Automated detection of atrial fibrillation using Bayesian paradigm

Martis, Roshan Joy; Acharya, U. Rajendra; Prasad, Hari; Chua, Chua Kuang; Lim, C.M.

doi:10.1016/j.knosys.2013.09.016

Cited by 76 publications

(22 citation statements)

References 26 publications

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“…In literature, except the discussed methods in medical research, various other methods for CAD classification have been developed using other learning and data mining techniques, for instance, random forest [ 48 , 49 ], decision tree [ 50 , 51 ], clustering [ 52 , 53 ], and Gaussian mixture model [ 54 ].…”

Section: Methodology Proceduresmentioning

confidence: 99%

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Mastoi

Wah

Raj

et al. 2018

Cardiology Research and Practice

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Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.

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Section: Methodology Proceduresmentioning

confidence: 99%

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Mastoi

Wah

Raj

et al. 2018

Cardiology Research and Practice

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show abstract

“…It can be found that the performance of the NBC is generally lower than other classifiers. [17,18] Pourbabaee and Lucas [17] have proposed three different classifiers to identify the AFIB that are kNN, ANN and Naive Bayesian by using the beat information and power spectral density. This research shows that among the AFIB classifiers, kNN provides the highest AFIB detection accuracy of 93.75% compared with ANN and Naive Bayesian, which have 87.50% and 75.00% of accuracy, respectively.…”

Section: Naive Bayesian Classifier (Nbc)mentioning

confidence: 99%

“…This research shows that among the AFIB classifiers, kNN provides the highest AFIB detection accuracy of 93.75% compared with ANN and Naive Bayesian, which have 87.50% and 75.00% of accuracy, respectively. In the paper of Joy et al, [18] he has proposed Naive Bayesian and Gaussian mixture model (GMM) to classify AFIB. It is discovered that GMM provides slightly higher performance in all sensitivity, specificity, and accuracy compared with Naive Bayesian, However, all of them still have the accuracy of 99.00% and above.…”

Section: Naive Bayesian Classifier (Nbc)mentioning

confidence: 99%

Artificial intelligence classification methods of atrial fibrillation with implementation technology

Lim

Hau

Lim

et al. 2016

Computer Assisted Surgery

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Background: Atrial fibrillation (AFIB) is one of the most common types of arrhythmia, which leads to heart failure and stroke to public. As AFIB has the high potential to cause permanent disability in patients, its early detection is extremely important. There are different types of AFIB classification algorithm that have been proposed by researchers in recent years. Methods: This paper reviews the features of AFIB in terms of ECG morphological features and heart rate variability (HRV) analysis on different methods. The existing classification method, particularly focusing on Artificial Intelligence technique, is also comprehensively described. Other than that, the existing implementation technology of arrhythmia detection platforms such as smart phone and System-on-Chip-based embedded device are also elaborated in terms of their design trade-offs. Conclusion: Current existing AFIB detection algorithm cannot compromise for high accuracy and low complexity. Due to the limitation of embedded system, design trade off should be considered to strike the balance between the performance of algorithm and the limitation.

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“…The value of the dominant frequency has been shown to be a distinctive feature for AF detection. In another ECG-based pattern analysis method for the classification of normal sinus rhythm and atrial fibrillation (AF) beats [13], the denoised and registered ECG beats were subjected to independent component analysis (ICA) for data reduction, and the ICA weights were used as features for classification using Naive Bayes and Gaussian mixture model (GMM) classifiers. All of these methods use handcraft features for pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

Xiao-ling

Zhang

et al. 2019

Journal of Probability and Statistics

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In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

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Automated detection of atrial fibrillation using Bayesian paradigm

Cited by 76 publications

References 26 publications

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Artificial intelligence classification methods of atrial fibrillation with implementation technology

Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

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