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Computational Tools and Techniques for Biomedical Signal Processing

Book Description

Biomedical signal processing in the medical field has helped optimize patient care and diagnosis within medical facilities. As technology in this area continues to advance, it has become imperative to evaluate other ways these computation techniques could be implemented. Computational Tools and Techniques for Biomedical Signal Processing investigates high-performance computing techniques being utilized in hospital information systems. 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 specialists, and medical physicists interested in signal processing within medical systems and facilities.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Dedication
  6. Preface
  7. Acknowledgment
  8. Chapter 1: Nonlinear Complexity Sorting Approach for 2D ECG Data Compression
    1. ABSTRACT
    2. INTRODUCTION
    3. DATABASE AND PERFORMANCE MEASURE
    4. NEED OF 2D ECG DATA COMPRESSION
    5. BASICS OF 2D ECG DATA COMPRESSION
    6. PROPOSED NON-LINEAR COMPLEXITY SORTING APPROACH FOR 2D ECG DATA COMPRESSION
    7. RESULTS
    8. DISCUSSION
    9. CONCLUSION
    10. REFERENCES
  9. Chapter 2: A Review on Noninvasive Beat-to-Beat Systemic and Pulmonary Blood Pressure Estimation through Surrogate Cardiovascular Signals
    1. ABSTRACT
    2. INTRODUCTION
    3. BLOOD PRESSURE MONITORING
    4. PHYSIOLOGY OF THE CARDIOVASCULAR SYSTEM
    5. METHODOLOGICAL REVIEW
    6. DISCUSSION
    7. CONCLUSION AND FUTURE SCOPE
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. ADDITIONAL READING
    11. KEY TERMS AND DEFINITIONS
  10. Chapter 3: Non-Linear Analysis of Heart Rate Variability and ECG Signal Features of Swimmers from NIT-Rourkela
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS OF THE CHAPTER
    5. VOLUNTEERS AND METHODS
    6. RESULTS
    7. DISCUSSION
    8. FUTURE RESEARCH DIRECTION
    9. CONCLUSION
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  11. Chapter 4: Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. PATIENT SPECIFIC MODELLING USING THE EXTENDED KALMAN FILTER
    5. SOLUTIONS AND RECOMMENDATIONS
    6. FUTURE RESEARCH DIRECTIONS
    7. CONCLUSION
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  12. Chapter 5: Two-Directional Two-Dimensional Principal Component Analysis Based on Wavelet Decomposition for High-Dimensional Biomedical Signals Classification
    1. ABSTRACT
    2. INTRODUCTION AND BACKGROUND
    3. METHODS
    4. RESULTS
    5. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
    6. REFERENCES
  13. Chapter 6: Medical Image Enhancement Using Edge Information-Based Methods
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. DIFFERENTIAL HYPERBOLIC TANGENT (DHBT) FILTER BASED ENHANCEMENT
    5. EDGE ENHANCEMENT OF X-RAY IMAGE BY HAAR FILTERS
    6. ENHANCEMENT OF VASCULATURE IN OPTIC DISC REGION
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  14. Chapter 7: Success Dimensions of ICTs in Healthcare
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKROUND
    4. MAIN FOCUS OF THE CHAPTER: INTRODUCTION TO DIFFERENT TECHNOLOGICAL ASPECTS IN HEALTHCARE
    5. BIG DATA ANALYTICS IN HEALTHCARE
    6. ISSUES AND CHALLENGES IN IMPLEMENTING BIG DATA ANALYTICS
    7. MOBILE COMPUTING
    8. INTERNET OF THINGS (IOT) AND SENSOR TECHNOLOGIES
    9. OPPORTUNITIES, BENEFITS, ISSUES, AND CONTROVERSIES
    10. FUTURE RESEARCH DIRECTIONS
    11. CONCLUSION
    12. REFERENCES
    13. KEY TERMS AND DEFINITIONS
  15. Chapter 8: Non-Invasive Cuffless Blood Pressure Monitoring System
    1. ABSTRACT
    2. INTRODUCTION
    3. PREVIOUS METHODS
    4. TECHNIQUES FOR BLOOD PRESSURE MEASUREMENT
    5. METHODOLOGY
    6. EXPERIMENTAL RESULTS
    7. FUTURE SCOPE
    8. CONCLUSION
    9. REFERENCES
  16. Chapter 9: Electromyogram and Inertial Sensor Signal Processing in Locomotion and Transition Classification
    1. ABSTRACT
    2. INTRODUCTION
    3. LOCOMOTION AND TRANSITION CLASSIFICATION
    4. SENSORS
    5. SPECTROGRAM APPROACH FOR EMG
    6. WAVELET APPLICATION OF ACCELEROMETRY DATA
    7. EMG AND ACCELEROMETER COMPARISION
    8. DISCUSSION AND CONCLUSION
    9. LIMITATIONS AND FUTURE DIRECTIONS
    10. ACKNOWLEDGMENT
    11. REFERENCES
  17. Chapter 10: Single Electronics for Biomedical Applications
    1. ABSTRACT
    2. INTRODUCTION
    3. NANOELECTRONICS
    4. SINGLE ELECTRONICS
    5. APPLICATIONS
    6. CONCLUSION
    7. REFERENCES
  18. Chapter 11: Analysis of HRV during the Menstrual Cycle and Postmenopause
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MAIN FOCUS OF THE CHAPTER
    5. DATA DESCRIPTION AND EXPERIMENTAL PROTOCOLS
    6. RESULTS
    7. FUTURE RESEARCH DIRECTIONS
    8. CONCLUSION
    9. REFERENCES
  19. Chapter 12: A Comparative Study on Diabetic Retinopathy Detection Using Texture-Based Feature Extraction Techniques
    1. ABSTRACT
    2. INTRODUCTION
    3. TEXTURE BASED FEATURE EXTRACTION
    4. EXPERIMENTS
    5. DISCUSSION
    6. CONCLUSION
    7. REFERENCES
  20. Chapter 13: Analysis of Electrocardiogram Data Compression Techniques
    1. ABSTRACT
    2. INTRODUCTION
    3. DATABASE AND PERFORMANCE MEASURE
    4. THREE METHODS FOR ECG DATA COMPRESSION
    5. CONCLUSION
    6. REFERENCES
  21. Chapter 14: Energy Efficient Particle Optimized Compressed ECG Data over Zigbee Environment
    1. ABSTRACT
    2. INTRODUCTION
    3. PREVIOUS METHODS
    4. CHAPTER’S HIGHLIGHTS
    5. NEED AND SIGNIFICANCE OF THIS CHAPTER
    6. EXPERIMENTAL DESIGN
    7. SIMULATION RESULTS
    8. FUTURE RESEARCH DIRECTIONS
    9. CONCLUSION
    10. REFERENCES
  22. Chapter 15: Optimized Clustering Techniques with Special Focus to Biomedical Datasets
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. OVERVIEW OF OPTIMIZATION
    5. OPTIMIZATION TECHNIQUES USING SWARM INTELLIGENCE (SI)
    6. K MEANS OPTIMIZED USING PSO AND QPSO METHODS
    7. FCM AND ENTROPY BASED CLUSTERING OPTIMIZED USING PSO AND QPSO
    8. FCM-PSO
    9. FCM-QPSO
    10. ENTROPY BASED CLUSTERING
    11. EXPERIMENTAL RESULTS
    12. CONCLUSION AND FUTURE RESEARCH
    13. REFERENCES
    14. ADDITIONAL READING
    15. KEY TERMS AND DEFINITIONS
  23. Compilation of References
  24. About the Contributors