2015-9-41zhongguo liu_biomedical engineering_shandong univ. biomedical signal processing chapter 1...
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Biomedical Signal processing
Chapter 1 Introduction
刘忠国 Zhongguo Liu
Biomedical Engineering
School of Control Science and Engineering, Shandong University
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Self Introduction
刘忠国:刘忠国: [email protected]@sdu.edu.cncellphone:18764171197 cellphone:18764171197
Tel:84Tel:84192192
山东省精品课程山东省精品课程《《生物医学信号处理生物医学信号处理 (( 双双语语 )) 》》
http://course.sdu.edu.cn/http://course.sdu.edu.cn/bdsp.htmlbdsp.html
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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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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
microprocessorsImportant theoretical developments
such as the Fast Fourier Transform algorithm (FFT)
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Digital and Discrete-time Signal Processing
In digital signal processingSignals 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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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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Discrete-time ProcessingDiscrete-time processing of continuous-time signal
Real-time operation is often desirable: output is computed at the same rate at which the input is sampled
ideal continuous-to-discrete-time (C/D) converter
ideal discrete-to-continuous-time (D/C) converter
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Objects of Signal Processing
Process one signal to obtain another signal;Signal interpretation: Characterization of
the input signal.
digital preprocessing(filtering,parameter estimation,etc)
speechsignal
pattern recognition
expert system
phonemic transcription
final signal interpretation
Example: speech recognition
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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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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.
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 metersSensor Signal processing User
Example: IST Vivago® WristCare
Health monitoring
Need for processing todraw any conclusions
Beat-to-beat heart rate
Systolic and diastolic blood pressure
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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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Extension
Multidimensional signal processingimage processing
Spectral AnalysisSignal modelingAdaptive signal processingSpecialized filter designSpecialized algorithm for evaluation of
Fourier transformSpecialized filter structureMultirate signal processingWavlet transform
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Historical Perspective17th century
The invention of calculusScientist 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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Historical Perspective18th century
Mathematicians developed methods for numerical integration and interpolation of continuous functions
19th centuryGauss (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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Historical PerspectiveEarly 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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Historical PerspectiveWith 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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Historical PerspectiveFFT discovered by Cooley and Tukey in
1965an 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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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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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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Goals of the courseTo 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)