PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture

Kobat, Mehmet Ali; Karaca, Ozkan; Barua, Prabal Datta; Doğan, Şengül

doi:10.3390/sym13101914

Cited by 14 publications

(5 citation statements)

References 45 publications

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“…Figure 4 lists the top 10 most relevant authors based on their contributions to arrhythmia research in terms of published articles. The author named U. R. Acharya [31][32][33][34][35], ranks first with five contributions, and S. Dogan [35][36][37][38], contributes four publications. The remaining eight authors contributed equally with three publications.…”

Section: Most Relevant Authorsmentioning

confidence: 99%

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

Gronthy,

Biswas,

Tapu

et al. 2023

Diagnostics

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Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. “Arrhythmia detection”, “arrhythmia classification” and “arrhythmia detection and classification” are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, “performance analysis” and “science mapping”, were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.

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Section: Most Relevant Authorsmentioning

confidence: 99%

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

Gronthy,

Biswas,

Tapu

et al. 2023

Diagnostics

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show abstract

“…Rahul and Sharma [18] suggest a method for classification of heart problem, e.g., Afib atrial fibrillation, Vfib ventricular fibrillation, Vtec ventricular tachycardia, and normal N rhythm, using a hybrid model based on 1-D convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). Ramkumar et al [19] [20], the authors use the index of PatNet54 charter in order to create an extractor based on a graph. This extractor is known and named as a prism pattern.…”

Section: Related Literature 21 Deep Learning Tasks For Ecg Signal Cla...mentioning

confidence: 99%

Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module

Meqdad

Hussein²,

Husain

et al. 2023

IJEECS

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Categorization of cardiac abnormalities received from several centers is not possible within the quickest time because of privacy and security restrictions. Today, individuals’ security problem is considered as one of the most important research fields in most research sciences. This study provides a novel approach for detection of cardiac abnormalities based on federated learning (FL). This approach addresses the challenge of accessing data from remote centers and presents the possibility of learning without the need for transferring data from the main center. We present a novel aggregation approach in the FL for addressing the challenge of imbalanced data using the averaging stochastic weights (SWA) optimizer and a multivariate Gaussian in order to make a better and more accurate detection possible. The advantage of the present proposed approach is robust and secure aggregation for unbalanced electrocardiogram (ECG) data from heterogeneous clients. We were able to achieve 87.98% accuracy in testing with the robust VGG19 architecture.

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“…They only considered seven classes of arrhythmia. In [ 13 ], the authors demonstrated a support vector machine (SVM)-based framework for classifying arrhythmia using a 1D ECG signal. They considered 17 classes of arrhythmia and achieved 97.3% accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia

Jamil

Rahman

2022

J. Imaging

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Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time–frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques.

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PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture

Cited by 14 publications

References 45 publications

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia

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