Wyniki wyszukiwania - Biblioteka Nauki

1

Modeling ECG waveforms

100%

Dubowik K. , Larsson J. E.

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tom Vol. 8, nr 1

23-36

EN

This paper describes a way to mode! ECG signals that is used for building and testing an automated arrhythmia analyzer. The segments of the waveform are approximated by Gaussian curves, which is simple and generates realistic-looking waveforms- By modeling subsequent heartbeats it is possible to obtain the ECG's of every arrhythmia. The signals generated may be comfortably used for designing an automated ECG analyzer and for leaching medicine students.

2

Entropia w badaniach zaburzeń rytmu serca

84%

Graff B. , Graff G. , Kolesiak A.

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2008

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tom Nr 9

46-56

PL

Artykuł prezentuje zastosowanie Approximate Entropy, będącej miarą stopnia złożoności szeregów czasowych, do analizy zmienności rytmu serca.

EN

Healthy human heart rate is characterized by oscillations observed in intervals between consecutive heartbeats (RR intervals). Conventional methods of heart rate variability analysis measure the overall magnitude of RR interval fluctuations around its mean value or the magnitude of fluctuations in predetermined frequencies. The new methods of chaos theory and nonlinear dynamics provide powerful tools, which allow to predict clinical outcome in patients with cardiovascular diseases. The main aim of our article is to present Approximate Entropy (ApEn), a measure of system regularity and complexity, introduced by Pincus in 1991. ApEn estimation used for clinical purposes is applied for finite number of records, divided in vectors, and depends on two fixed parameters m and r. Then Approximate Entropy may be interpreted as the average of negative natural logarithms of conditional probability, that two vectors of length m + 1 are similar (we define here r-similarity), if two vectors of the length m are similar. The article provides a formal mathematical description of ApEn and presents a simple algorithm for its assessment. The choice of input parameters m and r is also discussed. In vast majority of publications r depends on standard deviation (SD) of average of all records, when individual features of heart rhythm are taken into account. The fraction of r, equal to 0, 2SD, and m = 2 are usually chosen on the basis of previous findings of good statistical validity. With the above set of parameters we can avoid the influence of outliers and do not loose too much information. ApEn has also some disadvantages - the main is counting self similarities. To reduce this kind of bias some improvements of the methods based on Pincus’ algorithm were developed. For example Sample Entropy (SampEn), which has similar algorithm but does not count self-matches, was proposed and easily applied to clinical time-series. In the article we present also an application of ApEn in predicting atrial fibrillation (AF), a type of arrhythmia which is the most common sustained heart rhythm disturbance. Both ApEn and SampEn decrease before the spontaneous onset of AF. What is more, ApEn is not sensitive to ectopy beats and therefore can be assessed fully automatically. The potential application of ApEn is the possibility to detect an increased vulnerability to AF before the onset of arrhythmia during continuous heart rate recording, for example for patients with implantable pacemakers. The recognition of the higher risk of AF would be followed by immediate pacemaker reprogramming to prevent an episode of arrhythmia. It would result not only in better quality of life of the patient but also in decreased number of hospitalization and cost of treatment.

3

A generative model for Deep Fake Augmentation of phonocardiogram and electrocardiogram signals using LSGAN and Cycle GAN

84%

Rayavarapu S. M. , Prasanthi T. S. , Kumar G. S. , Rao G. S. , Prashanti G.

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tom T. 13, nr 4

34--38

EN

In order to diagnose a range of cardiac conditions, it is important to conduct an accurate evaluation of eitherphonocardiogram (PCG)and electrocardiogram (ECG) data. Artificial intelligence and machine learning-based computer-assisted diagnostics are becoming increasingly commonplace in modern medicine, assisting clinicians in making life-or-death decisions. The requirement for an enormous amount of informationfor training to establish the framework for a deeplearning-based technique is an empirical challenge in the field of medicine. This increases the riskof personal information being misused. As a direct result of this issue, there has been an explosion in the study of methods for creating synthetic patient data. Researchers have attempted to generate synthetic ECG or PCG readings. To balance the dataset, ECG data were first created on the MIT-BIH arrhythmia database using LS GAN and Cycle GAN. Next, using VGGNet, studies were conducted to classify arrhythmias for the synthesized ECG signals. The synthesized signals performed well and resembled the original signal and the obtained precision of 91.20%, recall of 89.52% and an F1 scoreof 90.35%.

