Machine Learning in Bio Signal Analysis and Diagnostic Imaging

Machine Learning in Bio Signal Analysis and Diagnostic Imaging
Author: Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
Publsiher: Academic Press
Total Pages: 345
Release: 2018-11-30
ISBN 10: 012816087X
ISBN 13: 9780128160879
Language: EN, FR, DE, ES & NL

Machine Learning in Bio Signal Analysis and Diagnostic Imaging Book Review:

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
Author: Ervin Sejdic,Tiago H. Falk
Publsiher: CRC Press
Total Pages: 606
Release: 2018-07-04
ISBN 10: 1351061216
ISBN 13: 9781351061216
Language: EN, FR, DE, ES & NL

Signal Processing and Machine Learning for Biomedical Big Data Book Review:

This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals. It will give signal processing researchers a glimpse into the issues faced with Big Medical Data.

Biomedical Signal Processing and Artificial Intelligence in Healthcare

Biomedical Signal Processing and Artificial Intelligence in Healthcare
Author: Walid A. Zgallai
Publsiher: Academic Press
Total Pages: 268
Release: 2020-07-29
ISBN 10: 0128189479
ISBN 13: 9780128189474
Language: EN, FR, DE, ES & NL

Biomedical Signal Processing and Artificial Intelligence in Healthcare Book Review:

Biomedical Signal Processing and Artificial Intelligence in Healthcare is a new volume in the Developments in Biomedical Engineering and Bioelectronics series. This volume covers the basics of biomedical signal processing and artificial intelligence. It explains the role of machine learning in relation to processing biomedical signals and the applications in medicine and healthcare. The book provides background to statistical analysis in biomedical systems. Several types of biomedical signals are introduced and analyzed, including ECG and EEG signals. The role of Deep Learning, Neural Networks, and the implications of the expansion of artificial intelligence is covered. Biomedical Images are also introduced and processed, including segmentation, classification, and detection. This book covers different aspects of signals, from the use of hardware and software, and making use of artificial intelligence in problem solving. Dr Zgallai’s book has up to date coverage where readers can find the latest information, easily explained, with clear examples and illustrations. The book includes examples on the application of signal and image processing employing artificial intelligence to Alzheimer, Parkinson, ADHD, autism, and sleep disorders, as well as ECG and EEG signals. Developments in Biomedical Engineering and Bioelectronics is a 10-volume series which covers recent developments, trends and advances in this field. Edited by leading academics in the field, and taking a multidisciplinary approach, this series is a forum for cutting-edge, contemporary review articles and contributions from key ‘up-and-coming’ academics across the full subject area. The series serves a wide audience of university faculty, researchers and students, as well as industry practitioners. Coverage of the subject area and the latest advances and applications in biomedical signal processing and Artificial Intelligence. Contributions by recognized researchers and field leaders. On-line presentations, tutorials, application and algorithm examples.

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
Author: Abdulhamit Subasi
Publsiher: Academic Press
Total Pages: 456
Release: 2019-03-16
ISBN 10: 0128176733
ISBN 13: 9780128176733
Language: EN, FR, DE, ES & NL

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques Book Review:

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

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
Author: Guorong Wu,Dinggang Shen,Mert Sabuncu
Publsiher: Academic Press
Total Pages: 512
Release: 2016-08-11
ISBN 10: 0128041145
ISBN 13: 9780128041147
Language: EN, FR, DE, ES & NL

Machine Learning and Medical Imaging Book Review:

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Signal Processing in Medicine and Biology

Signal Processing in Medicine and Biology
Author: Iyad Obeid,Ivan Selesnick,Joseph Picone
Publsiher: Springer Nature
Total Pages: 281
Release: 2020-03-16
ISBN 10: 3030368440
ISBN 13: 9783030368449
Language: EN, FR, DE, ES & NL

Signal Processing in Medicine and Biology Book Review:

This book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson’s; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology.

Medical Imaging

Medical Imaging
Author: K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
Publsiher: CRC Press
Total Pages: 238
Release: 2019-08-20
ISBN 10: 0429639325
ISBN 13: 9780429639326
Language: EN, FR, DE, ES & NL

Medical Imaging Book Review:

The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Classification and Clustering in Biomedical Signal Processing

Classification and Clustering in Biomedical Signal Processing
Author: Dey, Nilanjan
Publsiher: IGI Global
Total Pages: 463
Release: 2016-04-07
ISBN 10: 152250141X
ISBN 13: 9781522501411
Language: EN, FR, DE, ES & NL

Classification and Clustering in Biomedical Signal Processing Book Review:

Advanced techniques in image processing have led to many innovations supporting the medical field, especially in the area of disease diagnosis. Biomedical imaging is an essential part of early disease detection and often considered a first step in the proper management of medical pathological conditions. Classification and Clustering in Biomedical Signal Processing focuses on existing and proposed methods for medical imaging, signal processing, and analysis for the purposes of diagnosing and monitoring patient conditions. Featuring the most recent empirical research findings in the areas of signal processing for biomedical applications with an emphasis on classification and clustering techniques, this essential publication is designed for use by medical professionals, IT developers, and advanced-level graduate students.

