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

Rayavarapu Swarajya Madhuri, Prasanthi Tammineni Shanmukha, Kumar Gottapu Santosh, Rao Gottapu Sasibhushana, Prashanti Gottapu

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2023

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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%.

2

Fundamental heart sounds analysis using improved complete ensemble EMD with adaptive noise

Altuve Miguel, Suárez Luis, Ardila Jeyson

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

426--439

EN

Phonocardiogram (PCG) recordings contain valuable information about the functioning and state of the heart that is useful in the diagnosis of cardiovascular diseases. The first heart sound (S1) and the second heart sound (S2), produced by the closing of the atrioventricular valves and the closing of the semilunar valves, respectively, are the fundamental sounds of the heart. The similarity in morphology and duration of these heart sounds and their superposition in the frequency domain makes it difficult to use them in computer systems to provide an automatic diagnosis. Therefore, in this paper, we analyzed these heart sounds in the intrinsic mode functions (IMF) domain, which were issued from two time-frequency decomposition techniques, the empirical mode decomposition (EMD) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), with the aim of retrieving useful information on an expanded basis. The decomposition of PCG recordings into IMF allows representing the fundamental cardiac sounds in many oscillating components, increasing thus the observability of the system. Moreover, the time-frequency representation of PCG recordings could provide valuable information to automatically detect heart sounds and diagnose pathologies from characteristic patterns of these heart sounds in the IMF. The analysis was made through the variance and Shannon's entropy of the heart sounds, observed in time windows located among different IMF. In addition, we determined the frequencies ranges of the IMF from the decomposition of the PCG recordings using both techniques. Given that the frequency content of S1 and S2 is different but overlap each other, and the duration of these sounds are also different, these heart sounds were represented in different IMF with different variances and entropies, in both techniques, but the ICEEMDAN offers a more consistent decomposition of S1 and S2 (they were concentrated in IMF 4-6). The decomposition of PCG signals into IMF has allowed us to identify the frequency components of the IMF in which these sounds are found.

3

A proposed algorithm for analysing heart sounds and calculating their time intervals

Eesee Abdulrahman K., Qassim Hassan M., Aziz Mothanna Sh.

Bio-Algorithms and Med-Systems

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2020

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Vol. 16, no. 1

art. no. 20190057

EN

Heart sounds play a crucial role in the clinical assessment of patients. Stethoscopes are used for detecting heart sounds and diagnosing potential abnormal conditions. However, several parameters of the cardiac sounds cannot be extracted by traditional stethoscopes. This paper presents a proposed algorithm based on peaks detection. Besides its ability of filtering the heart sounds signals, the time intervals of these sounds in addition to the heart rate were calculated by the proposed algorithm in an efficient way. Signals of the heart sounds from two sources were used to evaluate the efficiency of the algorithm. The first source was the data recorded from 14 participants, whereas the second source was the free data set sponsored by PASCAL. The algorithm showed different performance accuracy for detecting the main heart sounds based on the source of the data used in the study. The accuracy was 93.6% when using the data recorded from the first source, whereas it was 76.194% for the data of the second source.