Wyniki wyszukiwania - BazTech

1

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

|

2023

|

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

Automated detection of abnormal respiratory sound from electronic stethoscope and mobile phone using MobileNetV2

Liao Ximing, Wu Yin, Jiang Nana, Sun Jiaxing, Xu Wujian, Gao Shaoyong, Wang Jun, Li Ting, Wang Kun, Li Qiang

Biocybernetics and Biomedical Engineering

|

2023

|

Vol. 43, no. 4

763--775

EN

Auscultation, a traditional clinical examination method using a stethoscope to quickly assess airway abnormalities, remains valuable due to its real-time, non-invasive, and easy-to-perform nature. Recent advancements in computerized respiratory sound analysis (CRSA) have provided a quantifiable approach for recording, editing, and comparing respiratory sounds, also enabling the training of artificial intelligence models to fully excavate the potential of auscultation. However, existing sound analysis models often require complex computations, leading to prolonged processing times and high calculation and memory requirements. Moreover, the limited diversity and scope of available databases limits reproducibility and robustness, mainly relying on small sample datasets primarily collected from Caucasians. In order to overcome these limitations, we developed a new Chinese adult respiratory sound database, LD-DF RSdb, using an electronic stethoscope and mobile phone. By enrolling 145 participants, 9,584 high quality recordings were collected, containing 6,435 normal sounds, 2,782 crackles, 208 wheezes, and 159 combined sounds. Subsequently, we utilized a lightweight neural network architecture, MobileNetV2, for automated categorization of the four types of respiratory sounds, achieving an appreciable overall performance with an AUC of 0.8923. This study demonstrates the feasibility and potential of using mobile phones, electronic stethoscopes, and MobileNetV2 in CRSA. The proposed method offers a convenient and promising approach to enhance overall respiratory disease management and may help address healthcare resource disparities.

3

Czy ultrasonograf to współczesny stetoskop?

Urbanik Andrzej

Inżynier i Fizyk Medyczny

|

2022

|

Vol. 11, nr 3

203--204

PL

Autor porównuje zasadę działania następujących technik diagnostycznych: ultrasonografii, osłuchiwania oraz opukiwania. W konkluzji stwierdza, że ultrasonografia to nowoczesna forma opukiwania.

EN

The author compares the principles of operation of the following diagnostic techniques – ultrasound, auscultation and percussion. He concludes that ultrasound is a modern form of percussion.

4

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

|

2020

|

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.