Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Author: Abdulhamit Subasi

Publisher: Academic Press

Published: 2019-03-16

Total Pages: 458

ISBN-13: 0128176733

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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

Practical Machine Learning for Data Analysis Using Python
Practical Machine Learning for Data Analysis Using Python

Author: Abdulhamit Subasi

Publisher: Academic Press

Published: 2020-06-05

Total Pages: 536

ISBN-13: 0128213809

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Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning
Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning

Author: Saeed Mian Qaisar

Publisher: Springer Nature

Published: 2023-03-01

Total Pages: 385

ISBN-13: 3031232399

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This book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of patients with critical health situation is required. It allows monitoring of their health status during daily life activities such as during sports, walking and sleeping. It is realizable by intelligently hybridizing the modern IoT framework, wireless biomedical implants and cloud computing. Such solutions are currently under development and in testing phases by healthcare and governmental institutions, research laboratories and biomedical companies. The biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic Obstructive Pulmonary (COP), Electrooculography (EoG), photoplethysmography (PPG), and image modalities such as positron emission tomography (PET), magnetic resonance imaging (MRI) and computerized tomography (CT) are non-invasively acquired, measured, and processed via the biomedical sensors and gadgets. These signals and images represent the activities and conditions of human cardiovascular, neural, vision and cerebral systems. Multi-channel sensing of these signals and images with an appropriate granularity is required for an effective monitoring and diagnosis. It renders a big volume of data and its analysis is not feasible manually. Therefore, automated healthcare systems are in the process of evolution. These systems are mainly based on biomedical signal and image acquisition and sensing, preconditioning, features extraction and classification stages. The contemporary biomedical signal sensing, preconditioning, features extraction and intelligent machine and deep learning-based classification algorithms are described. Each chapter starts with the importance, problem statement and motivation. A self-sufficient description is provided. Therefore, each chapter can be read independently. To the best of the editors’ knowledge, this book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning. We believe that theories, algorithms, realizations, applications, approaches, and challenges, which are presented in this book will have their impact and contribution in the design and development of modern and effective healthcare systems.

Practical Biomedical Signal Analysis Using MATLAB®
Practical Biomedical Signal Analysis Using MATLAB®

Author: Katarzyn J. Blinowska

Publisher: CRC Press

Published: 2011-09-12

Total Pages: 326

ISBN-13: 1439812020

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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.

Sub-Terahertz Sensing Technology for Biomedical Applications
Sub-Terahertz Sensing Technology for Biomedical Applications

Author: Shiban Kishen Koul

Publisher: Springer Nature

Published: 2022-08-20

Total Pages: 289

ISBN-13: 9811931402

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This book offers the readers an opportunity to acquire the concepts of artificial intelligence (AI) enabled sub-THz systems for novel applications in the biomedical field. The readers will also be inspired to contextualize these applications for solving real life problems such as non-invasive glucose monitoring systems, cancer detection and dental imaging. The introductory section of this book focuses on existing technologies for radio frequency and infrared sensing in biomedical applications, and their limited use in sensing applications, as well as the advantages of using THz technology in this context. This is followed by a detailed comparative analysis of THz electronics technology and other conventional electro optic THz setups highlighting the superior efficiency, affordability and portability of electronics-based THz systems. The book also discusses electronic sub-THz measurement systems for different biomedical applications. The chapters elucidate two major applications where sub-THz provides an edge over existing state of the art techniques used for non-invasive measurement of blood glucose levels and intraoperative assessment of tumor margins. There is a detailed articulation of an application of leveraging machine learning for measurement systems for non-invasive glucose concentration measurement. This helps the reader relate to the output in a more user-friendly format and understand the possible use cases in a more lucid manner. The book is intended to help the reader learn how to build tissue phantoms and characterize them at sub-THz frequencies in order to test the measurement systems. Towards the end of the book, a brief introduction to system automation for biomedical imaging is provided as well for quick analysis of the data. The book will empower the reader to understand and appreciate the immense possibilities of using electronic THz systems in the biomedical field, creating gateways for fueling further research in this area.​

Enhanced Telemedicine and e-Health
Enhanced Telemedicine and e-Health

Author: Gonçalo Marques

Publisher: Springer Nature

Published: 2021-05-09

Total Pages: 359

ISBN-13: 3030701115

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In recent years, new applications on computer-aided technologies for telemedicine have emerged. Therefore, it is essential to capture this growing research area concerning the requirements of telemedicine. This book presents the latest findings on soft computing, artificial intelligence, Internet of Things and related computer-aided technologies for enhanced telemedicine and e-health. Furthermore, this volume includes comprehensive reviews describing procedures and techniques, which are crucial to support researchers in the field who want to replicate these methodologies in solving their related research problems. On the other hand, the included case studies present novel approaches using computer-aided methods for enhanced telemedicine and e-health. This volume aims to support future research activities in this domain. Consequently, the content has been selected to support not only academics or engineers but also to be used by healthcare professionals.

