Biosignal and Biomedical Image Processing MATLAB based Applications - John L. Semmlow
Preface
Signal processing can be broadly defined as the application of analog or digital
techniques to improve the utility of a data stream. In biomedical engineering
applications, improved utility usually means the data provide better diagnostic
information. Analog techniques are applied to a data stream embodied as a timevarying
electrical signal while in the digital domain the data are represented as
an array of numbers. This array could be the digital representation of a timevarying
signal, or an image. This text deals exclusively with signal processing
of digital data, although Chapter 1 briefly describes analog processes commonly
found in medical devices.
This text should be of interest to a broad spectrum of engineers, but it
is written specifically for biomedical engineers (also known as bioengineers).
Although the applications are different, the signal processing methodology used
by biomedical engineers is identical to that used by other engineers such electrical
and communications engineers. The major difference for biomedical engineers
is in the level of understanding required for appropriate use of this technology.
An electrical engineer may be required to expand or modify signal
processing tools, while for biomedical engineers, signal processing techniques
are tools to be used. For the biomedical engineer, a detailed understanding of
the underlying theory, while always of value, may not be essential. Moreover,
considering the broad range of knowledge required to be effective in this field,
encompassing both medical and engineering domains, an in-depth understanding
of all of the useful technology is not realistic. It is important is to know what
tools are available, have a good understanding of what they do (if not how they
do it), be aware of the most likely pitfalls and misapplications, and know how
to implement these tools given available software packages. The basic concept
of this text is that, just as the cardiologist can benefit from an oscilloscope-type
display of the ECG without a deep understanding of electronics, so a biomedical
engineer can benefit from advanced signal processing tools without always understanding
the details of the underlying mathematics.
As a reflection of this philosophy, most of the concepts covered in this
text are presented in two sections. The first part provides a broad, general understanding
of the approach sufficient to allow intelligent application of the concepts.
The second part describes how these tools can be implemented and relies
primarily on the MATLAB software package and several of its toolboxes.
This text is written for a single-semester course combining signal and
image processing. Classroom experience using notes from this text indicates
that this ambitious objective is possible for most graduate formats, although
eliminating a few topics may be desirable. For example, some of the introductory
or basic material covered in Chapters 1 and 2 could be skipped or treated
lightly for students with the appropriate prerequisites. In addition, topics such
as advanced spectral methods (Chapter 5), time-frequency analysis (Chapter 6),
wavelets (Chapter 7), advanced filters (Chapter 8), and multivariate analysis (Chapter 9)
are pedagogically independent and can be covered as desired without affecting the other material.
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Preface
Signal processing can be broadly defined as the application of analog or digital
techniques to improve the utility of a data stream. In biomedical engineering
applications, improved utility usually means the data provide better diagnostic
information. Analog techniques are applied to a data stream embodied as a timevarying
electrical signal while in the digital domain the data are represented as
an array of numbers. This array could be the digital representation of a timevarying
signal, or an image. This text deals exclusively with signal processing
of digital data, although Chapter 1 briefly describes analog processes commonly
found in medical devices.
This text should be of interest to a broad spectrum of engineers, but it
is written specifically for biomedical engineers (also known as bioengineers).
Although the applications are different, the signal processing methodology used
by biomedical engineers is identical to that used by other engineers such electrical
and communications engineers. The major difference for biomedical engineers
is in the level of understanding required for appropriate use of this technology.
An electrical engineer may be required to expand or modify signal
processing tools, while for biomedical engineers, signal processing techniques
are tools to be used. For the biomedical engineer, a detailed understanding of
the underlying theory, while always of value, may not be essential. Moreover,
considering the broad range of knowledge required to be effective in this field,
encompassing both medical and engineering domains, an in-depth understanding
of all of the useful technology is not realistic. It is important is to know what
tools are available, have a good understanding of what they do (if not how they
do it), be aware of the most likely pitfalls and misapplications, and know how
to implement these tools given available software packages. The basic concept
of this text is that, just as the cardiologist can benefit from an oscilloscope-type
display of the ECG without a deep understanding of electronics, so a biomedical
engineer can benefit from advanced signal processing tools without always understanding
the details of the underlying mathematics.
As a reflection of this philosophy, most of the concepts covered in this
text are presented in two sections. The first part provides a broad, general understanding
of the approach sufficient to allow intelligent application of the concepts.
The second part describes how these tools can be implemented and relies
primarily on the MATLAB software package and several of its toolboxes.
This text is written for a single-semester course combining signal and
image processing. Classroom experience using notes from this text indicates
that this ambitious objective is possible for most graduate formats, although
eliminating a few topics may be desirable. For example, some of the introductory
or basic material covered in Chapters 1 and 2 could be skipped or treated
lightly for students with the appropriate prerequisites. In addition, topics such
as advanced spectral methods (Chapter 5), time-frequency analysis (Chapter 6),
wavelets (Chapter 7), advanced filters (Chapter 8), and multivariate analysis (Chapter 9)
are pedagogically independent and can be covered as desired without affecting the other material.
Download
*