Prediction models in the design of neural network based ECG classifiers: A neural network and genetic programming approach

Nugent, Chris; López, Jesús A.; Smith, Ann E.; Black, Norman D.

doi:10.1186/1472-6947-2-1

Cited by 19 publications

(8 citation statements)

References 8 publications

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“…Time frequency transformation is not necessary. The discrete wavelet transform DWT-PNN [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] can give similar performance but the optimization of the classification time has to be confirmed. In our case, our program implemented in Matlab7.10, takes 15 s for one hour of Holter recording.…”

Section: Classmentioning

confidence: 99%

“…The optimization of B m1 is the minimization of the mean square error J between the initial heartbeat S 1 and the first function B m1 with respect to the standard deviation σ m1 , the temporal position µ m1 and the amplitude A m1 E 1 (k)= S(k) at the first iteration Optimization was performed by Projected Gradient algorithm [7].This is an algorithm of minimization of first order applied to the cases of optimization under constraints [10].This optimization has the advantage of being fast because it takes into account only three parameters: A m1 , σ m1 , µ m1 of the Gaussian selected.…”

Section: 13mentioning

confidence: 99%

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Neural networks and SVM for heartbeat classification

Kedir-Talha

Ould-Slimane

2012

2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA)

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The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heartbeats. The purpose of this work is to propose a diagnostic system for modelling and classification of heartbeat, by use of time features and Support vector machines (SVM) classification algorithm. Neural Networks learning allow us to select a features of each heart beat on the basis of Generalized Orthogonal Forward Regression (GOFR) algorithm and a library of 132 Gaussians with different standard deviations and different means, each beat is represented by five Gaussians with different amplitudes. The parameters of this system are determined and its performance is evaluated for the MIT-BIH arrhythmia database. For a database of 364 normal heartbeats and 1148 abnormal heartbeats, we apply the SVM algorithm with Radial Basis Function kernel. Our results demonstrate that the testing performance of the neural network and SVM diagnostic system is found to be very satisfactory with a recognition rate of 99.67%.

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Section: Classmentioning

confidence: 99%

Section: 13mentioning

confidence: 99%

Neural networks and SVM for heartbeat classification

Kedir-Talha

Ould-Slimane

2012

2012 11th International Conference on Information Science, Signal Processing and Their Applications (ISSPA)

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“…This test assumes that there is information in the magnitudes of the differences between paired observations, as well as the signs. It is a very popular statistical test used by researchers to prove the significance of the experimental results [33,34]. Next, we briefly describe the method.…”

Section: The Wilcoxon Test Of Statistical Significancementioning

confidence: 99%

Analysing the localisation sites of proteins through neural networks ensembles

Anastasiadis

Magoulas

2006

Neural Comput & Applic

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Scientists involved in the area of proteomics are currently seeking integrated, customised and validated research solutions to better expedite their work in proteomics analyses and drug discoveries. Some drugs and most of their cell targets are proteins, because proteins dictate biological phenotype. In this context, the automated analysis of protein localisation is more complex than the automated analysis of DNA sequences; nevertheless the benefits to be derived are of same or greater importance. In order to accomplish this target, the right choice of the kind of the methods for these applications, especially when the data set is drastically imbalanced, is very important and crucial. In this paper we investigate the performance of some commonly used classifiers, such as the K nearest neighbours and feed-forward neural networks with and without cross-validation, in a class of imbalanced problems from the bioinformatics domain. Furthermore, we construct ensemble-based schemes using the notion of diversity, and we empirically test their performance on the same problems. The experimental results favour the generation of neural network ensembles as these are able to produce good generalisation ability and significant improvement compared to other single classifier methods.

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“…Here it can be seen that the performance of the train curve continues to increase throughout the process whereas the curve representing the test data reaches a maximum performance at approximately 500 epochs, after this the performance decreases significantly. By employing the early stopping method of training, a network can be trained to a point of maximum generalization based on a validation set and thus over-fitting is avoided [8]. Although this method is proven and effective, computational requirements are increased.…”

Section: Zntroductionmentioning

confidence: 98%

“…It is possible however to asses the NN at various stages of training by 'employing the early stopping method of training' [8]. This approach requires two sets of data, a training set and a test set.…”

Section: Zntroductionmentioning

confidence: 99%

Evaluation of a statistical prediction model used in the design of neural network based ECG classifiers: a multiple linear regression approach

Finlay

Nugent

McCullagh

et al.

4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003.

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Absiract-The application of Neural Networks in the implementation of ECG classifiers has become widespread. Unfortunately due to the lack of scientific evidence many of the choices made in the design of these classifiers are based on trial and error. This paper details an investigation into a statistical approach aimed at reducing the computational requirements when training an ECG classifier. The Multiple Linear Regression method was used to develop a predictor that would indicate at which point training of a Neural Network should stop. When tested it was found that this genre of predictor exhibited reasonable accuracy and out performed other predictors based on Neural Network and Genetic Programming techniques. Keywords -ECG, Neural Network, Regression I. ZNTRODUCTION Computer interpretation of the Electrocardiogram (ECG) was one of the first applications of computers in healthcare [I]. Since then many technologies have been utilized to assist in decision support for cardiac care. A particular area of interest is the application of computer based classification techniques [2]. Techniques applied in this arena include Expert Systems, Fuzzy Logic, Decision Tree's and Statistical analysis. Neural Networks ("s) have also emerged as a contender and have been shown to produce comparable and superior results with other approaches [3]. Their ability to produce a classification model following training on a representative set of data can be seen as one of their major advantages in that they do not formally rely on human expert guidance to generate their rule set.NNs are vast interconnections of elements called neurons. The biological neuron itself is a building block of the brain and in NNs an attempt is made to replicate the behavior of these. An artificial neuron consists of a number of inputs which are weighted before reaching a main body or processing unit, the signal is processed and passed to an output. The operation of the NN depends greatly on how the neurons are interconnected. After design a NN must be trained, during this process the NN adjusts its own intemal parameters causing it to converge to the desired response.As well as the fimdamental design issues that must be considered when developing NNs the developer must carefully plan the training process. Firstly a training data set must be selected that is representative of the target domain. The network must then be trained, using this data set, to a point that is deemed sufficient. In the past this has been achieved by subjecting the NN to the training data until a minimum error is produced [4-61. This method however may lead to over training, in this case the NN becomes over familiar with the data; this phenomenon is referred to as over-fitting [7]. This is undesirable as a NN which is biased toward the training data may perform poorly when unknown data is presented.Many of the decisions taken in the design and implementation of a NN are based on trial and error due to the lack of clear proven scientific rationale. It is possible however to asses the N...

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Prediction models in the design of neural network based ECG classifiers: A neural network and genetic programming approach

Cited by 19 publications

References 8 publications

Neural networks and SVM for heartbeat classification

Neural networks and SVM for heartbeat classification

Analysing the localisation sites of proteins through neural networks ensembles

Evaluation of a statistical prediction model used in the design of neural network based ECG classifiers: a multiple linear regression approach

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