This book examines the use of biomedical signal processing—EEG, EMG, and ECG—in analyzing and diagnosing various medical conditions, particularly diseases related to the heart and brain. In combination with machine learning tools and other optimization methods, the analysis of biomedical signals greatly benefits the healthcare sector by improving patient outcomes through early, reliable detection. The discussion of these modalities promotes better understanding, analysis, and application of biomedical signal processing for specific diseases. The major highlights of Biomedical Signal Processing for Healthcare Applications include biomedical signals, acquisition of signals, pre-processing and analysis, post-processing and classification of the signals, and application of analysis and classification for the diagnosis of brain- and heart-related diseases. Emphasis is given to brain and heart signals because incomplete interpretations are made by physicians of these aspects in several situations, and these partial interpretations lead to major complications. FEATURES Examines modeling and acquisition of biomedical signals of different disorders Discusses CAD-based analysis of diagnosis useful for healthcare Includes all important modalities of biomedical signals, such as EEG, EMG, MEG, ECG, and PCG Includes case studies and research directions, including novel approaches used in advanced healthcare systems This book can be used by a wide range of users, including students, research scholars, faculty, and practitioners in the field of biomedical engineering and medical image analysis and diagnosis.
A brief introduction to EEG signal has been covered in Chapter 1, Section 1.2. The intention here, however, is to show detailed and practical applications for the reader to get an insight on how to process EEG signals using deep ...
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For example, EEG being more complex than ECG would need the aid of a complex machine learning tool such as SVM or Deep Learning algorithm, as opposed to using simple linear classifiers. • Labeling of signal data • Implementation ...
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 ...
Signal and Image Processing has played vital role in the development of intelligent healthcare systems and devices such as Image reconstruction and modeling techniques allow instant processing of 2D signals to create 3D images in case ...
Featuring comprehensive coverage on various theoretical perspectives, best practices, and emergent research in the field, this book is ideally suited for computer scientists, information technologists, biomedical engineers, data-processing ...
This encourages medical researchers to investigate innovative techniques and encourages physicians and medical investigators ... biological systems also concur that nanobiotechnology must constantly improve as it has great advantages, ...
This book provides an overview of the field of Networked e-health applications and telemedicine and its supporting technologies.
Figure 2. General methodology of machine-learning based arrhythmia detection technique Recently, neural networks have ... A type-2 fuzzy clustering neural network was employed by Ceylan et. al (Ceylan et al., 2009) to classify ten types ...
Covering the latest cutting-edge techniques in biomedical signal processing while presenting a coherent treatment of various signal processing methods and applications, this second edition of Practical Biomedical Signal Analysis Using ...