This cutting-edge resource provides you with a practical and theoretical understanding of state-of-the-art techniques for electrocardiogram (ECG) data analysis. Placing an emphasis on the fundamentals of signal etiology, acquisition, data selection, and testing, this comprehensive volume presents guidelines to help you design, implement, and evaluate algorithms used for the analysis of ECG and related data. Additionally, explanations of open source software and related databases for signal processing are given. The book focuses on the modeling, classification, and interpretation of features derived from advanced signal processing and artificial intelligence techniques. Key topics covered include physiological origin, hardware acquisition and filtering, time-frequency quantification of the ECG and derived signals (including heart rate variability and respiration), analysis of noise and artifact, models for ECG and RR interval processes, linear and nonlinear filtering techniques, and adaptive algorithms such as neural networks. Much of the book is devoted to deriving robust, clinically meaningful parameters such as the QRS axis, QT-interval, the ST-level, and T-wave alternan metrics. Methods for applying these metrics to clinical classification are also discussed, together with supervised and unsupervised classification techniques. Including over 190 illustrations, the book offers you a solid grounding in the relevant basics of physiology, data acquisition and database design, and addresses the practical issues of improving existing data analysis methods and developing new applications.
Preface.; Introduction -Introduction to Physiological Basis and Clinical Interpretation of ECG.Introduction to ECG Information Acquisition, Representation and Storage. Advanced Signal Processing and Artificial Intelligence for ECG Data Analysis.; Mathematical Characterization of the ECG and Its Contaminants - Noise/Signal/Artifact Comparision, Characterizing òUnwanted ' Signals Through Measures of Stationarity, Gaussianity, Nonlinearity, and Color. Long Term Trends (Circadian Rhythms, Segmentation, Nonstationary Shifts).; Filtering, Compression, Decompression, and Interpolation - Linearity and Stationary Filtering and Compression, Resampling, Interpolation, and Wavelets. Multidimensional Filtering. Nonlinear Projective Filtering.; Feature Extraction - Feature Extraction from ECG. Temporal Feature Extraction ECG-Derived Respiration and Heart Rate Variability.; Supervised and Unsupervised Classification ëIntroduction to Linear Supervised Learning. Supervised Neural Networks. Support Vector Machine Methods. Clustering-Based Analysis Methods. Unsupervised Learning Methods for Supporting Pattern Discovery and Interpretation. Hybrid Intelligent Classification Techniques.; Visualization Methods, Knowledge Management and Emerging Methods - Methods for Displaying ECG Information and Analysis Outcomes. Methods for Automatically Describing and Evaluating ECG Data Clusters and Classes. Introduction to Causal Reasoning.;
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Francisco Azuaje
Francisco Azuaje is a reader at the University of Ulster and was formerly a lecturer at Trinity College Dublin, Ireland. A Senior Member of the IEEE, he has several editorial board memberships in journals relevant to biomedical informatics and bioinformatics. Dr. Azuaje has co-edited other two books relevant to the areas of bioinformatics and systems biology. He received his B.Sc. in electronic engineering from Simon Bolivar University, Venezuela and his Ph.D. in artificial intelligence and medical informatics from the University of Ulster, U.K.
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Gari D. Clifford
Gari D. Clifford is a Research Scientist in the Harvard-MIT Division of Health Sciences where he is the engineering manager of a R01 NIH-funded research program, and a major contributor to the well-known Physionet Research Resource. He has taught at Oxford, MIT and Harvard and is currently an Instructor in Biomedical Engineering at MIT. Dr. Clifford, a senior member of the IEEE, has worked in industry on the design and production of several Ca- and FDA-approved medical devices, authored and coauthored over 40 publications in the field of biomedical engineering, and is on the editorial boards of Biomedical Engineering Online and the Journal of Biological Systems. Dr Clifford holds a PhD in Neural Networks and Biomedical Engineering from Oxford University and a Masters in Theoretical Physics from Southampton University.
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Patrick E. McSharry
Patrick E. McSharry is a Royal Academy of Engineering/EPSRC research fellow at the University of Oxford, a research associate at St Catherine's College, Oxford and a Senior Member of the IEEE. He received a B.A. in theoretical physics and an M.Sc. in electronic and electrical engineering from Trinity College, Dublin, and received a Ph.D. in mathematics, on time series analysis and forecasting, from the University of Oxford. He is currently supported by a Marie Curie Research Fellowship and leads the Systems Analysis, Modeling and Prediction (SAMP) Group at the University of Oxford.