Classification of ECG arrhythmia by a modular neural network based on Mixture of Experts and Negatively Correlated Learning

Javadi, Mehrdad; Arani, Seyed Ali Asghar Abbaszadeh; Sajedin, Atena; Ebrahimpour, Reza

doi:10.1016/j.bspc.2012.10.005

Cited by 104 publications

(42 citation statements)

References 32 publications

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“…Measures OA PVC Non-PVC Se +P Se +P Manu [9] 99.3 97.8 99.5 ----------------Javadi [11] 96.0 92.3 -------98.0 --------Laurent [12] 95.2 82.6 93.4 --------------Bazi [14] 96.7 97.3 96.6 --------------Li [16] 98. Table 3 displays the experimental results.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, many studies concerning the classification of arrhythmias have been conducted [4][5][6][7][8][9][10][11][12]. In one study, J. Wang developed a novel ECG arrhythmia classification method based on feature reduction by combing a principal component analysis (PCA) with a linear discriminant analysis (LDA).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, a probabilistic neural network (PNN) was used as a classifier to distinguish among eight different types of arrhythmias types [10]. In another study, M. Javadi extracted the morphological and time data from ECGs by combining mixture of experts (ME) and negatively correlated learning (NCL) in order to differentiate different types of arrhythmias [11]. L. Giovangrandi classified the ECG beats obtained from a large set of data using wavelet transforms and the timing information of a neural network classifier [12].…”

Section: Introductionmentioning

confidence: 99%

“…Peng Li developed a low complexity data-adaptive PVC recognition approach that exhibited good robustness against noise, generalization capabilities, and a PVC recognition accuracy of 98.2%, indicating that it could be effectively used for real-time applications [16]. Using these algorithms, the features of ECGs were manually extracted based on time domain information, such as ECG morphology [6,7,11,12], and transform domain information [4,5,9,[12][13][14], such as the wavelet transforms or statistical parameters [10,15,16]. These processes require artificial experience or specialized knowledge and increase computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Yang

Bai

et al. 2015

BME

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Abstract. Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Yang

Bai

et al. 2015

BME

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“…Accuracy in % ACO [4] 47.45 BBO [14] 91.1 KNN [29] 80.03 MNN [10] 92.1 NaiveBayes [29] 91.63 PSO [11] 91.16 Random Forest [24] 89.12 ES [19] 91.81 Proposed Method 92.61…”

Section: Algorithm/techniquesmentioning

confidence: 99%

A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data

Doreswamy¹,

M²

2016

IJSCAI

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Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large number of people around the world. Diagnosing the disease at its earliest instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for training data and 89.95% for testing data.

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Three‐Bits Linear Interpolation Error Signal Based ECG Beat Classification

Alshrouf

Mansouri

Alharbi

2022

IEEJ Transactions Elec Engng

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About three million people die from sudden cardiac arrest (SCA) or infarction each year. It is the biggest killer on the planet and it can happen to anyone, anywhere, and anytime. Early and effective diagnosis from ECG signals can help save a large part of these SCAs. Premature ventricular contraction (PVC) detection and classification was performed with different methods. A new method of automatic classification of PVC based on a three‐bit linear interpolation error signal (LIES) is proposed in this paper. We start by a QRS detection, a nonlinear transformation and then a sliding window. This method was tested on normal and abnormal beats obtained from MIT‐BIH and AHA databases. The proposed algorithm with a sensitivity 94.8% and a specificity was 98.6%. is accurate. This algorithm is also automatic, robust, and fast. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Classification of ECG arrhythmia by a modular neural network based on Mixture of Experts and Negatively Correlated Learning

Cited by 104 publications

References 32 publications

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data

Three‐Bits Linear Interpolation Error Signal Based ECG Beat Classification

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