Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning (Registro nro. 261060)

MARC details
000 -LIDER
fixed length control field 06130nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-031-23239-8
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240207153527.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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fixed length control field 230301s2023 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031232398
-- 978-3-031-23239-8
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TK7882.B56
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQP
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM016000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQP
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.248
Edition number 23
245 10 - TITLE STATEMENT
Title Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning
Medium [electronic resource] /
Statement of responsibility, etc. edited by Saeed Mian Qaisar, Humaira Nisar, Abdulhamit Subasi.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2023.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2023.
300 ## - PHYSICAL DESCRIPTION
Extent XVII, 373 p. 131 illus., 90 illus. in color.
Other physical details online resource.
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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500 ## - GENERAL NOTE
General note Acceso multiusuario
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 1. Introduction to non-invasive biomedical signals for healthcare -- 2. Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals -- 3. The Role of EEG as Neuro-Markers for Patients with Depression: A systematic Review -- 4. Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning -- 5. Advances in the analysis of electrocardiogram in context of mass screening: technological trends and application of artificial intelligence anomaly detection -- 6. Application of Wavelet Decomposition and Machine Learning for the sEMG Signal based Gesture Recognition -- 7. Review of EEG Signals Classification using Machine Learning and Deep-learning Techniques -- 8. "Biomedical signal processing and artificial intelligence in EOG signals" -- 9. Peak Spectrogram and Convolutional Neural Network-based Segmentation and Classification for Phonocardiogram Signals -- 10. Eczema skin lesions segmentation using deep neural network (U-net) -- 11. Biomedical signal processing for automated detection of sleep arousals Based on Multi-Physiological Signals with Ensemble learning methods -- 12. Deep Learning Assisted Biofeedback -- 13. Estimations of Emotional Synchronization Indices for Brain regions using Electroencephalogram Signal Analysis -- 14. Recognition Enhancement of Dementia Patients' Working Memory using Entropy-based Features and Local Tangent Space Alignment Algorithm.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE
Owner UABC ;
Method of acquisition Perpetuidad
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Biometric identification.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Medical informatics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Machine learning.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Biometrics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Health Informatics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Machine Learning.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Qaisar, Saeed Mian.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Nisar, Humaira.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Subasi, Abdulhamit.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031232381
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031232404
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031232411
856 40 - ELECTRONIC LOCATION AND ACCESS
Public note Libro electrónico
Uniform Resource Identifier http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-23239-8
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Libro Electrónico
Existencias
Estado de retiro Colección Ubicación permanente Ubicación actual Fecha de ingreso Total Checkouts Date last seen Número de copia Tipo de material
  Colección de Libros Electrónicos Biblioteca Electrónica Biblioteca Electrónica 07/02/2024   07/02/2024 1 Libro Electrónico

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