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Micro-electrode array (MEA) electrophysiological signal analysis by descriptive stochastic tests of the probability density structure

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Research on Biomedical engineering 2025年第2期41卷 1-13页

作者： Faria, Vinícius Naves Rezende Ramos, Marina Abadia Martinoia, Sérgio Destro-Filho, João-Batista Postgraduate Program in Electrical Engineering Universidade Federal de MG Uberlândia Brazil Postgraduate Program in Biomedical Sciences Universidade Federal de MG Uberlândia Brazil Division of Informatics Bioengineering Robotics and System Engineering Università Degli Studi Di Genova Genova Italy

Purpose: The primary objective of this study is to present, analyze, and quantitatively characterize the electrical signals obtained from neuronal cultures coupled to micro-electrode arrays (MEAs). Special attention is given to the temporal evolution of electrical activity across distinct geometric regions of the MEA devices. Methods: The proposed methodology involves signal acquisition, classical analysis based on spike and burst detection, and, critically, the characterization of the probability density structure of the signals. This is achieved through the quantification of Gaussianity—expressed as the percentage of segments non-Gaussian (PSNG)—and stationarity— expressed as the percentage of segments non-stationary (PSNE). Results: Initial analyses showed that first-order power spectral density quantifiers lacked the statistical robustness required to effectively describe the complex components of MEA signals. In contrast, PSNG and PSNE demonstrated strong potential in capturing the different stages of neuronal culture development. Cluster analysis further supported the discriminative capacity of these quantifiers. Conclusion: The phase corresponding to decreased neuronal activity and onset of cell death—a biologically critical stage—exhibited the strongest correlation among the studied variables. These results suggest a discernible biological organization and highlight the relevance of PSNG and PSNE as promising tools for assessing neuronal culture dynamics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Cell death

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Analysis and Control of Ball Displacement in Air Levitation Experiment by Using a Disturbance Rejection-Based Technique

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IEEE Access 2025年 13卷 71530-71550页

作者： Zachi, Alessandro R.L. Gouvêa, Josiel A. Correia, Carlos A.M. Pinto, Milena F. Petraglia, Mariane R. Haddad, Diego B. Andrade, Fabio A.A. Rio de Janeiro20271-204 Brazil Rio de Janeiro20271-204 Brazil Program of Electrical Engineering Coordination of Postgraduate and Research in Engineering Federal University of Rio de Janeiro Rio de Janeiro21941-617 Brazil Borre3184 Norway

This work describes the application of a disturbance rejection technique for controlling ball position in an air levitation experiment. The prototype consists of a light-mass ball that moves freely inside a vertical transparent cylindrical tube. The control objective is to force the ball position to track a desired vertical trajectory by defining a voltage control law for an air blower actuator. The system’s nonlinear mathematical model is presented and discussed. To deal with model uncertainties, nonlinearities and non measurable states, the design and the analysis of a Modified-Plant Active Disturbance Rejection Controller (MP-ADRC) are performed. ADRC-based controllers have been studied for decades, and their main characteristics are the use of an extended state observer (ESO) to estimate the non-measurable signals of the plant and a state-feedback control law in standard form fed by that observer. The key advantage of the adopted variant MP-ADRC, which contrasts with the basic methodology, is that it only requires knowledge of the sign (+/−) of plant’s control coefficient, rather than its exact value. This improves the well-known robustness property of the method, allowing its application to a general class of dynamical systems with uncertain parameters. By adopting and using such a control methodology, the resulting closed-loop scheme exhibits several advantageous characteristics: (i) it is robust to parametric uncertainties;(ii) it can compensate for external disturbances and unmodeled dynamics;(iii) it can deal with plants with slowly time-varying control coefficient;(iv) even for nonlinear plants, mathematical analysis using Laplace transform can always be performed. This work also presents and discusses simulation and experimental results obtained with the proposed control strategy applied to a real air levitation prototype. ©2013 The Authors.

关键词： Disturbance rejection

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Control of an Underactuated Quadrotor Using LQR and Visual Markers

Control of an Underactuated Quadrotor Using LQR and Visual M...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Gabriel Fernandes Andre G. S. Conceicao Humberto X. De Araújo Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

In this study, a robust Linear Quadratic Regulator (LQR) controller was developed and evaluated for the Bebop 2 quadrotor drone. Polytopic uncertainties in the dynamic model were taken into account in the control design, ensuring robustness. The drone's localization was achieved using fiducial markers and a Kalman Filter for state estimation, integrating visual and positional data. The controller design utilized Linear Matrix Inequalities (LMIs) to optimize performance while accounting for system constraints. A circular trajectory in three-dimensional space was employed to assess the controller's effectiveness in simulation. Results demonstrated precise trajectory tracking, stability, and robust performance despite parameter uncertainties. These findings highlight the applicability of the proposed framework for real-world scenarios involving underactuated UAVs.

