +1 more
The authors' method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat achieved high sensitivity and specificity in discrimination of supraventricular rhythms from ventricular ones.
Biomedical Signal Processing
263 Citations2021Iyad Obeid, Ivan Selesnick +1 more
International Journal of Medical InformaticsECG pattern recognition and classification using non-linear transformations and neural networks: A review
235 Citations1998Nicos Maglaveras, T. Stamkopoulos +3 more
A generalised approach to the classification problems in n-dimensional spaces will be presented using among others NN, radial basis function networks (RBFN) and non-linear principal component analysis (NLPCA) techniques.
Pattern RecognitionClassification of heart rate data using artificial neural network and fuzzy equivalence relation
229 Citations2002U. Rajendra Acharya, P. Subbanna Bhat +3 more
The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification, and the same data is also used for fuzzy equivalence classifier.
Computer Methods and Programs in BiomedicineECG beat classification by a novel hybrid neural network
211 Citations2001Zümray Dokur, Tamer Ölmez
Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 96% by using the hybrid structure.
International Journal of Systems ScienceFeature extraction from ECG signals using wavelet transforms for disease diagnostics
172 Citations2002Sahaj Saxena, Vinod Kumar +1 more
A modified combined wavelet transform technique that has been developed to analyse multilead electrocardiogram signals for cardiac disease diagnostics and two alternate diagnostic criteria have been used to check the diagnostic authenticity of the test results.
IEEE/SP 13th Workshop on Statistical Signal Processing, 200520 - Biomedical Signal Processing
156 Citations2005Metin Akay, José M. F. Moura
Computer Methods and Programs in BiomedicineAutomatic pattern recognition in ECG time series
117 Citations2002Karsten Sternickel
Neural Networks are trained with the wavelet transformed templates providing an efficient detector even for temporally varying patterns within the complete time series, which solves the problem of automatic P wave detection in Holter ECG recordings.
Pattern Recognition LettersEfficient computations for large least square support vector machine classifiers
110 Citations2002Kok Seng Chua
The linear system in the training of the least square support vector machine (LSSVM) proposed by Suykens and Vandewalle can be placed in a more symmetric form so that for a data set with N data points and m features, the linear system can be solved by inverting an m × m instead of an N × N matrix and storing and working with matrices of size at most m × N.
IEEE Engineering in Medicine and Biology MagazineEvaluating arrhythmias in ECG signals using wavelet transforms
106 Citations2000Paul S. Addison, James N. Watson +4 more
An energy-based method of interrogating the ECG in VF using high-resolution, log-scale continuous wavelet plots and underlying structures within the VF waveform are made visible in the wavelet time-scale half space.
IEEE Transactions on Information Technology in BiomedicineA novel large-memory neural network as an aid in medical diagnosis applications
101 Citations2001Hubert Kordylewski, Daniel Graupe +1 more
It is shown that the LAMSTAR network is hundreds, and even thousands, times faster in its training than backpropagation-based networks when used for the same problem with exactly the same information.
Artificial Intelligence in MedicineModel selection for a medical diagnostic decision support system: a breast cancer detection case
92 Citations2000David West, Vivian West
This paper illustrates the use of a self-organizing map (SOM) to guide model selection for a breast cancer MDSS and the topological ordering properties of the SOM are used to define targets for an ideal accuracy level similar to a Bayes optimal level.
Pattern RecognitionKnowledge-based ECG interpretation: a critical review
84 Citations2000Mahantapas Kundu, Mita Nasipuri +1 more
A fuzzy-logic-based approach and a domain-dependent control algorithm are discussed to show how the production level parallelism can be exploited to reduce the length of the match–resolve–act cycle of a rule based ECG interpretation system.
IEEE Transactions on Signal ProcessingAllpass filter design and applications
80 Citations1998Markus Lang
Low computational cost classifiers for ECG diagnosis using neural networks
25 Citations2002Branko G. Celler, Philip de Chazal
This work investigates and compares a number of computationally efficient classifiers for categorising the Frank lead ECG as normal or one of six disease conditions using a neural network expert system and results are comparable to those derived from neural nets trained with over 229 scalar parameters.
Development of Qrs Detection Method for Real-Time Ambulatory Cardiac Monitor
22 Citations1997Ming-Yao Yang, Wei-Chih Hu +1 more
An integrated system for ECG diagnosis that combines the wavelet transform (WT) for feature extraction and artificial neural network (ANN) models for the classification is proposed and the result indicates that the accuracy of diagnosing cardiac disease is above 97.77%.
Selecting a neural network structure for ECG diagnosis
18 Citations2002Philip de Chazal, Branko G. Celler
A classifier for categorising the Frank lead ECG as normal or one of six disease conditions was designed, which modelled the Bayesian posterior probabilities by using a neural network with no hidden units and a softmax output stage.
Automatic detection of PVCs using autoregressive models
12 Citations2002Joseph Suresh Paul, M. Ramasubba Reddy +1 more
An algorithm is presented for the identification of ventricular ectopic beats (PVC) by performing a beat by beat analysis of the electrocardiogram (ECG) and was able to successfully identify PVCs from the recordings of MIT-BIH database.
Automatic detection of PVC's using Autoregressive Models.
11 Citations1997M. R. S. Reddyt, V. Jagadeesh Kumart
An algorithm is presented for identification of ventricular ectopic beats (PVC) by performing a beat by beat analysis of Electrocardiogram (ECG), and clearly distinguishes PVC patterns from those of normal beats upto a signal to noise.
Adaptive wavelet representation and classification of ECG signals
10 Citations2002M. Kanapathipillai, Ismail Jouny +1 more
A neural network classifier that utilizes adaptive wavelet based features is used to discriminate between normal and abnormal beats in ECG signals used in the experimental phase of this study.