The remarkable functionality of the brain is made possible by the metabolism (chemical reaction) of oxygen (O₂) and nutrients in the brain. These metabolism components are supplied to the brain by an intricate blood circulatory system (vasculature). The blood brain barrier (BBB), which is the central topic of this book, determines the rate of transfer from the blood to the brain tissue.
In particular, mathematical models are developed for mass transfer across the BBB based on partial differential equations (PDEs) applied to the blood capillaries, the endothelial membrane, and the brain tissue. The PDEs derived from mass balances and computer routines in R are presented for the numerical (computer-based) solution of the PDEs. The computed concentration profiles of the transferred components are functions of time and space within the BBB system, i.e., spatiotemporal solutions.
The R routines and the associated numerical algorithms for computing the numerical solutions are discussed in detail. The discussion is introductory, without formal mathematics, e.g., theorems and proofs. The general methodology (algorithm) for numerical PDE solutions is the method of lines (MOL).
The models are used to study the transfer of oxygen and nutrients, harmful substances that should not enter the brain such as chemicals and pathogens (viruses, bacteria), and therapeutic drugs. The intent of the book is to provide a quantitative approach to the study of BBB dynamics using a computer-based methodology programmed in R, a quality open-source scientific programming system that is easily downloaded from the Internet for execution on modest computers.
Автор: Natasha Lepore; Jorge Brieva; Eduardo Romero; Dani Название: Processing and Analysis of Biomedical Information ISBN: 3030138348 ISBN-13(EAN): 9783030138349 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the First International SIPAIM Workshop on Processing and Analysis of Biomedical Information, SaMBa 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 14 full papers presented were carefully reviewed and selected for inclusion in this volume.
Название: Bioimage Data Analysis Workflows ISBN: 303022385X ISBN-13(EAN): 9783030223854 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows.The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images.
Автор: Elgendi, Mohamed Название: PPG Signal Analysis ISBN: 1138049719 ISBN-13(EAN): 9781138049710 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book serves as a current resource for Photoplethysmogram (PPG) signal analysis using MATLAB (R). This technology is critical in the evaluation of medical and diagnostic data utilized in mobile devices.
Описание: Addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.
Автор: Changming Sun; Tomasz Bednarz; Tuan D. Pham; Pasca Название: Signal and Image Analysis for Biomedical and Life Sciences ISBN: 3319109839 ISBN-13(EAN): 9783319109831 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Part I Signal Analysis
1. Visual Analytics of Signalling Pathways Using Time Profiles; David K. G. Ma, Christian Stolte, Sandeep Kaur, Michael Bain and Se an I. O'Donoghue
2. Modeling of Testosterone Regulation by Pulse-modulated Feedback; Per Mattsson and Alexander Medvedev
3. Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequence; Madawa Priyadarshana, Tatiana Polushina and Georgy Sofronov
4. Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson's Disease; Daniel Jansson, Alexander Medvedev, Hans Axelson and Dag Nyholm
5. Identification of the Reichardt Elementary Motion Detector Model; Egi Hidayat, Alexander Medvedev and Karin Nordstrцm
6. Multi-Complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics; Valeriy Gavrishchaka, Olga Senyukova and Kristina Davis
7. Development of a Motion Capturing and Load Analyzing System for Caregivers Aiding a Patient to Sit Up in Bed; Akemi Nomura, Yasuko Ando, Tomohiro Yano, Yosuke Takami, Shoichiro Ito, Takako Sato, Akinobu Nemoto and Hiroshi Arisawa
8. Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-Scale K-means; Guohun Zhu, Yan Li, Peng (Paul) Wen and Shuaifang Wang
9. Tracking of EEG Activity Using Motion Estimation to Understand Brain Wiring; Humaira Nisar, Aamir Saeed Malik, Rafi Ullah, Seong-O Shim, Abdullah Bawakid, Muhammad Burhan Khan and Ahmad Rauf Subhani
Part II Image Analysis
10. Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery; Rongxin Li, Dadong Wang, Changming Sun, Ryan Lagerstrom, Hai Tan, You He and Tiqiao Xiao
11. Cloud Based Toolbox for Image Analysis, Processing and Reconstruction Tasks; Tomasz Bednarz, Dadong Wang, Yulia Arzhaeva, Ryan Lagerstrom, Pascal Vallotton, Neil Burdett, Alex Khassapov, Piotr Szul, Shiping Chen, Changming Sun, Luke Domanski, Darren Thompson, Timur Gureyev and John A. Taylor
12. Pollen Image Classification Using the Classifynder System: Algorithm Comparison and a Case Study on New Zealand Honey; Ryan Lagerstrom, Katherine Holt, Yulia Arzhaeva, Leanne Bischof, Simon Haberle, Felicitas Hopf and David Lovell
13. Digital Image Processing and Analysis for Activated Sludge Wastewater Treatment; Muhammad Burhan Khan, Xue Yong Lee, Humaira Nisar, Choon Aun Ng, Kim Ho Yeap and Aamir Saeed Malik
14. A Complete System for 3D Reconstruction of Roots for Phenotypic Analysis; Pankaj Kumar, Jinhai Cai and Stan Miklavcic
Автор: Schiesser William E Название: Computational Chemotaxis Models For Neurodegenerative Disease ISBN: 9813207450 ISBN-13(EAN): 9789813207455 Издательство: World Scientific Publishing Рейтинг: Цена: 12830.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The mathematical model presented in this book, based on partial differential equations (PDEs) describing attractant-repellent chemotaxis, is offered for a quantitative analysis of neurodegenerative disease (ND), e.g., Alzheimer's disease (AD). The model is a representation of basic phenomena (mechanisms) for diffusive transport and biochemical kinetics that provides the spatiotemporal distribution of components which could explain the evolution of ND, and is offered with the intended purpose of providing a small step toward the understanding, and possible treatment of ND.
