Ica For Data Analysis And Interpretation
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Ica For Data Analysis And Interpretation
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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-04-13
Ica For Data Analysis And Interpretation written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-13 with Science categories.
ICA for Data Analysis and Interpretation delves into the fascinating world of Independent Component Analysis (ICA), a powerful signal processing technique that has revolutionized data analysis across diverse fields. This comprehensive guide provides a thorough exploration of ICA, from its mathematical foundations to its wide-ranging applications. Within the pages of this book, readers will embark on a journey through the theoretical underpinnings of ICA, gaining a deep understanding of the statistical models and algorithms that drive its success. The book delves into various ICA algorithms, comparing their strengths and limitations, and equipping readers with the knowledge to select the most appropriate algorithm for their specific needs. Moving beyond theory, the book showcases the practical applications of ICA in various domains. Readers will learn how ICA can be harnessed to separate signals, extract meaningful features from data, and uncover hidden patterns in complex datasets. Real-world examples and case studies illustrate the transformative power of ICA in fields such as speech enhancement, image processing, financial analysis, and neuroscience. With its clear explanations, insightful examples, and comprehensive coverage, ICA for Data Analysis and Interpretation is an invaluable resource for researchers, practitioners, and students seeking to master the art of ICA. Whether you're a data scientist, engineer, or simply someone fascinated by the power of data analysis, this book will provide you with the knowledge and tools to unlock the full potential of ICA. Discover the transformative power of ICA and gain the skills to uncover hidden insights from your data. ICA for Data Analysis and Interpretation is your essential guide to this cutting-edge technique, empowering you to solve complex problems and make informed decisions in an increasingly data-driven world. If you like this book, write a review on google books!
Independent Component Analysis
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Author : James V. Stone
language : en
Publisher: MIT Press
Release Date : 2004
Independent Component Analysis written by James V. Stone and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
A tutorial-style introduction to a class of methods for extracting independent signals from a mixture of signals originating from different physical sources; includes MatLab computer code examples. Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions. In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method. An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls "the mathematical nuts and bolts" of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA. Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code.
On Statistical Pattern Recognition In Independent Component Analysis Mixture Modelling
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Author : Addisson Salazar
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-20
On Statistical Pattern Recognition In Independent Component Analysis Mixture Modelling written by Addisson Salazar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-20 with Technology & Engineering categories.
A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.
Independent Component Analysis
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Author : Aapo Hyvärinen
language : en
Publisher: John Wiley & Sons
Release Date : 2004-03-22
Independent Component Analysis written by Aapo Hyvärinen and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-22 with Science categories.
A comprehensive introduction to ICA for students and practitioners Independent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and more. Independent Component Analysis is divided into four sections that cover: * General mathematical concepts utilized in the book * The basic ICA model and its solution * Various extensions of the basic ICA model * Real-world applications for ICA models Authors Hyvarinen, Karhunen, and Oja are well known for their contributions to the development of ICA and here cover all the relevant theory, new algorithms, and applications in various fields. Researchers, students, and practitioners from a variety of disciplines will find this accessible volume both helpful and informative.
Independent Component Analysis And Signal Separation
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Author : Tulay Adali
language : en
Publisher: Springer
Release Date : 2009-03-16
Independent Component Analysis And Signal Separation written by Tulay Adali and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-03-16 with Computers categories.
This book constitutes the refereed proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009, held in Paraty, Brazil, in March 2009. The 97 revised papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on theory, algorithms and architectures, biomedical applications, image processing, speech and audio processing, other applications, as well as a special session on evaluation.
Entropy Measures For Data Analysis
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Author : Karsten Keller
language : en
Publisher: MDPI
Release Date : 2019-12-19
Entropy Measures For Data Analysis written by Karsten Keller and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-19 with Science categories.
Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses. The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given.
Handbook Of Neuroimaging Data Analysis
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Author : Hernando Ombao
language : en
Publisher: CRC Press
Release Date : 2016-11-18
Handbook Of Neuroimaging Data Analysis written by Hernando Ombao and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-18 with Mathematics categories.
