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Maternal Near Miss in the State of Rio Grande Do Norte (Brazil) Between 2003 to 2019: A Preliminary Analysis of Identification and Monitoring 9th

Maternal Near Miss in the State of Rio Grande Do Norte (Braz...

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9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022

作者： Rêgo, T.S. Silva, S.P. Vieira, D.V. Freitas-Júnior, R.A.O. Rodrigues, A.C. Graduate Program in Neuroengineering. Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute. Av. Alberto Santos Dumont 1560 Zona Rural. RN Macaiba59280-000 Brazil

ISBN: (纸本)9783031494093

The decline in maternal mortality rate is one of the global goals established by the United Nations. The analysis of maternal near miss makes it possible to identify the morbidities to which women are exposed and, consequently, to recognize the weaknesses of the health system in a region. A temporal trend ecological study was carried out, aiming to identify and monitor cases of maternal near miss between 2003 and 2019 in Rio Grande do Norte, Brazil. With the data made available by the Hospital Information System (SIH) of the Unified Health System (SUS) and with the help of the PySUS library, in Python programming language, it was possible to identify the years of higher incidences of maternal near miss and the main risks that pregnant or postpartum women suffer in the state. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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Spinal Cord Stimulation Modulates Rat Cortico-Basal Ganglia Locomotor Circuit

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Neuromodulation 2024年

作者： Fiorin, Fernando da Silva de Araújo e Silva, Mariane de Medeiros, Raquel E. Viana da Silva, Guilherme H. Rodrigues, Abner Cardoso Morya, Edgard Graduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute Macaíba Brazil

Objective: The cortico-basal ganglia circuit is crucial to understanding locomotor behavior and movement disorders. Spinal cord stimulation modulates that circuit, which is a promising approach to restoring motor functions. However, the effects of electrical spinal cord stimulation in the healthy brain motor circuit in pre- and postgait are poorly understood. Thus, this report aims to evaluate, through electrophysiological analyses, the dynamic spectral features of motor networks underlying locomotor initiation with spinal cord stimulation. Materials and Methods: Wistar male rats underwent spinal cord stimulation (current 30–150 μA, frequency 100, 333, and 500 Hz) with the electrophysiological recording of the caudate and putamen nuclei, primary and secondary motor cortices, and primary somatosensory cortex. Video tracking recorded treadmill locomotion and extracted the motor planning and gait initiation. Results: Spectral analysis of segments of gait initiation (pre- and postgait), with stimulation off, showed increased low-frequency activity. Postgait initiation showed increased alpha and beta rhythms and decreased delta rhythm with the stimulation off. Overall, the stimulation frequencies reduced alpha and beta rhythms in all brain areas during movement initiation. Regarding movement planning, such an effect was observed in the sensorimotor area, comprising the delta and alpha rhythms. Conclusion: This study showed a short-term effect of spinal cord stimulation on the brain areas of the motor circuit, suggesting possible facilitation of movement planning and starting through neuromodulation. Thus, the electrophysiological characterization of this study may contribute to understanding basal ganglia networks and developing new approaches to treat movement disorders in the gait initiation phase. © 2024 International Neuromodulation Society

关键词： Cortical-basal ganglia local field potentials movement planning spinal cord stimulation unitary spikes

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Expert-level sleep staging using an electrocardiography-only feed-forward neural network

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Computers in Biology and Medicine 2024年 176卷 108545-108545页

作者： Jones, Adam M. Itti, Laurent Sheth, Bhavin R. Neuroscience Graduate Program University of Southern California Los AngelesCA United States Department of Electrical & Computer Engineering University of Houston HoustonTX United States Center for NeuroEngineering and Cognitive Systems University of Houston HoustonTX United States

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomnography (PSG). Unfortunately, PSG is an expensive and cumbersome process involving numerous electrodes, often conducted in an unfamiliar clinic and annotated by a professional. Although commercial devices like smartwatches track sleep, their performance is well below PSG. To address these disadvantages, we present a feed-forward neural network that achieves gold-standard levels of agreement using only a single lead of electrocardiography (ECG) data. Specifically, the median five-stage Cohen's kappa is 0.725 on a large, diverse dataset of 5 to 90-year-old subjects. Comparisons with a comprehensive meta-analysis of between-human inter-rater agreement confirm the non-inferior performance of our model. Finally, we developed a novel loss function to align the training objective with Cohen's kappa. Our method offers an inexpensive, automated, and convenient alternative for sleep stage classification—further enhanced by a real-time scoring option. Cardiosomnography, or a sleep study conducted with ECG only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This advancement democratizes access to high-quality sleep studies, considerably enhancing the field of sleep medicine and neuroscience. It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions. © 2024 Elsevier Ltd

