Electrocardiogram Based Arrhythmia Classification Using Wavelet Transform with Deep Learning Model

Mohonta, Shadhon Chandra; Motin, Mohammod Abdul; Kumar, Dinesh

doi:10.2139/ssrn.4088025

Cited by 6 publications

(4 citation statements)

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“…Likewise, Mohanto et al [46] proposed using the CWT and Morse wavelet for arrhythmia detection performed through a 2D CNN. The trained model was used to detect five types of heartbeats by processing ECG data: normal, left bundle branch block, right bundle branch block, atrial premature, and premature ventricular contraction.…”

Section: Applications Of the Generalized Morse Wavelets A Medical And...mentioning

confidence: 99%

Applications of the Generalized Morse Wavelets: A Review

et al. 2023

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The study of signals, processes, and systems has motivated the development of different representations that can be used to analyze and understand them. Classical ways of studying the behavior of signals are the time domain and frequency domain representations. For the analysis of non-stationary signals, time-frequency representations have become an essential tool to understand how the frequency content of signals changes with time. A common time-frequency technique employed in the literature is the wavelet transform. Nevertheless, selecting an adequate mother wavelet to perform the wavelet transform has become challenging due to the diverse available wavelet families. This paper reviews the applications and uses of a particular class of wavelet basis known as the Generalized Morse Wavelets. This class of wavelet family provides a systematic framework to choose and generate a wavelet for general-purpose use. This study reviews the application of Generalized Morse Wavelets in biomedical engineering, dynamical systems analysis, electrical engineering, geophysics, and communication systems. Moreover, the parameters of the Generalized Morse Wavelets used in each study are presented. The results of this study reveal that Generalized Morse Wavelets have proven helpful in studying signals, systems, and processes in areas ranging from biomedical engineering to geophysics. Nonetheless, the parameters of the Generalized Morse Wavelets are yet to be chosen through a rigorous methodology and argumentation. Therefore, there is an opportunity to generate methods for selecting the parameters of the Generalized Morse Wavelets based on the characteristics of the signals, systems, or processes under research.INDEX TERMS Generalized Morse wavelets, mother wavelet selection, continuous wavelet transform, applications, time-frequency analysis.

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Section: Applications Of the Generalized Morse Wavelets A Medical And...mentioning

confidence: 99%

Applications of the Generalized Morse Wavelets: A Review

et al. 2023

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“…This extractor is known and named as a prism pattern. Mohonta et al [21] develope a deep learning approach to automatically detect cardiac abnormalities based the ECG signal using the continuous wavelet transform (CWT) wavelet transform technique that performs the classification operation continuously. In the proposed model, training and testing are performed on a 2dimensional convolutional neural network in order to detect five types of heartbeats.…”

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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“…In this type of signals, a very important process is the location of R-wave peaks, since it is the basis for measuring intervals, QRS complexes and waves. The Pan-Tompkins algorithm is widely used for peak wave location task [13][18] [19] .…”

Section: Introductionmentioning

confidence: 99%

A Novel Detector of Atypical Beats for Early Diagnosis of Heart Diseases Based on the Stacked Beats Representation of 12-lead ECG

Kurmyshev,

Galeana-Pérez

2023

Rev. Mex. Ing. Biomed.

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We have developed and present in this work a series of algorithms that present a long-duration electrocardiogram (ECG) in a compact form of stacked beats, extracting and visualizing the basic features and facilitating the tedious and time-consuming process of ECG analysis for cardiologists. The expert system based on this representation provides detection of atypical heartbeats, precursors of cardiovascular disease, and their locations in each of the 12 leads. This system was extensively tested with two public databases, MIT-BIH arrhythmia database and China Physiological Signal Challenge (CPSC2018), showing its rapid ECG processing and high efficiency in detecting abnormalities in beat morphology. In particular, tests for atypical beats based on the CPSC2018 database revealed that the set of ECGs marked as normal contains a considerable number of leads with atypical beats. The system is used as a classifier into two classes, normal beats, and atypical beats, the latter being the precursors or indicators of cardiovascular diseases (CVD). It is considered potentially useful for routine studies in groups at high risk of CVD in early stages, as a preventive medicine tool in the public health area. The system allows an intervention of a cardiologist in the intermediate stages of ECG analysis to corroborate the diagnosis in ambiguous cases.

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Electrocardiogram Based Arrhythmia Classification Using Wavelet Transform with Deep Learning Model

Cited by 6 publications

References 0 publications

Applications of the Generalized Morse Wavelets: A Review

Applications of the Generalized Morse Wavelets: A Review

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

A Novel Detector of Atypical Beats for Early Diagnosis of Heart Diseases Based on the Stacked Beats Representation of 12-lead ECG

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