A review of different ECG classification/detection techniques for improved medical applications

Gupta, Varun; Saxena, Nitin Kumar; Kanungo, Abhas; Gupta, Anmol; Kumar, Parvin; Salim, Salim

doi:10.1007/s13198-021-01548-3

Cited by 16 publications

(9 citation statements)

References 158 publications

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“…Thus, the canonical expansion (8) for N = 4 (random coefficients are a array of independent random variables) with the corresponding sets of coordinate functions are adequate models 40 of the studied random sequences {C(i)/k}, i = 1, 14, k = 1, 9 . The following methods were used for recognition: (a) linear criterion (rule ( 6) for E C j C(i) = 0,E C ν j C µ (i) = 0, ν + µ > 2 ); (b) polynomial criterion (6) 55 ; (c) fuzzy logic method [56][57][58] ; (d) neural network 59,60 based on the Daubechies wavelet function of the fourth order and the Levenberg-Marquardt algorithm for learning; (e) generalized non-linear criterion (11). Let us consider mentioned criteria in details: Input parameters: p 1 -increase of double product per one kilogram of the body weight of the sick; p 2increase of double product per one kilogram of physical exertion; p 3 -coefficient of phosphorilation; p 4 -age of the sick; p 5 -double product of pulse on arterial tension; p 6 -adenosinetriphosphoric acid; p 7 -adenosine diphosphoric acid; p 8 -adenylic acid; p 9 -coefficient of the ratio of lactic and pyruvic acid content;p 10 -maximal consumption of oxygen per one kilogram of the body weight of the sick; p 11 -increase of double product in the response for submaximal physical exertion; p 12 -tolerance to physical activity.…”

Section: Results Of the Numerical Experimentsmentioning

confidence: 99%

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Atamanyuk

Kondratenko

Havrysh

et al. 2023

Sci Rep

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Decision support systems can seriously help medical doctors in the diagnosis of different diseases, especially in complicated cases. This article is devoted to recognizing and diagnosing heart disease based on automatic computer processing of the electrocardiograms (ECG) of patients. In the general case, the change of the ECG parameters can be presented as a random sequence of the signals under processing. Developing new computational methods for such signal processing is an important research problem in creating efficient medical decision support systems. Authors consider the possibility of increasing the diagnostic accuracy of cardiovascular diseases by implementing of the new proposed computational method of information processing. This method is based on the generalized nonlinear canonical decomposition of a random sequence of the change of cardiogram parameters. The use of a nonlinear canonical model makes it possible to significantly simplify the maximum likelihood criterion for classifying diseases. This simplification is provided by the transition from a multi-dimensional distribution density of cardiogram parameters to a product of one-dimensional distribution densities of independent random coefficients of a nonlinear canonical decomposition. The absence of any restrictions on the class of random sequences under study makes it possible to achieve maximum accuracy in diagnosing cardiovascular diseases. Functional diagrams for implementing the proposed method reflecting the features of its application are presented. The quantitative parameters of the core of the computational diagnostic procedure can be determined in advance based on the preliminary statistical data of the ECGs for different heart diseases. That is why the developed method is quite simple in terms of computation (computing complexity, accuracy, computing time, etc.) and can be implemented in medical computer decision systems for monitoring cardiovascular diseases and for their diagnosis in real time. The results of the numerical experiment confirm the high accuracy of the developed method for classifying cardiovascular diseases.

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Section: Results Of the Numerical Experimentsmentioning

confidence: 99%

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Atamanyuk

Kondratenko

Havrysh

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In [62], a brand-new feature extraction method called the fractional wavelet transform (FrWT) is suggested. [63] presents a comprehensive literature review of ECG arrhythmia classification/ detection methods and summarises their outcomes. The authors of [64] suggest using a fuzzy radial basis function neural network model to estimate the length of stay in an intensive care unit.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Chaitanya

Sharma

Rahul

et al. 2023

Biomed. Phys. Eng. Express

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Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.

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“…Almost 17.9 million people have died due to arrhythmia (Pandey et al, 2020). Therefore, several studies have been reported to detect the beat for diagnosis of cardiovascular diseases using several techniques (Gupta et al, 2022). Wavelet is one of the most popular and widely used techniques to detect beet from an ECG signal (Gupta et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, several studies have been reported to detect the beat for diagnosis of cardiovascular diseases using several techniques (Gupta et al, 2022). Wavelet is one of the most popular and widely used techniques to detect beet from an ECG signal (Gupta et al, 2022). Over the two decades, wavelets have accumulated an expanding enthusiasm for some signal handling and detection applications, having been the basis for various programmed and rapid arrhythmia-finding devices (Daqrouq et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular Diseases Identification Using Wavelet Optimization and Modify Cuckoo Search Algorithm

Negi,

Bartwal,

Verma

et al. 2023

Journal of Computer Science

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The problem the author aims to solve is the extraction of discriminatory features in ECG (Electrocardiogram) signals for classification purposes. The scope of the work is to propose a new method for building wavelets that best reflect the discriminatory capacity of ECG signals. The approach involves optimizing the wavelets specifically for the classification function under consideration. To address the problem, the author proposes a novel method for creating wavelets that optimize discriminatory feature extraction in ECG signals. The approach utilizes the poly-phase demonstration of the filter bank and incorporates the Modified Cuckoo Search (MCS) algorithm to project the problem context. The experiments are conducted using the MIT/BIH arrhythmia database to evaluate the performance of the proposed method against existing state-ofthe-art techniques. The Support Vector Machine (SVM) classifier is used to demonstrate the effectiveness, precision, and robustness of the projected strategy on standard wavelets like Daubechies and Symlet. The extent of the author's work involves developing a new method for wavelet construction to enhance discriminatory feature extraction in ECG signals. Important variables controlled in the study include the choice of wavelet parameters, the application of the MCS algorithm, and the evaluation of results against standard wavelet-based classification methods. The experiment results demonstrate the superiority of the proposed wavelet construction method over traditional wavelets like Daubechies and Symlet for ECG signal classification. The new wavelet shows improved discriminatory capacity, leading to higher classification precision and accuracy. The findings imply that optimizing wavelets for the classification of ECG signals using the Modified Cuckoo Search algorithm can significantly enhance discriminatory feature extraction and improve classification precision. The results are potentially generalizable and can be applied to various ECG signal classification tasks, contributing to advancements in medical diagnostics and monitoring. Background: The extraction of discriminatory features from ECG signals is a critical task in the field of medical signal processing. Accurate classification of these features plays a crucial role in diagnosing various cardiac arrhythmias and abnormalities. Wavelet-based techniques have shown promise in this domain, but further optimizations are necessary to achieve higher classification precision and robustness. Current Status in the Field: The field of ECG signal classification continues to evolve, with researchers exploring various approaches to improve discriminatory feature extraction. Wavelet-based methods have gained popularity due to their ability to capture both frequency and time-domain information, but fine-tuning the

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A review of different ECG classification/detection techniques for improved medical applications

Cited by 16 publications

References 158 publications

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders

Cardiovascular Diseases Identification Using Wavelet Optimization and Modify Cuckoo Search Algorithm

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