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What is Wavelet Transform

Encyclopedia of Healthcare Information Systems
Refers to a transformation where a signal containing N samples is first filtered by a pair of filters that decompose the signal into low and a high frequency bands. Each band is undersampled by a factor of two, that is, each band contains N/2 samples. With the appropriate filter design, this action is reversible.
Published in Chapter:
Biomedical Signal Compression
Pedro de A. Berger (University of Brasilia, Brazil), Francisco A. de O. Nascimento (University of Brasilia, Brazil), Leonardo R.A.X. de Menezes (University of Brasilia, Brazil), Adson F. da Rocha (University of Brasilia, Brazil), and Joao L.A. Carvalho (University of Southern California, USA)
Copyright: © 2008 |Pages: 10
DOI: 10.4018/978-1-59904-889-5.ch022
Abstract
Digitization of biomedical signals has been used in several areas. Some of these include ambulatory monitoring, phone line transmission, database storage, and several other applications in health and biomedical engineering. These applications have helped in diagnostics, patient care, and remote treatment. One example is the digital transmission of ECG signals, from the patient’s house or ambulance to the hospital. This has been proven useful in cardiac diagnoses. Biomedical signals need to be digitally stored or transmitted with a large number of samples per second, and with a great number of bits per sample, in order to assure the required fidelity of the waveform for visual inspection. Therefore, the use of signal compression techniques is fundamental for cost reduction and technical feasibility of storage and transmission of biomedical signals.
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More Results
Pit Pattern Classification Using Multichannel Features and Multiclassification
A transform used to decompose a signal into its frequency components, similar to the Fourier transform. But the time-frequency resolution of the wavelet transform can be adjusted since basis functions with compact support are used, in contrast to the Fourier transform, where sine and cosines are used as basis functions.
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PCG-Based Biometrics
A transform that characterizes various functions in wavelet. It has a better advantage over Fourier transform as it deconstructs or constructs signal accurately.
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Myoelectric Control of Prosthetic Devices for Rehabilitation
In mathematics, wavelets, wavelet analysis, and the wavelet transform refers to the representation of a signal in terms of a finite length or fast-decaying, oscillating waveform (known as the mother wavelet). This waveform is scaled and translated to match the input signal. In formal terms, this representation is a wavelet series, which is the coordinate representation of a square integrable function with respect to a complete, orthonormal set of basis functions for the Hilbert space of square integrable functions.
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Compressive Spectrum Sensing: Wavelet-Based Compressive Spectrum Sensing in Cognitive Radio
A transform characterized by its ability to provide time-frequency resolution and is considered a powerful tool for analyzing local singularities in a signal, which makes it suitable for edge detection.
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Methodologies of Damage Identification Using Non-Linear Data-Driven Modelling
Capable of revealing aspects of data that other signal analysis techniques miss, aspects like trends, breakdown points, discontinuities in higher derivatives, and self-similarity. It will provide accurate location of the transient signals while simultaneously reporting the fundamental frequency and its low-order harmonics.
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Computational Models for the Analysis of Modern Biological Data
The representation of a signal in terms of scaled and translated copies of a finite length or fast decaying oscillating waveform.
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Arrhythmia Detection and Classification Using Wavelet and ICA
Representation of a signal in terms of a finite length or fast decaying oscillating waveform (known as the mother wavelet). This waveform is scaled and translated to match the input signal.
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Validation of Damage Identification Using Non-Linear Data-Driven Modelling
Capable of revealing aspects of data that other signal analysis techniques miss, aspects like trends, breakdown points, discontinuities in higher derivatives, and self-similarity. It will provide accurate location of the transient signals while simultaneously reporting the fundamental frequency and its low-order harmonics.
Full Text Chapter Download: US $37.50 Add to Cart
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