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Biomedical Signal processing Chapter 1 Introduction

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Title: Biomedical Signal processing Chapter 1 Introduction


1
Biomedical Signal processing Chapter 1
Introduction
  • ???Zhongguo Liu
  • Biomedical Engineering
  • School of Control Science and Engineering,
    Shandong University

2015-3-17
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Zhongguo Liu_Biomedical Engineering_Shandong Univ.
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Self Introduction
???liuzhg_at_sdu.edu.cn cellphone18764171197
Tel84192
???????????????(??) http//course.sdu.edu.cn/bds
p.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
    microprocessors
  • Important theoretical developments such as the
    Fast Fourier Transform algorithm (FFT)

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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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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 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

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Objects of Signal Processing
  • Process one signal to obtain another signal
  • Signal interpretation Characterization of the
    input signal.

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.

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Processing of biomedical signals
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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

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Example heart rate meters
Signal processing
Sensor
User
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Example IST Vivago WristCare
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Health monitoring
Systolic and diastolic blood pressure
Beat-to-beat heart rate
  • Need for processing to
  • draw any conclusions

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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 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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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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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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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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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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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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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 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)

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