Data Mining in Biomedical Imaging, Signaling, and Systems

Data Mining in Biomedical Imaging, Signaling, and Systems

by Sumeet Dua, Rajendra Acharya U
Released April 2016
Publisher(s): Auerbach Publications
ISBN: 9781439839393

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Book description

Data mining can help pinpoint hidden information in medical data and accurately differentiate pathological from normal data. It can help to extract hidden features from patient groups and disease states and can aid in automated decision making. Data Mining in Biomedical Imaging, Signaling, and Systems provides an in-depth examination of the biomedi

Table of contents

  1. Front Cover
  2. Contents
  3. Preface (1/2)
  4. Preface (2/2)
  5. Editors
  6. Contributors
  7. Chapter 1. Feature Extraction Methods in Biomedical Signaling and Imaging (1/5)
  8. Chapter 1. Feature Extraction Methods in Biomedical Signaling and Imaging (2/5)
  9. Chapter 1. Feature Extraction Methods in Biomedical Signaling and Imaging (3/5)
  10. Chapter 1. Feature Extraction Methods in Biomedical Signaling and Imaging (4/5)
  11. Chapter 1. Feature Extraction Methods in Biomedical Signaling and Imaging (5/5)
  12. Chapter 2. Supervised and Unsupervised Learning Methods in Biomedical Signaling and Imaging (1/5)
  13. Chapter 2. Supervised and Unsupervised Learning Methods in Biomedical Signaling and Imaging (2/5)
  14. Chapter 2. Supervised and Unsupervised Learning Methods in Biomedical Signaling and Imaging (3/5)
  15. Chapter 2. Supervised and Unsupervised Learning Methods in Biomedical Signaling and Imaging (4/5)
  16. Chapter 2. Supervised and Unsupervised Learning Methods in Biomedical Signaling and Imaging (5/5)
  17. Chapter 3. Data Mining of Acoustical Properties of Speech as Indicators of Depression (1/5)
  18. Chapter 3. Data Mining of Acoustical Properties of Speech as Indicators of Depression (2/5)
  19. Chapter 3. Data Mining of Acoustical Properties of Speech as Indicators of Depression (3/5)
  20. Chapter 3. Data Mining of Acoustical Properties of Speech as Indicators of Depression (4/5)
  21. Chapter 3. Data Mining of Acoustical Properties of Speech as Indicators of Depression (5/5)
  22. Chapter 4. Typicality Measure and the Creation of Predictive Models in Biomedicine (1/7)
  23. Chapter 4. Typicality Measure and the Creation of Predictive Models in Biomedicine (2/7)
  24. Chapter 4. Typicality Measure and the Creation of Predictive Models in Biomedicine (3/7)
  25. Chapter 4. Typicality Measure and the Creation of Predictive Models in Biomedicine (4/7)
  26. Chapter 4. Typicality Measure and the Creation of Predictive Models in Biomedicine (5/7)
  27. Chapter 4. Typicality Measure and the Creation of Predictive Models in Biomedicine (6/7)
  28. Chapter 4. Typicality Measure and the Creation of Predictive Models in Biomedicine (7/7)
  29. Chapter 5. Gaussian Mixture Model–Based Clustering Technique for Electrocardiogram Analysis (1/4)
  30. Chapter 5. Gaussian Mixture Model–Based Clustering Technique for Electrocardiogram Analysis (2/4)
  31. Chapter 5. Gaussian Mixture Model–Based Clustering Technique for Electrocardiogram Analysis (3/4)
  32. Chapter 5. Gaussian Mixture Model–Based Clustering Technique for Electrocardiogram Analysis (4/4)
  33. Chapter 6. Pattern Recognition Algorithms for Seizure Applications (1/6)
  34. Chapter 6. Pattern Recognition Algorithms for Seizure Applications (2/6)
  35. Chapter 6. Pattern Recognition Algorithms for Seizure Applications (3/6)
  36. Chapter 6. Pattern Recognition Algorithms for Seizure Applications (4/6)
  37. Chapter 6. Pattern Recognition Algorithms for Seizure Applications (5/6)
  38. Chapter 6. Pattern Recognition Algorithms for Seizure Applications (6/6)
  39. Chapter 7. Application of Parametric and Nonparametric Methods in Arrhythmia Classification (1/6)
  40. Chapter 7. Application of Parametric and Nonparametric Methods in Arrhythmia Classification (2/6)
  41. Chapter 7. Application of Parametric and Nonparametric Methods in Arrhythmia Classification (3/6)
  42. Chapter 7. Application of Parametric and Nonparametric Methods in Arrhythmia Classification (4/6)
  43. Chapter 7. Application of Parametric and Nonparametric Methods in Arrhythmia Classification (5/6)
  44. Chapter 7. Application of Parametric and Nonparametric Methods in Arrhythmia Classification (6/6)
  45. Chapter 8. Supervised and Unsupervised Metabonomic Techniques in Clinical Diagnosis: Classification of 677-MTHFR Mutations in Migraine Sufferers (1/4)
  46. Chapter 8. Supervised and Unsupervised Metabonomic Techniques in Clinical Diagnosis: Classification of 677-MTHFR Mutations in Migraine Sufferers (2/4)