关键词： Location awareness Visualization Uncertain systems Uncertainty Trajectory tracking Stability analysis Trajectory Vehicle dynamics Quadrotors Drones

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Comparative Analysis of Clusterization Methods Applied to a Solid-State LiDAR

Comparative Analysis of Clusterization Methods Applied to a ...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Henrique N. Poleselo Andre G. S. Conceicao Postgraduate Program in Electrical Engineering Federal University of Bahia Salvador Brazil Department of Electrical and Computer Engineering Federal University of Bahia Salvador Brazil

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Object segmentation is one of the main activities for the robot to create a sense of its environment. This task is a precursor to other activities, such as autonomous navigation in a given environment. Through sensors such as LiDAR, it's possible to generate high-resolution three-dimensional maps of the environment in which the robot is located, thus enabling their interpretation so that tasks such as object segmentation can be performed. In this article, the DBSCAN and HDBSCAN unsupervised clustering methods are explored. Results in a simulated environment in Gazebo together with Robot Operating System ROS framework for capturing sensory data from LiDAR Livox Mid-70 coupled to a mobile robot show the performance of such techniques through comparisons.

关键词： Laser radar Operating systems Clustering methods Object segmentation Robot sensing systems Sensors Mobile robots Autonomous robots

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Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss

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Clinical Oral Investigations 2025年第4期29卷 1-8页

作者： Cassiano, Luanny de Brito Avelino da Silva, Jordão Paulino Cassiano Martins, Agnes Andrade Barbosa, Matheus Targino Rodrigues, Katryne Targino Barbosa, Ádylla Rominne Lima da Silva Gomes, Gabriela Ellen Maia, Paulo Raphael Leite de Oliveira, Patrícia Teixeira de Sousa Lopes, Maria Luiza Diniz da Silva, Ivanovitch Medeiros Dantas de Aquino Martins, Ana Rafaela Luz Department of Dentistry Federal University of Rio Grande do Norte - UFRN Rio Grande do Norte Natal Brazil Postgraduate Program in Electrical and Computer Engineering Center of Technology Rio Grande do Norte Natal Brazil Department of Dentistry Federal University of Rio Grande do Norte Av. Senador Salgado Filho 1787 Rio Grande do Norte Natal 59056-000 Brazil

Objective: To develop an artificial intelligence model based on convolutional neural network for detecting and measuring periodontal radiographic bone loss (RBL). Materials and methods: Keypoint annotations were carried out in 595 digital bitewing radiographic images using a Computer Vision Annotation Tool. The dataset was splitted: 416 of these images were trained using the You Only Look Once version 8 architecture with pose estimation (YOLO-v8-pose), 119 images were destined for the validation set, and 60 images were used for the test set, resulting in a model capable of detecting keypoints related to the cementoenamel junction (CEJ) and alveolar bone crest (ABC). In order to evaluate the performance of the obtained model, the following metrics were analyzed: F1-Score, precision, sensitivity and mean average precision (mAP). Then, an algorithm was implemented to measure the RBL by calculating the Euclidean distance between CEJ and ABC. Results: The model achieved an F1-Score of 66,89%, precision of 61,1%, a sensitivity of 73,9% and an mAP of 73.8%. Conclusions: The developed model and its algorithm for identifying and measuring periodontal radiographic bone loss demonstrated promising performance, thereby presenting a potential tool for assisting in periodontal diagnosis. Further studies comparing the developed model with manual measurements performed by specialists are necessary for its validation. Clinical relevance: Applying artificial intelligence in clinical dental practice can support diagnosis, streamline clinical workflows, and inform treatment planning, representing a significant advancement in dental automation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Alveolar bone loss Computer neural networks Deep learning Periodontitis Radiography

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An End-to-End Framework for Neonatal Asphyxia Detection through Video Analysis

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IEEE Access 2025年 13卷 74689-74704页