This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.
Independent Component Analysis
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Author : Te-Won Lee
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Independent Component Analysis written by Te-Won Lee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Computers categories.
Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, blind source separation by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, telecommunications, medical signal-processing and several data mining issues. This book presents theories and applications of ICA and includes invaluable examples of several real-world applications. Based on theories in probabilistic models, information theory and artificial neural networks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, nonlinear PCA, Bussgang algorithm and cumulant-based methods are reviewed and put in an information theoretic framework to unify several lines of ICA research. An algorithm is presented that is able to blindly separate mixed signals with sub- and super-Gaussian source distributions. The learning algorithms can be extended to filter systems, which allows the separation of voices recorded in a real environment (cocktail party problem). The ICA algorithm has been successfully applied to many biomedical signal-processing problems such as the analysis of electroencephalographic data and functional magnetic resonance imaging data. ICA applied to images results in independent image components that can be used as features in pattern classification problems such as visual lip-reading and face recognition systems. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification. Independent Component Analysis: Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation. It is essential reading for researchers and practitioners with an interest in ICA.
Interpreting Cardiac Electrograms
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Author : Kevin Michael
language : en
Publisher: BoD – Books on Demand
Release Date : 2017-10-18
Interpreting Cardiac Electrograms written by Kevin Michael and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-18 with Medical categories.
This is a reference book aimed at cardiologists, electrophysiologists and fellows in training. It presents an expansive review of cardiac electrogram interpretation in a collation of manuscripts that represent clinical studies, relevant anecdotal cases and basic science chapters evaluating cardiac signal processing pertaining to persistent atrial fibrillation. A diagnostic approach to arrhythmias using a standard ECG, the signal average ECG and fetal ECG is highlighted. Intracardiac ICD electrograms are also explored in terms of trouble shooting and device programming.
Next Frontier Medical Devices And Embedded Systems Harnessing Biomedical Engineering Artificial Intelligence And Cloud Powered Big Data Analytics For Smarter Healthcare Solutions
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Author : Sai Teja Nuka
language : en
Publisher: Deep Science Publishing
Release Date : 2025-06-06
Next Frontier Medical Devices And Embedded Systems Harnessing Biomedical Engineering Artificial Intelligence And Cloud Powered Big Data Analytics For Smarter Healthcare Solutions written by Sai Teja Nuka and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Technology & Engineering categories.
The intersection of biomedical engineering, artificial intelligence, and cloud-powered big data analytics marks a pivotal moment in the evolution of modern healthcare. Next-Frontier Medical Devices and Embedded Systems: Harnessing Biomedical Engineering, Artificial Intelligence, and Cloud-Powered Big Data Analytics for Smarter Healthcare Solutions is a timely exploration into how these cutting-edge technologies are converging to transform patient care, medical diagnostics, and therapeutic delivery. In an age where real-time data, personalized treatment, and intelligent automation are becoming the norm, the role of smart medical devices and embedded systems has never been more critical. These innovations are not only enhancing the precision and efficiency of clinical operations but also bringing care closer to the patient—through wearable monitors, implantable sensors, and AI-enabled diagnostic tools that function seamlessly in both hospital and home environments. This book is born out of the recognition that future-ready healthcare systems will rely heavily on adaptive, intelligent technologies that are both secure and scalable. Biomedical engineers, data scientists, clinicians, and healthcare technologists are now working in tandem to design solutions that are deeply integrated, data-driven, and focused on preventive and personalized care. The chapters herein reflect this collaboration—providing a multidisciplinary perspective on the design, deployment, and societal impact of next-generation medical systems. Whether you are a researcher, practitioner, policy leader, or student, this book offers critical insights into the challenges, breakthroughs, and ethical dimensions of embedding intelligence into healthcare hardware. From AI-driven surgical tools and diagnostic algorithms to cloud-enabled analytics and edge computing in critical care—this work offers a comprehensive guide to the technological shift redefining healthcare at its core. We hope this book serves not only as a knowledge resource but also as an inspiration to those driving innovation at the frontier of medicine and technology.