  47. Chapter 8. Supervised and Unsupervised Metabonomic Techniques in Clinical Diagnosis: Classification of 677-MTHFR Mutations in Migraine Sufferers (3/4)
  48. Chapter 8. Supervised and Unsupervised Metabonomic Techniques in Clinical Diagnosis: Classification of 677-MTHFR Mutations in Migraine Sufferers (4/4)
  49. Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier (1/8)
  50. Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier (2/8)
  51. Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier (3/8)
  52. Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier (4/8)
  53. Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier (5/8)
  54. Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier (6/8)
  55. Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier (7/8)
  56. Chapter 9. Automatic Grading of Adult Depression Using a Backpropagation Neural Network Classifier (8/8)
  57. Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data (1/8)
  58. Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data (2/8)
  59. Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data (3/8)
  60. Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data (4/8)
  61. Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data (5/8)
  62. Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data (6/8)
  63. Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data (7/8)
  64. Chapter 10. Alignment-Based Clustering of Gene Expression Time-Series Data (8/8)
  65. Chapter 11. Mining of Imaging Biomarkers for Quantitative Evaluation of Osteoarthritis (1/5)
  66. Chapter 11. Mining of Imaging Biomarkers for Quantitative Evaluation of Osteoarthritis (2/5)
  67. Chapter 11. Mining of Imaging Biomarkers for Quantitative Evaluation of Osteoarthritis (3/5)
  68. Chapter 11. Mining of Imaging Biomarkers for Quantitative Evaluation of Osteoarthritis (4/5)
  69. Chapter 11. Mining of Imaging Biomarkers for Quantitative Evaluation of Osteoarthritis (5/5)
  70. Chapter 12. Supervised Classification of Digital Mammograms (1/7)
  71. Chapter 12. Supervised Classification of Digital Mammograms (2/7)
  72. Chapter 12. Supervised Classification of Digital Mammograms (3/7)
  73. Chapter 12. Supervised Classification of Digital Mammograms (4/7)
  74. Chapter 12. Supervised Classification of Digital Mammograms (5/7)
  75. Chapter 12. Supervised Classification of Digital Mammograms (6/7)
  76. Chapter 12. Supervised Classification of Digital Mammograms (7/7)
  77. Chapter 13. Biofilm Image Analysis: Automatic Segmentation Methods and Applications (1/7)
  78. Chapter 13. Biofilm Image Analysis: Automatic Segmentation Methods and Applications (2/7)
  79. Chapter 13. Biofilm Image Analysis: Automatic Segmentation Methods and Applications (3/7)
  80. Chapter 13. Biofilm Image Analysis: Automatic Segmentation Methods and Applications (4/7)
  81. Chapter 13. Biofilm Image Analysis: Automatic Segmentation Methods and Applications (5/7)
  82. Chapter 13. Biofilm Image Analysis: Automatic Segmentation Methods and Applications (6/7)
  83. Chapter 13. Biofilm Image Analysis: Automatic Segmentation Methods and Applications (7/7)
  84. Chapter 14. Discovering Association of Diseases in the Upper Gastrointestinal Tract Using Text Mining Techniques (1/5)
  85. Chapter 14. Discovering Association of Diseases in the Upper Gastrointestinal Tract Using Text Mining Techniques (2/5)
  86. Chapter 14. Discovering Association of Diseases in the Upper Gastrointestinal Tract Using Text Mining Techniques (3/5)
  87. Chapter 14. Discovering Association of Diseases in the Upper Gastrointestinal Tract Using Text Mining Techniques (4/5)
  88. Chapter 14. Discovering Association of Diseases in the Upper Gastrointestinal Tract Using Text Mining Techniques (5/5)
  89. Chapter 15. Mental Health Informatics: Scopes and Challenges (1/4)
  90. Chapter 15. Mental Health Informatics: Scopes and Challenges (2/4)
  91. Chapter 15. Mental Health Informatics: Scopes and Challenges (3/4)
  92. Chapter 15. Mental Health Informatics: Scopes and Challenges (4/4)
  93. Chapter 16. Systems Engineering for Medical Informatics (1/6)
  94. Chapter 16. Systems Engineering for Medical Informatics (2/6)
  95. Chapter 16. Systems Engineering for Medical Informatics (3/6)
  96. Chapter 16. Systems Engineering for Medical Informatics (4/6)
  97. Chapter 16. Systems Engineering for Medical Informatics (5/6)
  98. Chapter 16. Systems Engineering for Medical Informatics (6/6)
  99. Back Cover

Product information

  • Title: Data Mining in Biomedical Imaging, Signaling, and Systems
  • Author(s): Sumeet Dua, Rajendra Acharya U
  • Release date: April 2016
  • Publisher(s): Auerbach Publications
  • ISBN: 9781439839393