WITHDRAWN: Review of ECG arrhythmia classification using deep neural network

Gupta, Deepak; Bajpai, Bhavna; Dhiman, Gaurav; Soni, Mukesh; Gomathi, S.; Mane, Dadaso

doi:10.1016/j.matpr.2021.05.249

Cited by 21 publications

(14 citation statements)

References 36 publications

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“…They found that the best features were reply frequency, increased bytes, and average contribution through experiments. Noh et al [ 19 ] used the Adaboost method which detects the political navy in Twitter, uses the chi-square test to give the 10 most contributing characteristics, and analyzes the characteristics of the discovered political navy. Shalash et al [ 20 ] used support vector machines, random forests, and Adaboost methods to detect deception in healthcare social networks.…”

Section: Supervised Algorithmmentioning

confidence: 99%

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

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A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person’s abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.

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Section: Supervised Algorithmmentioning

confidence: 99%

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

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“…The first AlexNet layer takes a filtered image with dimensions of 227 × 227 × 3, width, height, and depth (red, green, blue). The AlexNet architecture comprises 1000 connected layers, and the remaining layers are used for feature extraction [ 23 , 34 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…A review of ECG arrhythmia classification using a deep neural network is explained in [ 23 ]. This paper describes a new DL approach for categorizing ECG signals.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of ECG signal using FFT based improved Alexnet classifier

Arunkumar

Chakrapani

2022

PLoS ONE

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Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.

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“…Commonly used computer-aided diagnosis techniques mainly extract features from the viewpoints of signal analysis, dynamic system modeling (DSA) and topological data analysis (TDA), which are also combined with classic statistical analysis [ 4 ] and machine learning [ 5 , 6 , 7 , 8 , 9 , 10 ]. Signal analysis can directly extract morphological features such as amplitude [ 11 , 12 ] or use the wavelet transform to acquire frequency domain features [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Based on Wasserstein Scalar Curvature

Sun

Luo

et al. 2022

Entropy

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Electrocardiograms (ECG) analysis is one of the most important ways to diagnose heart disease. This paper proposes an efficient ECG classification method based on Wasserstein scalar curvature to comprehend the connection between heart disease and the mathematical characteristics of ECG. The newly proposed method converts an ECG into a point cloud on the family of Gaussian distribution, where the pathological characteristics of ECG will be extracted by the Wasserstein geometric structure of the statistical manifold. Technically, this paper defines the histogram dispersion of Wasserstein scalar curvature, which can accurately describe the divergence between different heart diseases. By combining medical experience with mathematical ideas from geometry and data science, this paper provides a feasible algorithm for the new method, and the theoretical analysis of the algorithm is carried out. Digital experiments on the classical database with large samples show the new algorithm’s accuracy and efficiency when dealing with the classification of heart disease.

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WITHDRAWN: Review of ECG arrhythmia classification using deep neural network

Cited by 21 publications

References 36 publications

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Classification of ECG signal using FFT based improved Alexnet classifier

ECG Classification Based on Wasserstein Scalar Curvature

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