关键词： Electrocardiography

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Comparative Analysis of Control Methods for a Wind Turbine in Normal and Gusty Conditions

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SSRN 2024年

作者： Reddy, Yiza Srikanth Hur, Sung-Ho de Azevedo Dantas, André Felipe Oliveira School of Electronic and Electrical Engineering Kyungpook National University 80 Daehakro Bukgu Daegu41566 Korea Republic of Graduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute Av. Alberto Santos Dumont 1560 – Zona Rural-RN. Macaiba59280-000 Brazil

This paper provides a thorough evaluation of well-known control algorithms, i.e., proportional-integral (PI), model predictive control (MPC), and H-infinity (H∞) controllers, by implementing them in a full nonlinear wind turbine model under normal wind conditions in below and above-rated wind speeds. The simulation results illustrate that all the controllers perform satisfactorily. This study extends the MPC to include a feedforward (FF) loop (FF-MPC) that uses the wind speed information provided by a light detection and ranging (LiDAR) sensor, which measures the upcoming wind (in advance), to improve the overall control performance. The FF-MPC was tested under both normal and anomalous (i.e., gusty) wind conditions, and the results were compared with those of the standard feedback MPC (FB-MPC). The results demonstrate that the incorporation of the FF loop into the standard FB controller can improve the control performance, which can result in improved reliability and lifespan of the turbine. Furthermore, MPC is chosen to be augmented with an FF loop over PI and H∞ controllers owing to its versatility in handling constraints, nonlinearities, and multiple objectives, along with its inherent capability to incorporate preview wind data. All the controllers used in this study were implemented in C++ and subsequently compiled into a dynamic link library for compatibility using DNV’s full nonlinear Bladed software model of the Supergen 5-MW exemplar wind turbine. Using a high-fidelity aeroelastic wind turbine simulator, i.e., Bladed, various realistic wind conditions, including gusts, can be modeled and simulated to test the controllers. Thus, the process of a nacelle-based LiDAR used for measuring the wind and providing the control system can be modeled with realistic and accurate measurements, considering various phenomena, such as the wind evolution between the LiDAR and the rotor of the turbine, as well as inevitable uncertainties. © 2024, The Authors. All rights rese

关键词： Model predictive control

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Predicting Epileptic Seizures: Case Studies Harnessing Machine Learning

Predicting Epileptic Seizures: Case Studies Harnessing Machi...

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IEEE International Conference on Communications (ICC)

作者： Augusto Neto Liliane da Silva Renan Moioli Fabricio Brasil Joel J. P. C. Rodrigues Federal University of Rio Grande do Norte (UFRN) Natal RN Brazil Graduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute (IIN-ELS) Macaíba Brazil Instituto de Telecomunicações Portugal

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Epileptic seizure prediction is a critical patient-specific challenging task, relies on big data streams, and is essential for patient care. With the aid of recent advances, together with forthcoming technologies and powerful computing capabilities, smart healthcare attracts great attention in harnessing Intelligent Computing to yield seizure prediction and detection. However, it is unclear how even simple classification methods can be employed to carry out such challenging and mission-critical tasks. This paper investigates the performance impact of mainstream supervised Machine Learning techniques with different configurations in predicting epileptic seizures. A lab-premised testbed, along with neurophysiological data in dogs, enables the set of tests. Through analysis in the Area Under the ROC Curve (AUC) Key Performance Information (KPI), it was found that classification committees show improved performance capabilities than single classifiers. Overall, we believe this work represents a step towards making seizure prediction more accurate and widely available in different computational platforms.

关键词： Classification algorithms Machine learning Support vector machines Signal processing algorithms Prediction algorithms Electroencephalography Training

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Neural decoding with SVM and feature selection in a rat active tactile discrimination task

Neural decoding with SVM and feature selection in a rat acti...