Deep Learning for Data Analytics

Deep Learning for Data Analytics
Author: Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
Publsiher: Academic Press
Total Pages: 218
Release: 2020-05-29
ISBN 10: 0128226080
ISBN 13: 9780128226087
Language: EN, FR, DE, ES & NL

Deep Learning for Data Analytics Book Review:

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning

Pattern Recognition and Signal Analysis in Medical Imaging

Pattern Recognition and Signal Analysis in Medical Imaging
Author: Anke Meyer-Baese,Volker J. Schmid
Publsiher: Elsevier
Total Pages: 466
Release: 2014-03-21
ISBN 10: 0124166156
ISBN 13: 9780124166158
Language: EN, FR, DE, ES & NL

Pattern Recognition and Signal Analysis in Medical Imaging Book Review:

Medical imaging is one of the heaviest funded biomedical engineering research areas. The second edition of Pattern Recognition and Signal Analysis in Medical Imaging brings sharp focus to the development of integrated systems for use in the clinical sector, enabling both imaging and the automatic assessment of the resultant data. Since the first edition, there has been tremendous development of new, powerful technologies for detecting, storing, transmitting, analyzing, and displaying medical images. Computer-aided analytical techniques, coupled with a continuing need to derive more information from medical images, has led to a growing application of digital processing techniques in cancer detection as well as elsewhere in medicine. This book is an essential tool for students and professionals, compiling and explaining proven and cutting-edge methods in pattern recognition for medical imaging. New edition has been expanded to cover signal analysis, which was only superficially covered in the first edition New chapters cover Cluster Validity Techniques, Computer-Aided Diagnosis Systems in Breast MRI, Spatio-Temporal Models in Functional, Contrast-Enhanced and Perfusion Cardiovascular MRI Gives readers an unparalleled insight into the latest pattern recognition and signal analysis technologies, modeling, and applications

Machine Intelligence and Signal Analysis

Machine Intelligence and Signal Analysis
Author: M. Tanveer,Ram Bilas Pachori
Publsiher: Springer
Total Pages: 767
Release: 2018-08-07
ISBN 10: 981130923X
ISBN 13: 9789811309236
Language: EN, FR, DE, ES & NL

Machine Intelligence and Signal Analysis Book Review:

The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.

Information Technology and Intelligent Transportation Systems

Information Technology and Intelligent Transportation Systems
Author: L.C. Jain,X. Zhao,V.E. Balas
Publsiher: IOS Press
Total Pages: 224
Release: 2020-03-18
ISBN 10: 1643680617
ISBN 13: 9781643680613
Language: EN, FR, DE, ES & NL

Information Technology and Intelligent Transportation Systems Book Review:

Intelligent transport systems, from basic management systems to more application-oriented systems, vary in the technologies they apply. Information technologies, including wireless communication, are important in intelligent transportation systems, as are computational technologies: floating car data/floating cellular data, sensing technologies, and video vehicle detection. Theoretical and application technologies, such as emergency vehicle notification systems, automatic road enforcement and collision avoidance systems, as well as some cooperative systems are also used in intelligent transportation systems. This book presents papers selected from the 128 submissions in the field of information technology and intelligent transportation systems received from 5 countries. In December 2019 Chang’an University organized a round-table meeting to discuss and score the technical merits of each selected paper, of which 23 are included in this book. Providing a current overview of the subject, the book will be of interest to all those working in the field of intelligent transportation systems and traffic management.

Sensors for Health Monitoring

Sensors for Health Monitoring
Author: Nilanjan Dey,Jyotismita Chaki,Rajesh Kumar
Publsiher: Academic Press
Total Pages: 322
Release: 2019-09-09
ISBN 10: 012819362X
ISBN 13: 9780128193624
Language: EN, FR, DE, ES & NL

Sensors for Health Monitoring Book Review:

Sensors for Health Monitoring discusses the characteristics of U-Healthcare systems in different domains, providing a foundation for working professionals and undergraduate and postgraduate students. The book provides information and advice on how to choose the best sensors for a U-Healthcare system, advises and guides readers on how to overcome challenges relating to data acquisition and signal processing, and presents comprehensive coverage of up-to-date requirements in hardware, communication and calculation for next-generation uHealth systems. It then compares new technological and technical trends and discusses how they address expected u-Health requirements. In addition, detailed information on system operations is presented and challenges in ubiquitous computing are highlighted. The book not only helps beginners with a holistic approach toward understanding u-Health systems, but also presents researchers with the technological trends and design challenges they may face when designing such systems. Presents an outstanding update on the use of U-Health data analysis and management tools in different applications, highlighting sensor systems Highlights Internet of Things enabled U-Healthcare Covers different data transmission techniques, applications and challenges with extensive case studies for U-Healthcare systems

