دانلود کتاب پردازش سیگنال زیست پزشکی

The recent advances in modern signal processing techniques in medicine have improved the accuracy and reliability of medical diagnoses. Today, biomedical signal analysis is becoming one of the most important visualizations and interpretation methods in biology and medicine. The goal of the present book is to present a complete range of proven and new methods that play a leading role in the improvement of biomedical signal analysis and interpretation. The book provides a forum for researchers to exchange their ideas and to foster a better understanding of the state of the art of the subject. This book is intended for biomedical, computer science, and electronics engineers (researchers and graduate students) who wish to get novel research ideas and some training in novel biomedical research areas, especially on ECG, EEG, and EMG signal applications. Additionally, the research results previously scattered in many scientific articles worldwide are collected methodically and presented in the book in a unified form. The book is organized into four parts. The first part is devoted to recent developments in theories, algorithms, and extensions of EMG signal processing and human locomotion applications. In this part, we have collected four chapters with several novel contributions. The set of chapters include an insight on EMG signal processing, classification, and practical considerations the by Angkoon, Evan and Eric; estimation of ankle joint torque and angle based on EMG for assistive rehabilitation robots by Palayil Baby et al.; force myography and its application to human locomotion by Anoop et al.; and an application of EMG for stroke rehabilitation applications by Naik et al. The second part focuses on the various applications of EEG and its links to other relevant areas, such as BCI and epileptic seizure identification system. We have gathered five chapters in this part, and they are, respectively, EEG-based BCI to classify motor imagery signals by Andrade et al., artificial neural networks on multi-channel EEG data to detect the effect of binaural stimuli in resting state by Júnior et al., automated detection of seizure and nonseizure EEG using two band biorthogonal wavelet filter banks by Bhati et al., automated identification of epileptic seizures from EEG using FBSE-EWT method by Gupta et al., and DWT-based epilepsy seizures by Sharmila and Geethanjali. The third part covers various cardiac and ECG signal processing applications, namely, unipolar cardiac leads ECG analysis by Hussein et al., cardiac arrhythmias classification based on long-duration ECG signal fragments analysis by Plawiak and Abdar, artificial intelligence-enabled ECG big data mining for pervasive heart health monitoring by Zhang, tensor-based approaches in cardiac applications by Padhy et al., syntactic methods for ECG diagnosis and QRS complexes recognition by Hamdi et al., and extraction of ECG significant features for remote CVD monitoring by Naresh and Acharyya. The final part covers a chapter on accelerated computational approach in proteomics by Bhardwaj, Gudur, and Acharyya. I want to thank the authors for their excellent submissions (chapters) to this book and their significant contributions to the review process, which have helped to ensure the high quality of this publication. Without their contributions, it would not have been possible for the book to come successfully into existence.