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Publicado por Taylor & Francis Ltd, London, 2025
ISBN 10: 1032061928 ISBN 13: 9781032061924
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Añadir al carritoPaperback. Condición: new. Paperback. This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications. Features: Explains signal processing of neuroscience applications using modern data science techniques. Provides comprehensible review on biomedical signals nature and acquisition aspects. Focusses on selected applications of neurosciences, cardiovascular and muscle-related biomedical areas. Includes computational intelligence, machine learning and biomedical signal processing and analysis. Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis. This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience and computer science. This book presents the theoretical basis and applications of biomedical signal analysis and processing. This covers the nature of the most common biomedical signals followed by theoretical basis of linear signal processing and machine learning concepts, and pertinent applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Añadir al carritoCondición: New. pages cm First edition Biomedical signal processing (Naik) Includes bibliographical references and index.
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ISBN 10: 1032061928 ISBN 13: 9781032061924
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Añadir al carritoPaperback. Condición: new. Paperback. This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications. Features: Explains signal processing of neuroscience applications using modern data science techniques. Provides comprehensible review on biomedical signals nature and acquisition aspects. Focusses on selected applications of neurosciences, cardiovascular and muscle-related biomedical areas. Includes computational intelligence, machine learning and biomedical signal processing and analysis. Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis. This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience and computer science. This book presents the theoretical basis and applications of biomedical signal analysis and processing. This covers the nature of the most common biomedical signals followed by theoretical basis of linear signal processing and machine learning concepts, and pertinent applications. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Añadir al carritoPaperback. Condición: Brand New. 294 pages. 9.18x6.12x9.21 inches. In Stock.
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ISBN 10: 1032061928 ISBN 13: 9781032061924
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Añadir al carritoPaperback. Condición: new. Paperback. This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications. Features: Explains signal processing of neuroscience applications using modern data science techniques. Provides comprehensible review on biomedical signals nature and acquisition aspects. Focusses on selected applications of neurosciences, cardiovascular and muscle-related biomedical areas. Includes computational intelligence, machine learning and biomedical signal processing and analysis. Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis. This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience and computer science. This book presents the theoretical basis and applications of biomedical signal analysis and processing. This covers the nature of the most common biomedical signals followed by theoretical basis of linear signal processing and machine learning concepts, and pertinent applications. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Publicado por TAYLOR & FRANCIS NP EXCLUSIVE(CBS), 2024
ISBN 10: 1032061928 ISBN 13: 9781032061924
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents the theoretical basis and applications of biomedical signal analysis and processing. This covers the nature of the most common biomedical signals followed by theoretical basis of linear signal processing and machine learning concepts, and pertinent applications.