Time Series Recognition with Convolutional and Recursive Neural Networks in BSPM

Wójcik, Dariusz; Rymarczyk, Tomasz; Maciura, Łukasz; Adamkiewicz, Przemysław

doi:10.1109/iiphdw54739.2023.10124427

2023 International Interdisciplinary PhD Workshop (IIPhDW) 2023

DOI: 10.1109/iiphdw54739.2023.10124427

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Time Series Recognition with Convolutional and Recursive Neural Networks in BSPM

Dariusz Wójcik

Tomasz Rymarczyk

Łukasz Maciura³

et al.

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2024

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Cited by 3 publications

References 12 publications

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3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles

Ruiz-Mateos Serrano,

Aguzin,

Mitoudi-Vagourdi

et al. 2024

Biomaterials

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3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles

Ruiz-Mateos Serrano,

Aguzin,

Mitoudi-Vagourdi

et al. 2024

Biomaterials

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Monitoring the health of patients using a nodal potential map

Iskra,

Dziadosz,

Oleszek

et al. 2024

JoMS

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Purpose: The article presents the developed device and methods for monitoring the heart's condition using a nodal potential map. The proposal enables monitoring of the patient's health condition. Methods: Testing the heart's electrical activity in our device's case uses a "nodal potential map" BSPM (Body Surface Potential Mapping) technique. This is an extended version of the standard ECG measurement, which, instead of 12 leads, uses several dozen or even several hundred measurement electrodes placed in appropriate locations on the front and back of the chest. Results: The developed solution enables monitoring of heart function in a noninvasive way and is more precise than a standard ECG test. The collected measurements allowed us to determine the health and condition of the patient's heart—no anomalies were detected during its operation. Discussion: The next stage of work will be the development of machine learning algorithms trained on the measurements obtained from the vest. The results obtained so far indicate the potential of models classifying heart diseases.

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Analysis of the effectiveness of two different loss functions in training a neural network in lung image reconstruction using impedance tomography

Lalak-Dybała,

Stefaniak,

Olszewski

2024

JoMS

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The article presents research findings on developing a medical diagnostic system based on electrical impedance tomography technology. One of the key components of this project is developing a method for reconstructing the structure of human lungs using this technology. The authors of the article compared the effectiveness of two different loss functions in training a neural network, which is tasked with accurately replicating the lung structure based on electrical impedance tomography data. The researchers analyzed various approaches to calculating loss functions, including cosine embedding loss and InfoNCE loss. They compared the results obtained using these two functions to identify which one performs better in lung structure reconstruction. The findings of these studies may have significant implications for the development of diagnostic systems based on electrical impedance tomography and for improving the effectiveness of lung disease diagnosis. Additionally, the authors discuss potential future directions for the project, including possible applications of the research findings in clinical practice. Future research efforts may focus on optimizing neural network parameters, exploring alternative loss functions, or utilizing advanced machine learning techniques for even more precise lung structure reconstruction. The pursuit of improving such diagnostic systems could lead to significant advancements in the field of medicine, particularly in diagnosing and treating respiratory diseases.

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Time Series Recognition with Convolutional and Recursive Neural Networks in BSPM

Cited by 3 publications

References 12 publications

3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles

3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles

Monitoring the health of patients using a nodal potential map

Analysis of the effectiveness of two different loss functions in training a neural network in lung image reconstruction using impedance tomography

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