Automated System for the Analysis and Interpretation of Ecg

Дубровін, В. І.; Tverdohleb, J. V.; Kharchenko, V. V.

doi:10.15588/1607-3274-2014-1-22

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is easy to see from the above scheme that not a single step of ECG analysis is completed by without morphological analysis of the signal. As noted above, in the context of the introduction of telemedicine into the health care system of Ukraine, the creation of cardiological DSSs based on automatic morphological ECG analysis is of particular importance, for which various methods are used:  ECG analysis in the time domain using various classification methods, including probabilistic classification [14,15], cluster analysis and pattern recognition [16,17], neural networks [18,19], fuzzy clustering [20,21] and others;  ECG analysis in the time-frequency domain, for example, local (window) Fourier transform (spectral-time mapping) and wavelet transform [22,23], as well as in the phase plane [10,24];  morphological filtration of ECG using the multichannel matched morphological filter proposed by the authors [25].…”

Section: Analysis Of Literary Datamentioning

confidence: 99%

Synthesis of an integral signal for solving the problem of morphological analysis of electrocardiograms

Filatova¹,

Povoroznyuk²,

Nosachenko³

et al. 2022

HAIT

View full text Add to dashboard Cite

This work is devoted to solving the scientific and practical problem of morphological analysis of electrocardiograms based on an integral biomedical signal with locally concentrated features. In modern conditions of introduction of telemedicine in the health care system of Ukraine the creation of cardiological decision support systems based on automatic morphological analysis of electrocardiogram is of particular importance. The authors proposed a method for synthesizing an integral electrocardiogram in the frontal plane from all limb leads, taking into account the lead angle in the hexaxial reference system and the position of the heart’s electrical axis, since integral electrocardiological signals allow to obtain more accurate results compared to conventional electrocardiogram, because they take into account the individual characteristics of patients, a wide variety of electrocardiogram waveforms and complexes, which is associated not only with the presence of pathological processes in the myocardium, but also with the position of the electrical axis of the heart, in particular, the electrocardiogram will not register a low-amplitude P wave in the II department in the case of a horizontal electrical axis, but it will be clearly visible on the integral signal. To implement the method proposed in the article, a program was written in the MATLAB language, , the high speed of computation and good optimization of which allow to obtain results much faster and more accurate than using traditional approaches, and using the MATLAB Runtime library, which does not require licensing and is distributed free of charge, it was possible to provide more economical development, as well as to implement interaction with popular operating systems, which makes it more accessible and versatile. Verification of the results was carried out using a database of electrocardiograms, which were recorded using a transtelephone digital 12-channel electrocardiological complex “Telecard”, which is part of the medical diagnostic complex “TREDEX”. The paper shows that the proposed method for the synthesis of an integral signal with locally concentrated features will improve the quality of morphological analysis of electrocardiograms in cardiological decision support systems.

show abstract

Section: Analysis Of Literary Datamentioning

confidence: 99%

Synthesis of an integral signal for solving the problem of morphological analysis of electrocardiograms

Filatova¹,

Povoroznyuk²,

Nosachenko³

et al. 2022

HAIT

View full text Add to dashboard Cite

show abstract

Automatic Arrhythmia Detection Based on the Analysis of Electrocardiograms With Deep Learning

Shchetinin¹

2021

vkit

View full text Add to dashboard Cite

According to the World Health Organization, cardiovascular diseases (CVD) are one of the most common causes of death in the world. The most effective clinical method for visualizing the cardiac electrical activity is electrocardiography (ECG). Automated ECG analysis has been of great interest in the medical researches. The problem of automated detection of cardiac arrhythmias may be reduced to the ECG signals classification. To solve this task such methods were used as Hidden Markov Models (HMM), discrete wavelet transforms (DWT), support vector machine (SVM) etc. Now days, the deep learning models began to play the major role in solving this problem. In this paper, for the classification of ECG signals, a number of models of deep neural networks, including deep convolutional, recurrent based on short-term long memory have been developed and implemented. To improve the classification accuracy of individual classes of the studied data, the CNN-LSTM deep model was built, which combines convolutional and recurrent networks. In addition the following machine learning algorithms were used for ECG signals classification: support vector machine (SVM), decision trees (DT), random forest (RF) and extreme gradient boosting (XGB). To test the performance of the proposed models, MIT-BIH database was used, a freely available dataset that is widely used to evaluate the effectiveness of ECG signal classification algorithms. The results of a comparative analysis of various algorithms for the quality of classification for individual classes showed that machine learning algorithms classify classes with a large volume of samples well. For example, SVM and DT classify samples from class N and Q with an accuracy of 92 and 97%, respectively, while samples from classes S and F are classified with much worse accuracy of 63%. At the same time, analyzing and comparing the performance of various neural network models based on the obtained estimates of the classification accuracy, it can be argued that CNN LSTM model allows not only a high classification accuracy of 99.37%, but also high values of other indicators of classification quality, such as F1- metric, precision, and recall.The proposed algorithms for the automated detection of cardiac arrhythmias can be applied in biomedical applications that analyze the electrocardiogram and help physicians diagnose cardiac arrhythmias more accurately.

show abstract

Automated System for the Analysis and Interpretation of Ecg

Cited by 2 publications

References 5 publications

Synthesis of an integral signal for solving the problem of morphological analysis of electrocardiograms

Synthesis of an integral signal for solving the problem of morphological analysis of electrocardiograms

Automatic Arrhythmia Detection Based on the Analysis of Electrocardiograms With Deep Learning

Contact Info

Product

Resources

About