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis
Author: Nilanjan Dey
Publsiher: Academic Press
Total Pages: 218
Release: 2019-07-31
ISBN 10: 0128180056
ISBN 13: 9780128180051
Language: EN, FR, DE, ES & NL

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis Book Review:

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images. Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications Introduces several techniques for medical image processing and analysis for CAD systems design

Practical Biomedical Signal Analysis Using MATLAB

Practical Biomedical Signal Analysis Using MATLAB
Author: Katarzyn J. Blinowska,Jaroslaw Zygierewicz
Publsiher: CRC Press
Total Pages: 324
Release: 2011-09-12
ISBN 10: 1439812020
ISBN 13: 9781439812020
Language: EN, FR, DE, ES & NL

Practical Biomedical Signal Analysis Using MATLAB Book Review:

Practical Biomedical Signal Analysis Using MATLAB® presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data. The first several chapters of the text describe signal analysis techniques—including the newest and most advanced methods—in an easy and accessible way. MATLAB routines are listed when available and freely available software is discussed where appropriate. The final chapter explores the application of the methods to a broad range of biomedical signals, highlighting problems encountered in practice. A unified overview of the field, this book explains how to properly use signal processing techniques for biomedical applications and avoid misinterpretations and pitfalls. It helps readers to choose the appropriate method as well as design their own methods.

Soft Computing Based Medical Image Analysis

Soft Computing Based Medical Image Analysis
Author: Nilanjan Dey,Amira Ashour,Fuquian Shi,Valentina E. Balas
Publsiher: Academic Press
Total Pages: 292
Release: 2018-01-18
ISBN 10: 0128131748
ISBN 13: 9780128131749
Language: EN, FR, DE, ES & NL

Soft Computing Based Medical Image Analysis Book Review:

Soft Computing Based Medical Image Analysis presents the foremost techniques of soft computing in medical image analysis and processing. It includes image enhancement, segmentation, classification-based soft computing, and their application in diagnostic imaging, as well as an extensive background for the development of intelligent systems based on soft computing used in medical image analysis and processing. The book introduces the theory and concepts of digital image analysis and processing based on soft computing with real-world medical imaging applications. Comparative studies for soft computing based medical imaging techniques and traditional approaches in medicine are addressed, providing flexible and sophisticated application-oriented solutions. Covers numerous soft computing approaches, including fuzzy logic, neural networks, evolutionary computing, rough sets and Swarm intelligence Presents transverse research in soft computing formation from various engineering and industrial sectors in the medical domain Highlights challenges and the future scope for soft computing based medical analysis and processing techniques

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
Author: Exarchos, Themis P.,Papadopoulos, Athanasios,Fotiadis, Dimitrios I.
Publsiher: IGI Global
Total Pages: 598
Release: 2009-04-30
ISBN 10: 1605663158
ISBN 13: 9781605663159
Language: EN, FR, DE, ES & NL

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications Book Review:

"This book includes state-of-the-art methodologies that introduce biomedical imaging in decision support systems and their applications in clinical practice"--Provided by publisher.

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
Author: S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publsiher: Academic Press
Total Pages: 458
Release: 2017-01-18
ISBN 10: 0128104090
ISBN 13: 9780128104095
Language: EN, FR, DE, ES & NL

Deep Learning for Medical Image Analysis Book Review:

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Deep Learning and Data Labeling for Medical Applications

Deep Learning and Data Labeling for Medical Applications
Author: Gustavo Carneiro,Diana Mateus,Loïc Peter,Andrew Bradley,João Manuel R. S. Tavares,Vasileios Belagiannis,João Paulo Papa,Jacinto C. Nascimento,Marco Loog,Zhi Lu,Jaime S. Cardoso,Julien Cornebise
Publsiher: Springer
Total Pages: 280
Release: 2016-10-07
ISBN 10: 3319469762
ISBN 13: 9783319469768
Language: EN, FR, DE, ES & NL

Deep Learning and Data Labeling for Medical Applications Book Review:

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

Biosignal Processing

Biosignal Processing
Author: Hualou Liang,Joseph D. Bronzino,Donald R. Peterson
Publsiher: CRC Press
Total Pages: 202
Release: 2012-10-17
ISBN 10: 1439871442
ISBN 13: 9781439871447
Language: EN, FR, DE, ES & NL

Biosignal Processing Book Review:

With the rise of advanced computerized data collection systems, monitoring devices, and instrumentation technologies, large and complex datasets accrue as an inevitable part of biomedical enterprise. The availability of these massive amounts of data offers unprecedented opportunities to advance our understanding of underlying biological and physiol