The format and emphasis of the presentation is based on the following elements:
A statement of the PDE system, including initial conditions (ICs), boundary conditions (BCs) and the model parameters.
Algorithms for the calculation of numerical solutions of the PDE system with a minimum of mathematical formality.
A set of R routines for the calculation of numerical solutions, including a detailed explanation of all of the sections of the code. The R routines can be executed after a straightforward download of R, an open-source scientific computing system available from the Internet.
Presentation of the numerical solutions, particularly in graphical (plotted) format to enhance the visualization of the solution.
Summary and conclusions concerning the principal results from the model that might serve as the basis for a next step in the modeling of ND.
In other words, a methodology for numerical PDE modeling is presented that is flexible, open ended and readily implemented on modest computers. If the reader is interested in an alternate model, it might possibly be implemented by: (1) modifying and/or extending the current model (for example, by adding terms to the PDEs or adding additional PDEs), or (2) using the reported routines as a prototype for the model of interest.
These suggestions illustrate an important feature of computer-based modeling, that is, the readily available procedure of numerically experimenting with a model. The current model is offered as only a first step toward the resolution of this urgent medical problem.
Автор: Changming Sun; Tomasz Bednarz; Tuan D. Pham; Pasca Название: Signal and Image Analysis for Biomedical and Life Sciences ISBN: 3319359029 ISBN-13(EAN): 9783319359021 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Part I Signal Analysis
1. Visual Analytics of Signalling Pathways Using Time Profiles; David K. G. Ma, Christian Stolte, Sandeep Kaur, Michael Bain and Se an I. O'Donoghue
2. Modeling of Testosterone Regulation by Pulse-modulated Feedback; Per Mattsson and Alexander Medvedev
3. Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequence; Madawa Priyadarshana, Tatiana Polushina and Georgy Sofronov
4. Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson's Disease; Daniel Jansson, Alexander Medvedev, Hans Axelson and Dag Nyholm
5. Identification of the Reichardt Elementary Motion Detector Model; Egi Hidayat, Alexander Medvedev and Karin Nordstrцm
6. Multi-Complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics; Valeriy Gavrishchaka, Olga Senyukova and Kristina Davis
7. Development of a Motion Capturing and Load Analyzing System for Caregivers Aiding a Patient to Sit Up in Bed; Akemi Nomura, Yasuko Ando, Tomohiro Yano, Yosuke Takami, Shoichiro Ito, Takako Sato, Akinobu Nemoto and Hiroshi Arisawa
8. Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-Scale K-means; Guohun Zhu, Yan Li, Peng (Paul) Wen and Shuaifang Wang
9. Tracking of EEG Activity Using Motion Estimation to Understand Brain Wiring; Humaira Nisar, Aamir Saeed Malik, Rafi Ullah, Seong-O Shim, Abdullah Bawakid, Muhammad Burhan Khan and Ahmad Rauf Subhani
Part II Image Analysis
10. Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery; Rongxin Li, Dadong Wang, Changming Sun, Ryan Lagerstrom, Hai Tan, You He and Tiqiao Xiao
11. Cloud Based Toolbox for Image Analysis, Processing and Reconstruction Tasks; Tomasz Bednarz, Dadong Wang, Yulia Arzhaeva, Ryan Lagerstrom, Pascal Vallotton, Neil Burdett, Alex Khassapov, Piotr Szul, Shiping Chen, Changming Sun, Luke Domanski, Darren Thompson, Timur Gureyev and John A. Taylor
12. Pollen Image Classification Using the Classifynder System: Algorithm Comparison and a Case Study on New Zealand Honey; Ryan Lagerstrom, Katherine Holt, Yulia Arzhaeva, Leanne Bischof, Simon Haberle, Felicitas Hopf and David Lovell
13. Digital Image Processing and Analysis for Activated Sludge Wastewater Treatment; Muhammad Burhan Khan, Xue Yong Lee, Humaira Nisar, Choon Aun Ng, Kim Ho Yeap and Aamir Saeed Malik
14. A Complete System for 3D Reconstruction of Roots for Phenotypic Analysis; Pankaj Kumar, Jinhai Cai and Stan Miklavcic
Автор: Hemanth, D. Jude Название: Intelligent Data Analysis for Biomedical Applications ISBN: 0128155531 ISBN-13(EAN): 9780128155530 Издательство: Elsevier Science Рейтинг: Цена: 17180.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases.
Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection
Contains an analysis of medical databases to provide diagnostic expert systems
Addresses the integration of intelligent data analysis techniques within biomedical information systems
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more.
This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.
Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction
Explains how to apply machine learning techniques to EEG, ECG and EMG signals
Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series
Автор: Schiesser William E Название: Computational Chemotaxis Models For Neurodegenerative Disease ISBN: 9813208910 ISBN-13(EAN): 9789813208919 Издательство: World Scientific Publishing Рейтинг: Цена: 7603.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The mathematical model presented in this book, based on partial differential equations (PDEs) describing attractant-repellent chemotaxis, is offered for a quantitative analysis of neurodegenerative disease (ND), e.g., Alzheimer's disease (AD). The model is a representation of basic phenomena (mechanisms) for diffusive transport and biochemical kinetics that provides the spatiotemporal distribution of components which could explain the evolution of ND, and is offered with the intended purpose of providing a small step toward the understanding, and possible treatment of ND.
The format and emphasis of the presentation is based on the following elements:
A statement of the PDE system, including initial conditions (ICs), boundary conditions (BCs) and the model parameters.
Algorithms for the calculation of numerical solutions of the PDE system with a minimum of mathematical formality.
A set of R routines for the calculation of numerical solutions, including a detailed explanation of all of the sections of the code. The R routines can be executed after a straightforward download of R, an open-source scientific computing system available from the Internet.
Presentation of the numerical solutions, particularly in graphical (plotted) format to enhance the visualization of the solution.
Summary and conclusions concerning the principal results from the model that might serve as the basis for a next step in the modeling of ND.
In other words, a methodology for numerical PDE modeling is presented that is flexible, open ended and readily implemented on modest computers. If the reader is interested in an alternate model, it might possibly be implemented by: (1) modifying and/or extending the current model (for example, by adding terms to the PDEs or adding additional PDEs), or (2) using the reported routines as a prototype for the model of interest.
These suggestions illustrate an important feature of computer-based modeling, that is, the readily available procedure of numerically experimenting with a model. The current model is offered as only a first step toward the resolution of this urgent medical problem.
Автор: Sanei, Saeid , Hassani, Hossein Название: Singular Spectrum Analysis of Biomedical Signals ISBN: 0367377047 ISBN-13(EAN): 9780367377045 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Поставка под заказ.
Описание:
Recent advancements in signal processing and computerised methods are expected to underpin the future progress of biomedical research and technology, particularly in measuring and assessing signals and images from the human body. This book focuses on singular spectrum analysis (SSA), an effective approach for single channel signal analysis, and its bivariate, multivariate, tensor based, complex-valued, quaternion-valued and robust variants.
SSA currently has numerous applications in detecting abnormalities in quasi-periodic biosignals, such as electrocardiograms, (ECGs or EKGs), oxygen levels, arterial pressure, and electroencephalograms (EEGs). Singular Spectrum Analysis of Biomedical Signals presents relatively newly applied concepts for biomedical applications of SSA, including:
Signal source separation, extraction, decomposition, and factorization
Physiological, biological, and biochemical signal processing
A new SSA grouping algorithm for filtering and noise reduction of genetics data
Prediction of various clinical events
The book introduces a new mathematical and signal processing technique for the decomposition of widely available single channel biomedical data. It also provides illustrations of new signal processing results in the form of signals, graphs, images, and tables to reinforce understanding of the related concepts.
Singular Spectrum Analysis of Biomedical Signals enhances current clinical knowledge and aids physicians in improving diagnosis, treatment and monitoring some clinical abnormalities. It also lays groundwork for progress in SSA by making suggestions for future research.
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