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Electrical and Ultrasound Tomography for Early Urinary Incontinence detection

Electrical and Ultrasound Tomography for Early Urinary Incon...

引用

2023 International Interdisciplinary Phd Workshop, IIPhdW 2023

作者： Wojcik, dariusz rymarczyk, Tomasz Oleszek, Michal Golabek, Michal Niderla, Konrad Wsei University R&d Center Netrix S.A. Lublin Poland R&d Center Netrix S.A. Lublin Poland

ISBN: (纸本)9781665410847

This article presents the development of two innovative medical devices: a cheap ultrasound tomography (UST) system that uses beamforming technology for imaging and an electrical impedance tomography (EIT) system. Combining these two technologies allows for the creation of a hybrid tomography system that uses novel algorithms to monitor the lower urinary tract. The information obtained from this system is intended to be used to detect diseases early and present them to medical personnel to contribute to diagnosing and detecting diseases. Methods: The UST system utilizes beamforming technology to focus and steer ultrasound waves to produce images of the body's internal organs and tissues. The EIT system uses low-frequency electrical currents to image the body's electrical conductivity. Combining these two technologies allows for a hybrid tomography system that uses novel algorithms to monitor the lower urinary tract. Findings: The information obtained from the hybrid tomography system is intended to be used to detect diseases early and present them to medical personnel to contribute to diagnosing and detecting diseases. The system is expected to be particularly useful for detecting diseases related to the lower urinary tract. © 2023 IEEE.

关键词： Ultrasonic imaging

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Time Series recognition with Convolutional and recursive Neural Networks in BSPM

Time Series Recognition with Convolutional and Recursive Neu...

引用

2023 International Interdisciplinary Phd Workshop, IIPhdW 2023

作者： Wojcik, dariusz rymarczyk, Tomasz MacIura, Lukasz Oleszek, Michal Adamkiewicz, Przemyslaw Wsei University R&d Center Netrix S.A. Lublin Poland R&d Center Netrix S.A. Lublin Poland Wsei University Lublin Poland

ISBN: (纸本)9781665410847

This paper presents biomedical time series from Body Surface Potential Mapping (BSPM) recognition using various convolutional and recurrent neural network structures. The BSPM signal is in a form of time series form 102 channels located around the chest. The time series are then transformed to time windows to allow recognition of heart diseases. The several options of neural network structures were compared: one-dimensional convolutional neural network, Long-Short-Term Memory neural network, and Gated recurrent Unit neural network. The article showcases different convolutional and recurrent neural network architectures for recognizing patterns in biomedical time series measured with Body Surface Potential Mapping. The study compared three types of neural network structures: Long-Short-Term Memory neural network, Gated recurrent Unit neural network, and one-dimensional convolutional neural network. The main goal of paper is to find optimal machine learning solution for heart disease recognition on the basis of BSPM signal. The best results are obtained using model with GrU layer. © 2023 IEEE.

关键词： Time series

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Poster: Beyond the Labels - Transforming Classification Outputs into Continuous Conductivity Maps in Electrical Impedance Tomography 25

Poster: Beyond the Labels - Transforming Classification Outp...

引用

Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems

作者： dariusz Wójcik dariusz Majerek Tomasz rymarczyk Tomasz Łobodiuk Michał Oleszek Krzysztof Król WSEI University R&D Center Netrix Lublin Poland Lublin University of Technology Lublin Poland R&D Center Netrix Lublin Poland

ISBN: (纸本)9798400714795

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique for estimating conductivity distributions, but its inverse problems are computationally demanding and noise-sensitive. This paper presents a deep learning framework integrating classification and regression to estimate conductivity maps efficiently. The model employs MobileNetV2-inspired residual blocks in a U-Net-based encoder-decoder structure. regression is handled by weighting class probabilities with a predefined conductivity scale. Evaluated on simulated and real EIT data, the model accurately reconstructs conductivity maps, offering an efficient, real-time solution for biomedical and industrial imaging.

关键词： EIT

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Electrical and Ultrasound Tomography for Early Urinary Incontinence detection

Electrical and Ultrasound Tomography for Early Urinary Incon...

引用

International Interdisciplinary Phd Workshop (IIPhdW)

作者： dariusz Wójcik Tomasz rymarczyk Michał Oleszek Michał Gołabek Konrad Niderla R&D Center Netrix S.A. WSEI University Lublin Poland R&D Center Netrix S.A. Lublin Poland

关键词： Electrical impedance tomography Ultrasonic imaging Medical devices Array signal processing Ultrasonic variables measurement Personnel Biomedical monitoring

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Poster Abstract: A Wearable for Non-Invasive Monitoring and diagnosing Functional disorders of the Lower Urinary Tract 23

Poster Abstract: A Wearable for Non-Invasive Monitoring and ...

引用

Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

作者： Tomasz rymarczyk Mariusz Mazurek Oleksii Hyka dariusz Wójcik Marcin dziadosz Marcin Kowalski R&D Center Netrix S.A. WSEI University Lublin Poland Institute of Philosophy and Sociology of the Polish Academy of Sciences Warsaw Poland R&D Center Netrix S.A. Lublin Poland Research and Development Center Netrix S.A Lublin Poland Faculty of Mathematics and Technical Informatics LUT Lublin Poland WSEI University Lublin Poland

The aim of the research was to develop a new device for monitoring and diagnosing functional disorders of the lower urinary tract, which will enable measurement of muscle tension (EMG) and electrical impedance tomogra... 详细信息

ISBN: (纸本)9798400704147

关键词： electrical impedance tomography non-invasive method sensors image reconstruction machine learning medical monitoring

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Time Series recognition with Convolutional and recursive Neural Networks in BSPM

Time Series Recognition with Convolutional and Recursive Neu...

引用

International Interdisciplinary Phd Workshop (IIPhdW)

作者： dariusz Wójcik Tomasz rymarczyk Łukasz Maciura Michał Oleszek Przemysław Adamkiewicz WSEI University Lublin Poland R&D Center Netrix S.A. Lublin Poland

关键词： Heart recurrent neural networks Convolution Time series analysis Machine learning Logic gates Electrocardiography

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Tomographic Measuring Sensors System for Analysis and Visualization of Technological Processes

Tomographic Measuring Sensors System for Analysis and Visual...

引用

IEEE/IFIP International Conference on dependable Systems and Networks â€“ Supplemental Volume (dSN-S)

作者： Mariusz Mazurek Tomasz rymarczyk Grzegorz Kłosowski Michał Maj Przemysław Adamkiewicz Institute of Philosophy and Sociology of the Polish Academy of Sciences Warsaw Poland R&D Center Netrix S.A. Lublin University of Economics and Innovation in Lublin Lublin Poland Lublin University of Technology Lublin Lublin Poland

ISBN: (数字)9781728172606

ISBN: (纸本)9781728172613

The paper presents the results of research on the use of tomographic sensors to analyze industrial processes using dedicated measuring devices, image reconstruction algorithms and cyber-physical system (CPS). The work mainly focuses on ultrasound tomography and image reconstruction using determi-nistic methods and machine learning. The tests were carried out for synthetic data and laboratory measurements. The main advantage of the proposed system is the ability to analyze spatial data and their high processing speed. The presented research results indicate that ultrasonic process tomography gives the opportunity to analyze processes occurring inside the facility without disrupting production. The presented method enables the analysis and detection of obstacles, defects and various anomalies. Knowing the characteristics of the problem, the application allows you to choose the right method of image reconstruction.

关键词： Tomography Image reconstruction Artificial neural networks Ultrasonic variables measurement Ultrasonic imaging Sensors Cyber-physical systems

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