کلمات کلیدی مربوط به کتاب تبدیل موج های گسسته - برنامه های کاربردی پزشکی: ابزار دقیق، پردازش سیگنال، تجزیه و تحلیل موجک
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Издательство InTech, 2011, -378 pp.
The discrete wavelet transform (DWT)
has an established role in multi-scale processing of biomedical
signals, such as EMG and EEG. Since DWT algorithms provide both
octave-scale frequency and spatial timing of the analyzed
signal. Hence, DWTs are constantly used to solve and treat more
and more advanced problems. The DWT algorithms were initially
based on the compactly supported conjugate quadrature filters
(CQFs). However, a drawback in CQFs is due to the nonlinear
phase effects such as spatial dislocations in multi-scale
analysis. This is avoided in biorthogonal discrete wavelet
transform (BDWT) algorithms, where the scaling and wavelet
filters are symmetric and linear phase. The biorthogonal
filters are usually constructed by a ladder-type network called
lifting scheme. Efficient lifting BDWT structures have been
developed for microprocessor and VLSI environment. Only integer
register shifts and summations are needed for implementation of
the analysis and synthesis filters. In many systems BDWT-based
data and image processing tools have outperformed the
conventional discrete cosine transform (DCT) -based approaches.
For example, in JPEG2000 Standard the DCT has been replaced by
the lifting BDWT.
A difficulty in multi-scale DWT analyses is the dependency of
the total energy of the wavelet coefficients in different
scales on the fractional shifts of the analysed signal. This
has led to the development of the complex shift invariant DWT
algorithms, the real and imaginary parts of the complex wavelet
coefficients are approximately a Hilbert transform pair. The
energy of the wavelet coefficients equals the envelope, which
provides shift-invariance. In two parallel CQF banks, which are
constructed so that the impulse responses of the scaling
filters have half-sample delayed versions of each other, the
corresponding wavelet bases are a Hilbert transform pair.
However, the CQF wavelets do not have coefficient symmetry and
the nonlinearity disturbs the spatial timing in different
scales and prevents accurate statistical analyses. Therefore
the current developments in theory and applications of shift
invariant DWT algorithms are concentrated on the dual-tree BDWT
structures. The dual-tree BDWTs have appeared to outperform the
real-valued BDWTs in several applications such as denoising,
texture analysis, speech recognition, processing of seismic
signals and multiscale-analysis of neuroelectric signals.
This book reviews the recent progress in DWT algorithms for
biomedical applications. The book covers a wide range of
architectures (e.g. lifting, shift invariance, multi-scale
analysis) for constructing DWTs. The book chapters are
organized into four major parts. Part I describes the progress
in implementations of the DWT algorithms in biomedical signal
analysis. Applications include compression and filtering of
biomedical signals, DWT based selection of salient EEG
frequency band, shift invariant DWTs for multiscale analysis
and DWT assisted heart sound analysis. Part II addresses speech
analysis, modeling and understanding of speech and speaker
recognition. Part III focuses biosensor applications such as
calibration of enzymatic sensors, multiscale analysis of
wireless capsule endoscopy recordings, DWT assisted electronic
nose analysis and optical fibre sensor analyses. Finally, Part
IV describes DWT algorithms for tools in identification and
diagnostics: identification based on hand geometry,
identification of species groupings, object detection and
tracking, DWT signatures and diagnostics for assessment of ICU
agitation-sedation controllers and DWT based diagnostics of
power transformers.
The chapters of the present book consist of both tutorial and
highly advanced material. Therefore, the book is intended to be
a reference text for graduate students and researchers to
obtain state-of-the-art knowledge on specific applications. The
editor is greatly indebted to all co-authors for giving their
valuable time and expertise in constructing this book. The
technical editors are also acknowledged for their tedious
support and help.
Part 1 Biomedical Signal
Analysis
Biomedical Applications of the Discrete Wavelet Transform
Discrete Wavelet Transform in Compression and Filtering of
Biomedical Signals
Discrete Wavelet Transform Based Selection of Salient EEG
Frequency Band for Assessing Human Emotions
Discrete Wavelet Transform Algorithms for Multi-Scale Analysis
of Biomedical Signals
Computerized Heart Sounds Analysis
Part 2 Speech Analysis
Modelling and Understanding of Speech and Speaker
Recognition
Discrete Wavelet Transform & Linear Prediction Coding Based
Method for Speech Recognition via Neural Network
Part 3 Biosensors
Implementation of the Discrete Wavelet Transform Used in the
Calibration of the Enzymatic Biosensors
Multiscale Texture Descriptors for Automatic Small Bowel Tumors
Detection in Capsule Endoscopy
Wavelet Transform for Electronic Nose Signal Analysis
Wavelets in Electrochemical Noise Analysis
Applications of Discrete Wavelet Transform in Optical Fibre
Sensing
Part 4 Identification and Diagnostics
Biometric Human Identification of Hand Geometry Features Using
Discrete Wavelet Transform
Wavelet Signatures of Climate and Flowering: Identification of
Species Groupings
Multiple Moving Objects Detection and Tracking Using Discrete
Wavelet Transform
Wavelet Signatures and Diagnostics for the Assessment of ICU
Agitation-Sedation Protocols
Application of Discrete Wavelet Transform for Differential
Protection of Power Transformers