Applications of Artificial Intelligence in Healthcare and Biomedicine
Applications of Artificial Intelligence in Healthcare and Biomedicine

Author: Abdulhamit Subasi

Publisher: Elsevier

Published: 2024-03-22

Total Pages: 550

ISBN-13: 0443223092

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??Applications of Artificial Intelligence in Healthcare and Biomedicine provides ?updated knowledge on the applications of artificial intelligence in medical image analysis. The book starts with an introduction to Artificial Intelligence techniques for Healthcare and Biomedicine. In 16 chapters it presents artificial applications in Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG), signal analysis, Computed Tomography (CT), Magnetic Resonance Imaging (MR) and Ultrasound image analysis. It equips researchers with tools for early breast cancer detection from mammograms using artificial intelligence (AI), AI models to detect lung cancer using histopathological images and a deep learning-based approach to get a proper and faster diagnosis of the Optical Coherence Tomography (OCT) images. It also presents present 3D medical image analysis using 3D Convolutional Neural Networks (CNNs). Applications of Artificial Intelligence in Healthcare and Biomedicine closes with a chapter on AI-based approach to forecast diabetes patients' hospital re-admissions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about the applications of Artificial Intelligence and its impact in medical/biomedical image analysis. Provides knowledge on Artificial Intelligence algorithms for clinical data analysis Gives insights into both AI applications in biomedical signal analysis, biomedical image analysis, and applications in healthcare, including drug discovery Equips researchers with tools for early breast cancer detection

Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction
Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction

Author: Abdulhamit Subasi

Publisher: Elsevier

Published: 2024-09-18

Total Pages: 426

ISBN-13: 0443291519

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Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modalities of communication in both Artificial Intelligence and Human-Machine Interaction. The book not only provides cross disciplinary research in the fields of multimodal signal acquisition and sensing, analysis, IoTs (Internet of Things), Artificial Intelligence, and system architectures, it also evaluates the role of Artificial Intelligence I in relation to the realization of contemporary Human Machine Interaction (HMI) systems.Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field. - Covers advances in the multimodal signal processing and artificial intelligence assistive HMIs - Presents theories, algorithms, realizations, applications, approaches, and challenges that will have their impact and contribution in the design and development of modern and effective HMI (Human Machine Interaction) system - Presents different aspects of the multimodal signals, from the sensing to analysis using hardware/software, and making use of machine/ensemble/deep learning in the intended problem-solving

Systems and Signal Processing of Capnography as a Diagnostic Tool for Asthma Assessment
Systems and Signal Processing of Capnography as a Diagnostic Tool for Asthma Assessment

Author: M. B. Malarvili

Publisher: Academic Press

Published: 2022-11-04

Total Pages: 210

ISBN-13: 0323915671

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Systems and Signal Processing of Capnography as a Diagnostic Tool for Asthma Assessment provides a comprehensive overview of groundbreaking tools and techniques for the diagnosis and monitoring of asthma. Sections cover an introduction to the human respiratory system and the pathophysiology of asthma before analyzing current assessment concepts, tools and techniques. The book describes spirometry and the peak flow meter as existing tools in assessing asthma along with their limitations. In addition, a detailed description of capnography as a new approach is included with various studies conducted on its analysis. Academicians and researchers in biomedical engineering, particularly in the course of biomedical signal processing and biomedical instrumentation will find the book useful. - Introduces a new concept of monitoring the severity of Asthma using a new index extracted from CO2 waveforms - Describes a newly designed device, including the significance of CO2 features, selection of infrared CO2 sensors, different components of the device, feature extraction, computation and transmission algorithms, data collection, and analysis and performance evaluations - Includes types of capnographs, differentiating normal from abnormal capnograms

Mechano-Electric Correlations in the Human Physiological System
Mechano-Electric Correlations in the Human Physiological System

Author: A. Bakiya

Publisher: CRC Press

Published: 2021-04-28

Total Pages: 113

ISBN-13: 1000374734

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The aim of Mechano-Electric Correlations in the Human Physiological System is to present the mechanical and electrical properties of human soft tissues and the mathematical models related to the evaluation of these properties in time, as well as their biomedical applications. This book also provides an overview of the bioelectric signals of soft tissues from various parts of the human body. In addition, this book presents the basic dielectric and viscoelastic characteristics of soft tissues, an introduction to the measurement and characteristics of bioelectric signals and their relationship with the mechanical activity, electromyography and the correlation of electromyograms with the muscle activity in normal and certain clinical conditions. The authors also present a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle. Features: Explains the basics of electrical and mechanical properties of soft tissues in time and frequency domain along with the mathematical models of soft tissue mechanics Explores the correlation of electrical properties with the mechanical properties of biological soft tissues using computational techniques Provides a detailed introduction to electrophysiological signals along with the types, applications, properties, problems and associated mathematical models Explains the electromechanics of muscles using electromyography recordings from various muscles of the human physiological system Presents a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle Mechano-Electric Correlations in the Human Physiological System is intended for biomedical engineers, researchers and medical scientists as well graduate and undergraduate students working on the mechanical properties of soft tissues.