Transcript Chapter 1 Introduction Biomedical Signal processing 刘忠国Zhongguo Liu Biomedical Engineering

Biomedical Signal processing
Chapter 1 Introduction
刘忠国Zhongguo Liu
Biomedical Engineering
School of Control Science and Engineering, Shandong University
2016/5/24
1
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Self Introduction
刘忠国：[email protected]
cellphone:18764171197
Tel:84192
山东省精品课程《生物医学信号处理(双语)》
http://course.sdu.edu.cn/bdsp.html
2
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Chapter 1 Introduction
Signal processing is benefited from
a close coupling between theory,
application, and technologies for
implementing signal processing
systems.
Signal processing deals with the
representation, transformation, and
manipulation of signals and the
information they contain.
3
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Continuous and Digital Signal Processing
Prior to 1960: continuous-time analog
signal processing.
Digital signal processing is caused by:
the evolution of digital computers and
microprocessors
Important theoretical developments
such as the Fast Fourier Transform
algorithm (FFT)
4
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Digital and Discrete-time Signal Processing
In digital signal processing
Signals are represented by
sequences of finite-precision numbers
Processing is implemented using
digital computation
Digital signal processing is a special
case of discrete-time signal processing
5
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Digital and Discrete-time Signal Processing
Continuous-time signal processing:
time and signal are continuous
Discrete-time signal processing:
time is discrete, signal is continuous
Digital signal processing:
time and signal are discrete
6
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Discrete-time Processing
Discrete-time processing of continuous-time signal
ideal continuous-to-discrete-time (C/D) converter
ideal discrete-to-continuous-time (D/C) converter
Real-time operation is often desirable:
output is computed at the same rate at
which the input is sampled
7
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Objects of Signal Processing
Process one signal to obtain another signal;
Signal interpretation: Characterization of the
input signal.
Example: speech recognition
speech digital preprocessing
signal (filtering,parameter
estimation,etc)
pattern
recognition
final signal
interpretation
8
2016/5/24
phonemic
transcription
expert
system
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Objects of Signal Processing
Symbolic manipulation of signal
processing expression: signal and
systems are represented and
manipulated as abstract data objects,
without explicitly evaluating the data
sequence.
9
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Chapter 1 Introduction
Applications of signal processing:
entertainment, communications, space
exploration, medicine, archaeology, etc.
Role of signal processing is expanding,
driven by convergence of computers,
communications and signal processing.
10
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Processing of biomedical signals
Processing of biomedical signals
Processing of biomedical signals is application
of signal processing methods on biomedical
signals
→All possible processing algorithms may be
used
→Biomedical signal processing requires
understanding the needs (e.g. biomedical
processes and clinical requirements) and
selecting and applying suitable methods to
meet these needs
Example: heart rate meters
Sensor
Signal processing
User
Example: IST Vivago® WristCare
Health monitoring
Systolic and diastolic blood pressure
Need for processing to
draw any conclusions
Beat-to-beat heart rate
Why do We Learn DSP
Software, such as Matlab, has many
tools for signal processing.
It seems that it is not necessary to
know the details of these algorithms,
such as FFT.
A good understanding of the concepts
of algorithms and principles is essential
for intelligent use of the signal
processing software tools.
19
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Extension
Multidimensional signal processing
image processing
Spectral Analysis
Signal modeling
Adaptive signal processing
Specialized filter design
Specialized algorithm for evaluation of
Fourier transform
Specialized filter structure
Multirate signal processing
Wavlet transform
20
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Historical Perspective
17th century
The invention of calculus
Scientist developed models of physical
phenomena in terms of functions of
continuous variable and differential
equations
Numerical technique is used to solve
these equations
Newton used finite-difference methods
which are special cases of some discretetime systems
21
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Historical Perspective
18th century
Mathematicians developed methods for
numerical integration and interpolation of
continuous functions
19th century
Gauss (1805)discovered the fundamental
principle of the Fast Fourier Transform
(FFT) even before the publication(1822)
of Fourier's treatise on harmonic series
representation of function (proposed in
1807)
22
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Historical Perspective
Early 1950s
signal processing was done with analog
system, implemented with electronics
circuits or mechanical devices. first uses
of digital computers in digital signal
processing was in oil prospecting.
Simulate signal processing system on a
digital computer before implementing it
in analog hardware, ex. vocoder
23
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Historical Perspective
With flexibility the digital computer was
used to approximate, or simulate, an analog
signal processing system
The digital signal processing could not be
done in real time
Speed, cost, and size are three of the
important factors in favor of the use of
analog components.
Some digital flexible algorithm had no
counterpart in analog signal processing,
impractical. all-digital implementation
tempting
24
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Historical Perspective
FFT discovered by Cooley and Tukey in
1965
an efficient algorithm for computation
of Fourier transforms, which reduce the
computing time by orders of magnitude.
 FFT might be implemented in specialpurpose digital hardware
Many impractical signal processing
algorithms became to be practical
25
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Historical Perspective
FFT is an inherently discrete-time
concept. FFT stimulated a reformulation
of many signal processing concepts and
algorithms in terms of discrete-time
mathematics, which formed an exact set
of relationships in the discrete-time
domain, so there emerged a field of
discrete-time signal processing.
26
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Historical Perspective
The invention and proliferation of the
microprocessor paved the way for low-cost
implementations of discrete-time signal
processing systems
The mid-1980s, IC technology permitted
the implementation of very fast fixed-point
and floating-point microcomputer.
The architectures of these microprocessor
are specially designed for implementing
discrete-time signal processing algorithm,
named as Digital Signal Processors(DSP).
27
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.
Goals of the course
To understand: – what biomedical signals are;
– what problems and needs are related to
their acquisition and processing
– what kind of methods are available and get
an idea of how they are applied and to which
kind of problems
• To get to know basic digital signal
processing and analysis techniques commonly
applied to biomedical signals and to know
which kind of problems each method is suited
for (and for which not)
28
2016/5/24
Zhongguo Liu_Biomedical Engineering_Shandong Univ.