Marimuthu Palaniswami - Böcker

Healthcare sensor networks (HSNs) now offer the possibility to continuously monitor human activity and physiological signals in a mobile environment. Such sensor networks may be able to reduce the strain on the present healthcare workforce by providing new autonomous monitoring services ranging from simple user-reminder systems to more advanced monitoring agents for preventive, diagnostic, and rehabilitative purposes. Potential services include reminding people to take their medication, providing early warning for the onset of heart attacks or epileptic seizures, and monitoring a child’s physical activity in order to assess their growth and mental development. Healthcare Sensor Networks: Challenges Toward Practical Implementation discusses the fundamental concepts in designing and building such networks. It presents the latest developments in HSNs, explores applications of the technology, and provides insights into practical design and deployment challenges. Bringing together contributions from international experts in the field, the book highlights the key areas that require further research for HSNs to become a technological and commercially viable reality. The first part of the book concentrates on the engineering challenges, covering new biosensors, energy harvesting techniques, new wireless communication methods, and novel security approaches. Building from single sensing devices to networked sensing systems, the second part of the book looks at various health applications of HSNs. It addresses the human-centric requirements that should be considered in the design of HSN technologies—cost, portability, functionality, and user acceptance—and demonstrates how engineering compromises must be made in HSN solutions.A useful and timely resource for researchers, postgraduate students, and engineers looking for innovative solutions in healthcare, this book will also be of interest to medical and allied he

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-specific reference, Computational Intelligence in Biomedical Engineering provides a unique look at how techniques in CI can offer solutions in modelling, relationship pattern recognition, clustering, and other problems particular to the field.The authors begin with an overview of signal processing and machine learning approaches and continue on to introduce specific applications, which illustrate CI’s importance in medical diagnosis and healthcare. They provide an extensive review of signal processing techniques commonly employed in the analysis of biomedical signals and in the improvement of signal to noise ratio. The text covers recent CI techniques for post processing ECG signals in the diagnosis of cardiovascular disease and as well as various studies with a particular focus on CI’s potential as a tool for gait diagnostics. In addition to its detailed accounts of the most recent research, Computational Intelligence in Biomedical Engineering provides useful applications and information on the benefits of applying computation intelligence techniques to improve medical diagnostics.

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-specific reference, Computational Intelligence in Biomedical Engineering provides a unique look at how techniques in CI can offer solutions in modelling, relationship pattern recognition, clustering, and other problems particular to the field.The authors begin with an overview of signal processing and machine learning approaches and continue on to introduce specific applications, which illustrate CI’s importance in medical diagnosis and healthcare. They provide an extensive review of signal processing techniques commonly employed in the analysis of biomedical signals and in the improvement of signal to noise ratio. The text covers recent CI techniques for post processing ECG signals in the diagnosis of cardiovascular disease and as well as various studies with a particular focus on CI’s potential as a tool for gait diagnostics. In addition to its detailed accounts of the most recent research, Computational Intelligence in Biomedical Engineering provides useful applications and information on the benefits of applying computation intelligence techniques to improve medical diagnostics.

Healthcare sensor networks (HSNs) now offer the possibility to continuously monitor human activity and physiological signals in a mobile environment. Such sensor networks may be able to reduce the strain on the present healthcare workforce by providing new autonomous monitoring services ranging from simple user-reminder systems to more advanced monitoring agents for preventive, diagnostic, and rehabilitative purposes. Potential services include reminding people to take their medication, providing early warning for the onset of heart attacks or epileptic seizures, and monitoring a child’s physical activity in order to assess their growth and mental development. Healthcare Sensor Networks: Challenges Toward Practical Implementation discusses the fundamental concepts in designing and building such networks. It presents the latest developments in HSNs, explores applications of the technology, and provides insights into practical design and deployment challenges. Bringing together contributions from international experts in the field, the book highlights the key areas that require further research for HSNs to become a technological and commercially viable reality. The first part of the book concentrates on the engineering challenges, covering new biosensors, energy harvesting techniques, new wireless communication methods, and novel security approaches. Building from single sensing devices to networked sensing systems, the second part of the book looks at various health applications of HSNs. It addresses the human-centric requirements that should be considered in the design of HSN technologies—cost, portability, functionality, and user acceptance—and demonstrates how engineering compromises must be made in HSN solutions.A useful and timely resource for researchers, postgraduate students, and engineers looking for innovative solutions in healthcare, this book will also be of interest to medical and allied he

The Poincaré plot (named after Henri Poincaré) is a popular two-dimensional visualization tool for dynamic systems due to its intuitive display of the dynamic properties of a system from a time series. This book presents the basis of Poincaré plot and focus especially on traditional and new methods for analysing the geometry, temporal and spatial dynamics disclosed by the Poincaré plot to evaluate heart rate variability (HRV). Mathematical descriptors of Poincaré plot have been developed to quantify the autonomic nervous system activity (sympathetic and parasympathetic modulation of heart rate). Poincaré plot analysis has also been used in various clinical diagnostic settings like diabetes, chronic heart failure, chronic renal failure and sleep apnea syndrome. The primary aims of quantification of the Poincaré plots are to discriminate healthy physiological systems from pathological conditions and to classify the stage of a disease. The HRV analysis by Poincaré plot has opened up ample opportunities for important clinical and research applications. Therefore, the present book can be used either for self-study, as a supplement to courses in linear and nonlinear systems, or as a modern monograph by researchers in this field of HRV analysis.

The Poincaré plot (named after Henri Poincaré) is a popular two-dimensional visualization tool for dynamic systems due to its intuitive display of the dynamic properties of a system from a time series.