PL

W celu zdiagnozowania szeregu chorób serca, istotne jest przeprowadzenie dokładnej oceny danych z fonokardiogramu (PCG)i elektrokardiogram (EKG). Sztuczna inteligencja i diagnostyka wspomagana komputerowo, oparta na uczeniu maszynowym stają sięcoraz bardziej powszechne we współczesnej medycynie, pomagając klinicystom w podejmowaniu krytycznych decyzji. Z kolei, Wymóg ogromnej ilości informacjido trenowania, w celu ustalenia platformy (ang. framework) techniki, opartej na głębokim uczeniu stanowi empiryczne wyzwanie w obszarze medycyny. Zwiększa to ryzyko niewłaściwego wykorzystania danych osobowych. Bezpośrednim skutkiem tego problemu był gwałtowny rozwój badań nad metodami tworzenia syntetycznych danych pacjentów. Badacze podjęli próbę wygenerowania syntetycznych odczytów diagramów EKG lub PCG. Stąd, w celu zrównoważenia zbioru danych, w pierwszej kolejności utworzono dane EKG w bazie danych arytmii MIT-BIH przy użyciu struktur sieci generatywnych LSGAN i CycleGAN. Następnie, wykorzystując strukturę sieci VGGNet, przeprowadzono badania, mające na celu klasyfikację arytmii na potrzeby syntetyzowanych sygnałów EKG. Dla wygenerowanych sygnałów, przypominających sygnał oryginalny uzyskano dobre rezultaty. Należy podkreślić,że uzyskana dokładność wynosiła 91,20%, powtarzalność 89,52% i wynik F1 –odpowiednio 90,35%.

4

Implementation of an artificial intelligence-based ECG acquisition system for the detection of cardiac abnormalities

84%

Benba A. , El Attaoui F. Z. , Sandabad S.

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tom T. 13, nr 1

22--25

EN

The electrocardiogram (ECG) is a common test that measures the electrical activity of the heart. On the ECG, several cardiac abnormalities can be seen, including arrhythmias, which are one of the major causes of cardiac mortality worldwide. The objective for the research community is accurate and automated cardiovascular analysis, especially given the maturity of artificial intelligence technology and its contribution to the health area. The goal of this effort is to create an acquisition system and use artificial intelligence to classify ECG readings. This system is designed in two parts: the first is the signal acquisition using the ECG Module AD8232; the obtained signal is a single derivation that has been amplified and filtered. The second section is the classification for heart illness identification; the suggested model is a deep convolutional neural network with 12 layers that was able to categorize five types of heartbeats from the MIT-BIH arrhythmia database. The results were encouraging, and the embedded system was built.

PL

Elektrokardiogram (EKG) to powszechny test, który mierzy aktywność elektryczną serca. W zapisie EKG można zauważyć kilka nieprawidłowości serca, w tym arytmie, które są jedną z głównych przyczyn śmiertelności sercowej na całym świecie. Celem społeczności naukowej jest dokładna i zautomatyzowana analiza układu sercowo-naczyniowego, zwłaszcza biorąc pod uwagę dojrzałość technologii sztucznej inteligencji i jej wkład w obszar zdrowia. Celem tych wysiłków jest stworzenie systemu akwizycji i wykorzystanie sztucznej inteligencji do klasyfikacji odczytów EKG. System ten składa się z dwóch części: pierwsza to akwizycja sygnału za pomocą modułu EKG AD8232; uzyskany sygnał jest pojedynczą pochodną, która została wzmocniona i przefiltrowana. Druga sekcja to klasyfikacja identyfikacji chorób serca; sugerowany model to głęboka konwolucyjna sieć neuronowa z 12 warstwami, która była w stanie sklasyfikować pięć typów uderzeń serca z bazy danych arytmii MIT-BIH. Wyniki były zachęcające i zbudowano system wbudowany.

5

New generation impedance rheographs and examples of their clinical applications

67%

Pałko T.

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2007

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tom Vol. 27, no. 1-2

185-196

EN

The impedance rheography as a method of measurement and recording of electrical impedance and its changes in a body segment is widely used in research and medical practice. Several types, single or multichannel, analog or digital impedance rheographs were developed at Warsaw University of Technology. A digital impedance rheograph has been recently designed for measurement of impedance modulus (...) and its changes (...) of body segment or tissue sequent simultaneously with ECG signal. The designed rheographs were positively tested technically and clinically. In this article is presented the clinical application of impedance rheography for study of systemic and pulmonary blood flow in two different groups of patients with several cardiac abnormalities or after therapeutic procedures. The investigation of systemic blood circulation was done on group of 20 patients with cardiac arrhythmia (15 cardioverted and 5 cardiostimulated patients). The pulmonary blood circulation was studied on 20 healthy and 26 patients: 8 with isolated pulmonary stenosis and 18 with Fallot syndrome. The findings show that impedance method allows to detect and assess these abnormalities.