Biomedical Signal Analysis
Contemporary Methods and Applications
Publication Year: 2010
Published by: The MIT Press
Cover
Title Page, Copyright
Contents
Preface
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pp. vii-xiii
Our nation’s strongest information technology (IT) industry advances are occurring in the life sciences, and it is believed that IT will play an increasingly important role in information-based medicine. Nowadays, the research and economic benefits are found at the intersection...
I. Methods
1. Foundations of Medical Imaging and Signal Recording
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pp. 3-28
Computer processing and analysis of medical images, as well as experimental data analysis of physiological signals, have evolved since the late 1980s from a variety of directions, ranging from signal and imaging acquisition equipment to areas such as digital signal and image processing...
2. Spectral Transformations
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pp. 29-70
Pattern recognition tasks require the conversion of biosignals in features describing the collected sensor data in a compact form. Ideally, this should pertain only to relevant information. Feature extraction is an important technique in pattern recognition by determining descriptors for...
3. Information Theory and Principal Component Analysis
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pp. 71-99
We first give a short, somewhat technical review of necessary concepts from probability and estimation theory. We then introduce some key elements from information theory, such as entropy and mutual information. As a first data analysis method, we finish this chapter by...
4. Independent Component Analysis and Blind Source Separation
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pp. 101-139
Biostatistics deals with the analysis of high-dimensional data sets originating from biological or biomedical problems. An important challenge in this analysis is to identify underlying statistical patterns that facilitate the interpretation of the data set using techniques from machine learning...
5. Dependent Component Analysis
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pp. 141-159
In this chapter, we discuss the relaxation of the BSS model by taking into account additional structures in the data and dependencies between components. Many researchers have taken interest in this generalization, which is crucial for the application in real-world settings where such...
6. Pattern Recognition Techniques
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pp. 161-216
Modern classification paradigms such as neural networks, genetic algorithms, and neuro–fuzzy methods have become very popular tools in medical imaging. Whether diagnosis, therapeutics, or prognosis, artificial intelligence methods are leaders in these applications. In conjunction...
7. Fuzzy Clustering and Genetic Algorithms
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pp. 217-252
Biosignals are characterized by uncertainties resulting from incomplete or imprecise input information, ambiguity, ill–defined or overlapping boundaries among the disease classes or regions, and indefiniteness in extracting features and relations among them. Any decision taken at a...
II. Applications
8. Exploratory Data Analysis Methods for fMRI
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pp. 255-262
Functional magnetic resonance imaging (fMRI) has been shown to be an effective imaging technique in human brain research [188]. By blood oxygen level- dependent contrast (BOLD), local changes in the magnetic field are coupled to activity in brain areas. These magnetic changes are...
9. Low-frequency Functional Connectivity in fMRI
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pp. 263-274
Low-frequency fluctuations (< 0.08 Hz) temporally correlated between functionally related areas have been reported for the motor, auditory, and visual cortices and other structures [35]. The detection and quantification of these patterns without user bias poses a current challenge in...
10. Classification of Dynamic Breast MR Image Data
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pp. 275-298
Breast cancer is the most common cancer among women. Magnetic resonance (MR) is an emerging and promising new modality for detection and further evaluation of clinically, mammographically, and sonographically occult cancers [115, 293]. However, film and soft-copy reading and...
11. Dynamic Cerebral Contrast-enhanced Perfusion MRI
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pp. 299-323
Novel magnetic resonance imaging (MRI) techniques have emerged since the 1990s that allow for rapid assessment of normal brain function as well as cerebral pathophysiology. Both diffusion-weighted imaging and perfusion-weighted imaging have already been used extensively for...
12. Skin Lesion Classification
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pp. 325-348
This chapter describes an application of biomedical image analysis: the detection of malignant and benign skin lesions by employing local information rather than global features. For this we will build a neural network model in order to classify these different skin lesions by means...
13. Microscopic Slice Image Processing and Automatic Labeling
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pp. 349-379
A supervised interpretation of the initial data analysis model from section 4.1 leads to a classification problem: given a set of input-output samples, find a map that interpolates these samples, and, hopefully generalizes well to new input samples. Such a map thus serves as classifier...
14. NMR Water Artifact Removal
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pp. 381-395
Multidimensional proton nuclear magnetic resonance (NMR) spectra of biomolecules dissolved in aqueous solutions are usually contaminated by an intense water artifact. In this chapter, we will discuss the application of the generalized eigenvalue decomposition method using a matrix...
References
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pp. 397-411
Index
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pp. 413-415
Image Plates
E-ISBN-13: 9780262259248
Print-ISBN-13: 9780262013284
Page Count: 432
Publication Year: 2010
