1. Biomedical Signal processing Chapter 1 Introduction
刘忠国Zhongguo Liu
Biomedical Engineering
School of Control Science and Engineering, Shandong University
2018/11/27 1
Zhongguo Liu_Biomedical Engineering_Shandong Univ.

2. 山东省精品课程《生物医学信号处理(双语)》
Self Introduction
cellphone: Tel:84192
山东省精品课程《生物医学信号处理(双语)》
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3. 2.Discrete-Time Signals and Systems 3.The z-Transform
Contents of the Course
1. Introduction
2.Discrete-Time Signals and Systems
3.The z-Transform
4.Sampling of Continuous-Time Signals
5.Transform Analysis of Linear Time-Invariant Systems
6.Structures for Discrete-Time Systems
7.Filter Design Techniques
8.The Discrete Fourier Transform
9.Computation of the Discrete Fourier Transform(FFT)
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4. 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.
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5. 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)
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6. 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
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7. 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
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8. Discrete-time Processing
Discrete-time processing of continuous-time signal
A/D
D/A
Digital system
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
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9. Objects of Signal Processing
Process one signal to obtain another signal;
Signal interpretation: Characterization of the input signal.
Example: speech recognition
digital preprocessing
(filtering,parameter estimation,etc)
speech
signal
pattern recognition
expert system
phonemic
transcription
final signal
interpretation
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10. 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.
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11. 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.
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12. Processing of biomedical signals

13. 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

16. Example: heart rate meters
Sensor
Signal processing
User

17. Example: IST Vivago® WristCare
average and standard deviation of activity are significantly lower in deep sleep than in light sleep.
activity monitoring device:

18. Health monitoring Systolic and diastolic blood pressure
Beat-to-beat heart rate
Need for processing to draw any conclusions

19. Zhongguo Liu_Biomedical Engineering_Shandong Univ.
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.
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20. 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
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21. 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 discrete-time systems
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22. 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)
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23. 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
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24. 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
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25. 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 special-purpose digital hardware
Many impractical signal processing algorithms became to be practical
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26. 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.
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27. 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).
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28. End Thanks! Zhongguo Liu_Biomedical Engineering_Shandong Univ. 30
2018/11/27
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