作者： Kurnianingsih Nobukawa, Sou Widyawati, Melyana Nurul Pramana, Cipta Aji, Nurseno Bayu Thohari, Afandi Nur Aziz Hendrawati, Dwiana Sato-Shimokawara, Eri Kubota, Naoyuki Department of Electrical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Central Java Semarang50275 Indonesia Department of Computer Science Chiba Institute of Technology 2-17-1 Tsudanuma Chiba Narashino275-0016 Japan Department of Postgraduate Program Poltekkes Kemenkes Semarang Jl. Tirto Agung Pedalangan Kecamatan Banyumanik Central Java Semarang50268 Indonesia Department of Obstetrics and Gynecology KRMT Wongsonegoro Hospital Jl. Tembalang Central Java Semarang50272 Indonesia Department of Mechanical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Central Java Semarang50275 Indonesia Graduate School of Systems Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

Identifying asphyxia using computer vision in real-world settings poses challenges due to varying video quality, diverse lighting conditions, and subtle color changes in the newborn's skin. This study presents an end-to-end framework for automated neonatal asphyxia detection using time series video analysis and makes three key contributions. First, the proposed framework integrates YOLOv8-based instance segmentation with advanced feature extraction across multiple color spaces and texture analysis to detect neonatal asphyxia through the multi-modal analysis of skin features in video streams. Second, we introduce a new quality-aware temporal analysis framework that includes adaptive quality assessment for evaluating frames in real time, multi-stage feature stability tracking across temporal windows, hysteresis-based decision logic for ensuring temporal consistency, and LightGBM classification with comprehensive feature engineering to assess severity. Third, we provide a curated time series video dataset of 12,973 frames from 45 neonates, of which some were healthy, and some had asphyxia of varying severity. The findings show that the YOLOv8-based instance segmentation achieved a mean average precision (mAP@0.5) of 0.925 for accurate skin region isolation, and the LightGBM classifier outperformed traditional models with an accuracy of 0.998 and an F1-score of 0.998. The system maintains real-time processing at 30 FPS for normal and mild asphyxia cases with a minor reduction to 20 FPS in more challenging scenarios and exhibits robust temporal stability across severity levels, with consistency scores above 0.90. This framework has the potential to enhance neonatal care through continuous monitoring and timely intervention. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Deep learning

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Evaluation of Maternal and Fetal S-Values from Iodine Isotopes in Nuclear Medicine Using the Monte Carlo Method

SSRN

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SSRN 2025年

作者： Coelho, Nathalia B.M. Alves, Arthur de Souza Borges Zuchet Santos, Carla J. Neves, Lúcio P. Perini, Ana P. Belinato, Walmir Ferreira, José Rafael S. Santos, William S. Postgraduate Program in Biomedical Engineering Faculty of Electrical Engineering Federal University of Uberlândia Uberlândia Brazil Institute of Physics Federal University of Uberlândia Uberlândia Brazil Federal Institute of Bahia - IFBA BA Vitória da Conquista Brazil Department of Physics Federal University of Sergipe São Cristóvão Brazil Universidade Federal da Bahia Brazil

The use of radionuclides for therapeutic and diagnostic purposes is the primary function of nuclear medicine. Among these radionuclides, the iodine family stands out as isotopes that can serve various purposes, including brachytherapy, scintigraphy, and research. The internal dosimetry of these nuclides becomes even more important when considering high-risk groups such as pregnant patients, as it involves concerns for both the mother and the fetus. This study aims to use Monte Carlo simulations to calculate S-values for a set of target organs in both the pregnant woman and the fetus, considering different source organs in the mother and three iodine isotopes: 131I, 125I, and 123I. The results indicated that 131I, with the highest emission energy, presented a high dose deposition pattern in target organs farther from the source organs, while 125I, with the lowest energy, showed a localized dose absorption pattern in adjacent organs. The highest S-values (all in mGy/MBq.s) were obtained for the pregnant woman, with values of 5.8E-05 in the thyroid using 123I and 125I, and 3.7E-04 in the bladder using 131I. For fetal organs, the most irradiated were the brain and skull using 131I, with values of 6.3E-06 and 5.6E-06, respectively, while the lowest values were associated with the use of 125I in source organs distant from the target organ, with contributions as low as 1E-12 for the thyroid as a source. This work presents a qualitative and quantitative study of the internal dosimetry of a pregnant patient and her fetus, addressing variables such as radioactive isotope and source and target organs, serving as a reference for dosimetric calculations deemed necessary in clinical practices to minimize the dose received by this critical group. © 2025, The Authors. All rights reserved.

