| Vorliegende Sprache |
eng |
| ISBN |
978-0-85729-867-6 |
| Name |
Gacek, Adam |
| Pedrycz, Witold |
| ANZEIGE DER KETTE |
Pedrycz, Witold |
| T I T E L |
ECG Signal Processing, Classification and Interpretation |
| Zusatz zum Titel |
A Comprehensive Framework of Computational Intelligence |
| Verlagsort |
London |
| Verlag |
Springer-Verlag London Limited |
| Erscheinungsjahr |
2012 |
| 2012 |
| Umfang |
Online-Ressource (X, 278p. 104 illus., 61 illus. in color, digital) |
| Reihe |
SpringerLink. Bücher |
| Notiz / Fußnoten |
Includes bibliographical references and index |
| Weiterer Inhalt |
ECG Signal Processing, Classification and Interpretation; Preface; Contents; 1 An Introduction to ECG Interpretation; 1.1 Introduction; 1.2 The Electrical Conduction System of the Heart; 1.3 Electrical Axis and Orientation of the Heart in the Chest Cavity; 1.4 Waves, Segments, and Intervals; 1.5 Interpretation of the ECG; 1.6 The P-wave; 1.7 The PQ Interval; 1.8 The QRS Complex; 1.9 The ST Segment; 1.10 T-Wave; 1.11 The QT Interval; 1.12 Adjacent and Opposing Leads; 1.13 Negative or ``Inverted'' Leads; 1.14 Conclusions; References; 2 An Introduction to ECG Signal Processing and Analysis. 2.1 Introduction2.2 A Nature of ECG Signals; 2.3 The Main Properties of ECG Signals; 2.4 Processing and Analysis of ECG Signals; 2.4.1 Amplification of ECG Signals; 2.4.2 A/C Conversion; 2.4.3 Noise Suppression; 2.4.3.1 Low Frequency Noise; 2.4.3.2 Muscle Noise; 2.4.3.3 Electromagnetic Noise; 2.4.4 Data Transformation; 2.4.5 A Detection of P-QRS-T Complex - A Region of Interest; 2.4.6 Feature Extraction; 2.4.7 Classification of ECG Signals; 2.4.8 An Analysis of Rhythm Disturbances; 2.4.9 Interpretation of ECG Signals; 2.5 Categories of ECG Tests; 2.5.1 Analysis of Heart Micropotentials LVP. 2.5.2 Microvolt Variability of T Wave (T-Wave Alternans TWA)2.5.3 Analysis of Variability of Sinus Rhythm HRV; 2.6 Conclusions; References; 3 ECG Signal Analysis, Classification, and Interpretation: A Framework of Computational Intelligence; 3.1 Introduction; 3.2 Neural Networks and Neurocomputing; 3.3 Evolutionary and Biologically Inspired Computing: Toward a Holistic View at Global Optimization; 3.4 Information Granularity and Granular Computing; 3.5 Computational Intelligence: An Agenda of Synergy of Learning, Optimization, and Knowledge Representation. 3.6 Formal Platforms of Information Granularity3.6.1 Information Granules of Higher Type and Higher Order; 3.6.2 Hybrid Models of Information Granules; 3.7 Information Granularity in Signal Representation; 3.8 The Concept of Information Granulation-Degranulation; 3.9 The Principle of Justifiable Granularity; 3.10 Clustering as a Means of Design of Information Granules; 3.10.1 Unsupervised Learning with Fuzzy Sets; 3.10.2 Fuzzy C-Means as an Algorithmic Vehicle of Data Reduction Through Fuzzy Clusters; 3.10.3 Knowledge-Based Clustering; 3.11 Fuzzy Logic-Oriented Classifiers. 3.11.1 Main Categories of Fuzzy Neurons3.11.1.1 Aggregative Neurons; 3.11.2 Architectures of Logic Networks; 3.11.2.1 Logic Processor in the Processing of Fuzzy Logic Functions: A Canonical Realization; 3.12 Computational Intelligence Methods in ECG Signal Analysis and Interpretation; 3.13 Conclusions; References; 4 A Generic and Patient-Specific Electrocardiogram Signal Classification System; 4.1 Introduction; 4.2 ECG Data Processing; 4.2.1 ECG Data; 4.2.2 Feature Extraction Methodology; 4.2.3 Preprocessing by Principal Component Analysis; 4.3 MD PSO Technique for Automatic ANN Design. 4.3.1 MD PSO Algorithm |
| Titelhinweis |
Buchausg. u.d.T.ISBN: 978-0-85729-867-6 |
| ISBN |
ISBN 978-0-85729-868-3 |
| Klassifikation |
MQW |
| TEC009000 |
| 616.1207547 |
| R856-857 |
| Kurzbeschreibung |
Electrocardiogram (ECG) signals are among the most important sources of diagnostic information in healthcare so improvements in their analysis may also have telling consequences. Both the underlying signal technology and a burgeoning variety of algorithms and systems developments have proved successful targets for recent rapid advances in research. ECG Signal Processing, Classification and Interpretation shows how the various paradigms of Computational Intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. Neural networks do well at capturing the nonlinear nature of the signals, information granules realized as fuzzy sets help to confer interpretability on the data and evolutionary optimization may be critical in supporting the structural development of ECG classifiers and models of ECG signals. The contributors address concepts, methodology, algorithms, and case studies and applications exploiting the paradigm of Computational Intelligence as a conceptually appealing and practically sound technology for ECG signal processing. The text is self-contained, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts: · Part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morphology and use in diagnosis; · Part II deals with techniques and models of computational intelligence that are suitable for signal processing; and · Part III details ECG system-diagnostic interpretation and knowledge acquisition architectures. A wealth of carefully organized illustrative material is included: brief numerical experiments; detailed schemes, and more advanced problems. ECG Signal Processing, Classification and Interpretation will appeal to engineers working in the field of medical equipment and to researchers investigating biomedical signal processing, bioinformatics, Computational Intelligence and its applications, bioengineering and instrumentation. The three-part structure of the material also makes the book a useful reference source for graduate students in these disciplines |
| 2. Kurzbeschreibung |
This book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is self-contained, addressing concepts, methodology, algorithms, and case studies and applications, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts: Part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morph |
| 1. Schlagwortkette |
Biomedizinische Technik |
| Signalverarbeitung |
| Elektrokardiografie |
| Biosignal |
| Künstliche Intelligenz |
| ANZEIGE DER KETTE |
Biomedizinische Technik -- Signalverarbeitung -- Elektrokardiografie -- Biosignal -- Künstliche Intelligenz |
| SWB-Titel-Idn |
355104970 |
| Signatur |
Springer E-Book |
| Bemerkungen |
Elektronischer Volltext - Campuslizenz |
| Elektronische Adresse |
$uhttp://dx.doi.org/10.1007/978-0-85729-868-3 |
| Internetseite / Link |
Volltext |
| Siehe auch |
Volltext |
| Siehe auch |
Cover |