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Gajadhar, Andy Anand Moioli, Renan Cipriano De Melo, Bianca Karla Amorim Sousa Kunicki, Ana Carolina Bione Peres, Andre Salles Cunha Rego, Thais Gaudencio Do Centro de Informática-Universidade Federal da Paraíba R. Dos Escoteiros s/n-Mangabeira João Pessoa PB 58055-000 Brazil Graduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute Estrada Vicinal n. 1560 Zona Rural Macaiba RN59280-000 Brazil

ISBN: (纸本)9781509060146

Understanding, mapping, and repairing the central nervous system would benefit from a better comprehension of how sensory stimuli information is conveyed by the neural activity underlying animal behavior. In this way, a promising approach, neural decoding, include machine-learning methods that associate patterns of brain activity with sensory stimuli. By solving a regression or classification problem, decoding contributes to highlighting the features from neural activity that best relate to a target variable. In this work, we built a support vector machine decoder coupled with a recursive feature selection algorithm to investigate the relevance of three distinct aspects of neural activity in an active tactile discrimination experiment involving rodents. Considering simultaneous recordings from a population of single-neuron spikes and local field potentials from the prefrontal, posterior parietal, primary sensory and visual cortices of nine Long-Evans rats, we were able to decode animal behavioral choices with approximately 95% of accuracy. When only the 30 most informative features from neural activity were used, accuracy values marginally decreased, indicating that a reduced set of features convey most of stimuli information, at least from the decoder perspective. Importantly, all regions contributed with features, and no single feature was associated to high classification accuracy. In addition to further unveiling the distributed nature of information processing in the brain, our results suggest that efficient brain-machine interfaces may be built with a reduced set of features. © 2018 IEEE.

关键词： Brain

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Neural decoding with SVM and feature selection in a rat active tactile discrimination task

Neural decoding with SVM and feature selection in a rat acti...

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International Joint Conference on Neural Networks (IJCNN)

作者： Andy Anand Gajadhar Renan Cipriano Moioli Bianca Karla Amorim Sousa de Melo Ana Carolina Bione Kunicki André Salles Cunha Peres Thaís Gaudencio do Rêgo Centro de Informática-Universidade Federal da Paraíba R. dos Escoteiros s/n-Mangabeira João Pessoa PB Graduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute Estrada Vicinal n. 1560 Zona Rural Macaiba RN Brazil

关键词： Decoding Task analysis Feature extraction Rats Neurons Support vector machines

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SOPHIA: Soft Orthotic Physiotherapy Hand Interactive Aid

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Frontiers in Mechanical Engineering 2017年 3卷

作者： McConnell, Alistair C. Vallejo, Marta Moioli, Renan Cipriano Brasil, Fabricio L. Secciani, Nicola Nemitz, Markus P. Riquart, Cecile P. Corne, David W. Vargas, Patricia A. Stokes, Adam A. Robotics Lab School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh United Kingdom Stokes Research Group School of Engineering Institute for Integrated Micro and Nano Systems The University of Edinburgh Edinburgh United Kingdom Graduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute Macaiba Brazil

This work describes the design, fabrication, and initial testing of a Soft Orthotic Physiotherapy Hand Interactive Aid (SOPHIA) for stroke rehabilitation. SOPHIA consists of (1) a soft robotic exoskeleton, (2) a microcontroller-based control system driven by a brain–machine interface (BMI), and (3) a sensorized glove for passive rehabilitation. In contrast to other rehabilitation devices, SOPHIA is the first modular prototype of a rehabilitation system that is capable of three tasks: aiding extension based assistive rehabilitation, monitoring patient exercises, and guiding passive rehabilitation. Our results show that this prototype of the device is capable of helping healthy subjects to open their hand. Finger extension is triggered by a command from the BMI, while using a variety of sensors to ensure a safe motion. All data gathered from the device will be used to guide further improvements to the prototype, aiming at developing specifications for the next generation device, which could be used in future clinical trials. © Copyright © 2017 McConnell, Vallejo, Moioli, Brasil, Secciani, Nemitz, Riquart, Corne, Vargas and Stokes.

关键词： Orthotics

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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