Sumeet Dua, Rajendra Acharya U
Published May 16, 2011
Reference - 440 Pages - 137 B/W Illustrations
ISBN 9781439839386 - CAT# K11798
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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 biomedical and clinical applications of data mining. It supplies examples of frequently encountered heterogeneous data modalities and details the applicability of data mining approaches used to address the computational challenges in analyzing complex data.
The book details feature extraction techniques and covers several critical feature descriptors. As machine learning is employed in many diagnostic applications, it covers the fundamentals, evaluation measures, and challenges of supervised and unsupervised learning methods. Both feature extraction and supervised learning are discussed as they apply to seizure-related patterns in epilepsy patients. Other specific disorders are also examined with regard to the value of data mining for refining clinical diagnoses, including depression and recurring migraines. The diagnosis and grading of the world’s fourth most serious health threat, depression, and analysis of acoustic properties that can distinguish depressed speech from normal are also described. Although a migraine is a complex neurological disorder, the text demonstrates how metabonomics can be effectively applied to clinical practice.
The authors review alignment-based clustering approaches, techniques for automatic analysis of biofilm images, and applications of medical text mining, including text classification applied to medical reports. The identification and classification of two life-threatening heart abnormalities, arrhythmia and ischemia, are addressed, and a unique segmentation method for mining a 3-D imaging biomarker, exemplified by evaluation of osteoarthritis, is also presented. Given the widespread deployment of complex biomedical systems, the authors discuss system-engineering principles in a proposal for a design of reliable systems. This comprehensive volume demonstrates the broad scope of uses for data mining and includes detailed strategies and methodologies for analyzing data from biomedical images, signals, and systems.
Data Mining of Acoustical Properties of Speech as Indicators of Depression; Kumara Shama Ananthakrishna, V. P. Bhandary, K. B. Kumar, and U. C. Niranjan
Artificial Neural Network Based ECG Arrhythmia Classification; H. Haseena, Paul K. Joseph, and Abraham T. Mathew
Human Reflexive Response and its Objective Function Regarding Balance Recovery From Perturbation During Walking; Yu Yikemoto, Wenwei Yu, and Rajendra Acharya U
Automatic Identification of Epileptic EEG Signals Using Nonlinear Parameters; Rajendra Acharya U
Data Mining Approach to Classify the Pathological Images in Databases Using Color Image Analysis, Keerthana Prasad, Muktha Pai, and Gopalakrishna Prabhu
Discovery of Association Among Diseases in the Upper Gastro Intestinal Tract Using Data Mining Techniques, S. S. Saraf, G. R. Udupi, Santosh D. Hajare
Data Analysis Techniques Applied to Metabolomics: Analysis and Classification of Migraine Patterns, Pierangela Giustetto, Filippo Molinari, William Liboni, Cecilia Castro, and Cesare Manetti
System Engineering Principles in the Design of Biomedical Systems; Oliver Faust, Bernard Sputh, and Rajendra Acharya U
Feature Classification Methods for Knowledge Discovery in Mammograms; Sumeet Dua and Harpreet Singh
Mining of Imaging Biomarkers for Quantitative Evaluation of Osteosrthritis; Xian Du and Sumeet Dua
Typicality Measure and the Creation of Predictive Models in Biomedicine; Mila Kwiatkowska, Krzysztof Kielan, Najib T. Ayas, and C. Frank Ryan
Automatic Segmentation Methods and Applications to Biofilm Image Analysis; Dario Rojas, Luis Rueda, Homero Urrutia, Alioune Ngom, and Gerardo Carcamo
On Clustering Gene Expression Time-Series Signals; Numanul Subhani, Luis Ruedal, and Alioune Ngoml
Multi-Scale Method For Biomedical Image Segmentation; Michael K. Dessauerl and Sumeet Dual