Biomedical Signal Processing and Artificial Intelligence in Healthcare

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.

Produk Detail:

  • Author : Walid A. Zgallai
  • Publisher : Academic Press
  • Pages : 268 pages
  • ISBN : 0128189479
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKBiomedical Signal Processing and Artificial Intelligence in Healthcare

Biomedical Signal Processing and Artificial Intelligence in Healthcare

Biomedical Signal Processing and Artificial Intelligence in Healthcare
  • Author : Walid A. Zgallai
  • Publisher : Academic Press
  • Release : 29 July 2020
GET THIS BOOKBiomedical Signal Processing and Artificial Intelligence in Healthcare

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

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
  • Author : Ervin Sejdic,Tiago H. Falk
  • Publisher : CRC Press
  • Release : 04 July 2018
GET THIS BOOKSignal Processing and Machine Learning for Biomedical Big Data

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

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
  • Publisher : Academic Press
  • Release : 30 November 2018
GET THIS BOOKMachine Learning in Bio-Signal Analysis and Diagnostic Imaging

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

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
  • Release : 16 March 2019
GET THIS BOOKPractical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

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,

Machine Learning and the Internet of Medical Things in Healthcare

Machine Learning and the Internet of Medical Things in Healthcare
  • Author : Krishna Kant Singh,Mohamed Elhoseny,Akansha Singh,Ahmed A. Elngar
  • Publisher : Academic Press
  • Release : 12 April 2021
GET THIS BOOKMachine Learning and the Internet of Medical Things in Healthcare

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and

Handbook of Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering
  • Author : Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G. S. Mahalakshmi
  • Publisher : Apple Academic Press
  • Release : 15 December 2020
GET THIS BOOKHandbook of Artificial Intelligence in Biomedical Engineering

"Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert's knowledge in order to predict and monitor the

Signal Processing in Medicine and Biology

Signal Processing in Medicine and Biology
  • Author : Iyad Obeid,Ivan Selesnick,Joseph Picone
  • Publisher : Springer Nature
  • Release : 16 March 2020
GET THIS BOOKSignal Processing in Medicine and Biology

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

Computational Intelligence in Biomedical Engineering

Computational Intelligence in Biomedical Engineering
  • Author : Rezaul Begg,Daniel T.H. Lai,Marimuthu Palaniswami
  • Publisher : CRC Press
  • Release : 04 December 2007
GET THIS BOOKComputational Intelligence in Biomedical Engineering

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-specific reference, Computational Intelligence in Biomedical Engineering provides a unique look at how techniques in CI can offer solutions in modelling, relationship pattern recognition, clustering, and other problems particular to the field. The

Classification and Clustering in Biomedical Signal Processing

Classification and Clustering in Biomedical Signal Processing
  • Author : Dey, Nilanjan
  • Publisher : IGI Global
  • Release : 07 April 2016
GET THIS BOOKClassification and Clustering in Biomedical Signal Processing

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

Computational Intelligence and Its Applications in Healthcare

Computational Intelligence and Its Applications in Healthcare
  • Author : Prashant Johri,Jitendra Kumar Verma,Sudip Paul
  • Publisher : Academic Press
  • Release : 01 August 2020
GET THIS BOOKComputational Intelligence and Its Applications in Healthcare

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual

Biomedical Signal Processing

Biomedical Signal Processing
  • Author : Ganesh Naik
  • Publisher : Springer Nature
  • Release : 12 November 2019
GET THIS BOOKBiomedical Signal Processing

This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to

Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics
  • Author : Dr. Basant Agarwal,Valentina E. Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma
  • Publisher : Academic Press
  • Release : 14 January 2020
GET THIS BOOKDeep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they

Medical Imaging

Medical Imaging
  • Author : K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
  • Publisher : CRC Press
  • Release : 20 August 2019
GET THIS BOOKMedical Imaging

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

Machine Intelligence and Signal Analysis

Machine Intelligence and Signal Analysis
  • Author : M. Tanveer,Ram Bilas Pachori
  • Publisher : Springer
  • Release : 07 August 2018
GET THIS BOOKMachine Intelligence and Signal Analysis

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