Towards automatic detection of atrial fibrillation: A hybrid computational approach

Yaghouby, Farid; Ayatollahi, Ahmad; Bahramali, Reihaneh; Yaghouby, Maryam; Alavi, Amir H.

doi:10.1016/j.compbiomed.2010.10.004

Cited by 51 publications

(24 citation statements)

References 39 publications

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“…The use of ANN has been also found to be efficient as a PID controller (Dong et al, 2014). However, GP was found to be more efficient compared to radial basis function (RBF) neural network in the automatic detection of atrial fibrillation based on HRV signals (Yaghouby et al, 2010). AI techniques have been found to be better prediction tools for geoscience problems than conventional techniques (Goh, 2002;Kerh and Chu, 2002).…”

Section: Introductionmentioning

confidence: 98%

Prediction of the residual strength of clay using functional networks

Khan

Suman

Pavani

et al. 2016

Geoscience Frontiers

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a b s t r a c tLandslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks (FN) using data available in the literature. The performance of FN was compared with support vector machine (SVM) and artificial neural network (ANN) based on statistical parameters like correlation coefficient (R), NashSutcliff coefficient of efficiency (E), absolute average error (AAE), maximum average error (MAE) and root mean square error (RMSE). Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output. Ó 2015, China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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Section: Introductionmentioning

confidence: 98%

Prediction of the residual strength of clay using functional networks

Khan

Suman

Pavani

et al. 2016

Geoscience Frontiers

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“…Such a challenge calls for a wide variety of automatic AF detectors. For the past years, a series of sophisticated methods have been developed to tackle the challenges of AF detection (Kikillus et al, 2007; Couceiro et al, 2008; Babaeizadeh et al, 2009; Yaghouby et al, 2010; Larburu et al, 2011; Parvaresh and Ayatollahi, 2011). Two classes of AF detection methods, the atrial activity analysis-based (Artis et al, 1991; Slocum et al, 1992; Lake and Moorman, 2011; Zhou et al, 2014; Ladavich and Ghoraani, 2015) and the ventricular response analysis-based (Moody and Mark, 1983; Tateno and Glass, 2001; Dash et al, 2009; Park et al, 2009; Huang et al, 2011; Lian et al, 2011; Yaghouby et al, 2012; Lee et al, 2014) method, attract the interest of the most of studies.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

et al. 2018

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Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.

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“…Additionally, they can be considered similar to the biological genetic operations such as crossover recombination and mutation. These algorithms have been successfully applied to many real world problems (Divsalar et al 2011(Divsalar et al , 2012Yang et al 2012;Gandomi et al 2013;Zargari et al 2012;Yaghouby et al 2010). It can be argued that the GEP proves to be superior to GP due to the mere fact that it clearly distinguishes the differences between the genotype 1 and the phenotype 2 of individuals within a population.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms

Karatahansopoulos

Sermpinis

Laws

et al. 2014

Journal of Forecasting

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This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in forms of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to the ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter.

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Towards automatic detection of atrial fibrillation: A hybrid computational approach

Cited by 51 publications

References 39 publications

Prediction of the residual strength of clay using functional networks

Prediction of the residual strength of clay using functional networks

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms

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