Robust genetic programming‐based detection of atrial fibrillation using RR intervals

Abstract

In this study, two variants of genetic programming, namely linear genetic programming (LGP) and multi-expression programming (MEP) are utilized to detect atrial fibrillation (AF) episodes. LGP-and MEP-based models are derived to classify samples of AF and Normal episodes based on the analysis of RR interval signals. A weighted least-squares (WLS) regression analysis is performed using the same features and data sets to benchmark the models. Another important contribution of this paper is identification of the effective time domain features of heart rate variability (HRV) signals upon an improved forward floating selection (IFFS) analysis. The models are developed using MIT-BIH arrhythmia database. The diagnostic performances of the LGP and MEP classifiers are evaluated through receiver operating characteristics (ROC) analysis. The results indicate that the LGP and MEP models are able to diagnose the AF arrhythmia with an acceptable high accuracy. The proposed models have significantly better diagnosis performances than the regression and several models found in the literature.

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

“…The disease is associated with inefficiencies in blood flow dynamics which substantially increase the risk of stroke and systemic thromboembolism, resulting in high mortality and morbidity [3,4]. Some patients with AF are asymptomatic, but others have accompanying symptoms, such as fainting, palpitations, chest pain, fatigue, and heart failure, which seriously diminish the quality of life for the patients [5]. The link between AF and increased stroke risk was established in the Framingham study, which revealed that nonrheumatic AF is an independent stroke risk predictor among 5,070 participants [6].…”

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

“…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. The first category methods utilize the absence of P waves or the presence of f-waves for diagnosis.…”

Risk analysis of BOT contracts using soft computing

“…Statistical soft computing models based on machine learning have been widely used to address a wide range of optimization, classification or prediction problems in different science and engineering applications (Gandomi, Alavi 2009;Yaghouby et al 2010Yaghouby et al , 2012Azamathulla, Ahmad 2013;Najafzadeh, Azamathulla 2014;Gandomi et al , 2016. One of such models that could be used to automate the decision making scenario is the artificial neural network (ANN) (Jin, Zhang 2011;Sodikov 2005).…”