关键词： Nuclear medicine

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The effect of smart mirror environment on proprioception factors of children with Down syndrome

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Research on Biomedical engineering 2020年第2期36卷 187-195页

作者： Valencia-Jimenez, Nicolas da Luz, Sheila Santos, Dayse Souza, Mariane Bastos, Teodiano Frizera, Anselmo Postgraduate Program in Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Postgraduate Program in Biotechnology Federal University of Espirito Santo Vitoria Brazil Postgraduate Program in Psychology Federal University of Espirito Santo Vitoria Brazil

Purpose: Down syndrome (DS) is the most common cause of intellectual disability worldwide and the most common genetic disorder, which generates problems regarding the functionality and independence to perform activities of daily living (ADLs). This study aims at developing a smart mirror environment (SME) to assist in the evaluation of proprioception skills in children with Down syndrome (CwDS) through an automated analysis of corporal movements with RGB-D camera. Methods: The SME is implemented following a clinical protocol to objectively elicit cognitive and proprioceptive skills, in addition to practical needs and requirements raised by psychologists and physical therapists. The system is proposed as a complement to conventional assessments and motor development evaluation, providing support to professionals in generating objective parameters for analysis during physical training and therapy. Results: The results demonstrate the significance of the parameters obtained in the assessment protocol with the virtual immersion generated by the SME, where the proprioception was evaluated and the kinesthetic sense was improved in 29.9%. In the same way, we found an important influence of the intervention with visual feedback, which can help CwDS to develop their skills more efficiently, such as in the body drawings made after using the SME, presenting human body design improvement compared with the drawings made before the interaction. Conclusion: This work enhances the importance of our quantitative assessment, which provides objective parameters of the proprioception progress in CwDS, indicating that they need more stimulation to satisfactorily develop certain body skills. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Mirrors

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Possible solutions for oxygenation support in critically ill patients with COVID-19

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Research on Biomedical engineering 2021年第2期37卷 389-402页

作者： de Souza, Daniel Baldoino de Oliveira Andrade, Adriano Milagre, Selma Terezinha Pereira, Adriano Alves Centre for Innovation and Technology Assessment in Health Postgraduate Program in Electrical and Biomedical Engineering Faculty of Electrical Engineering Federal University of Uberlândia Uberlândia Brazil

Purpose: Due to the large number of patients with respiratory deficiency during the COVID-19 pandemic, several governments and their respective health care services have been studying ways to complement the care provided by offering immediate solutions. In view of this, the aim of this study was to carry out a systematic review of the advantages and disadvantages of possible solutions in oxygenation support. Methods: This systematic review used the PRISMA-P methodology and sought to list alternatives in oxygenation support that are being applied and studied worldwide. A bibliographic search was conducted in the MEDLINE and Cochrane Central databases, using the keywords SARS-CoV-2, COVID19, or coronavirus;combined with extracorporeal membrane oxygenation (ECMO), mechanical ventilation, mechanical ventilation support, low-cost, anesthesia, anesthesia machine, and ventilation therapy. The records were also found in the gray literature. Results: The search found 85 publications of which 41 articles were considered after excluding duplicate articles, reading the title and summary, and reading the articles in full. The oxygenation supports identified in these publications were the following: ECMO, shared mechanical ventilator, fast or low-cost production equipment, high-flow nasal cannula (HFNC), non-invasive ventilation, and use of anesthesia equipment as a mechanical ventilator. Conclusion: This study demonstrated the importance of a trained clinical team in the application of technologies. The alternatives found for support oxygenation require a more robust clinical evaluation to demonstrate their efficacy and safety for the COVID-19 patient. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： Oxygenation

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Terminal models for switching overvoltage studies in electrical machines

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WSEAS Transactions on Circuits and Systems 2006年第3期5卷 319-324页

作者： Guardado, J.L. Venegas, V. Melgoza, E. ITM Postgraduate Program in Electrical Engineering P.O. Box 262 Morelia Mich. Mexico

In this paper, terminal models for high voltage machine windings are developed. The models are useful for simulating transient overvoltages due to switching operations on industrial installations. The models are based on network synthesis of the winding terminal impedance. The process of synthesis is carried out by taking into account the winding resonant frequencies. The computer models arte validated by a comparison of measurements and the calculated transient overvoltages and winding impedances in a high voltage motor.

关键词： Machine windings

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