1. 2015-9-41Zhongguo Liu_Biomedical Engineering_Shandong Univ. Biomedical Signal processing Chapter 1 Introduction 刘忠国 Zhongguo Liu Biomedical Engineering School of Control Science and Engineering, Shandong University

2. 2015-9-4 2 Zhongguo Liu_Biomedical Engineering_Shandong Univ. Self Introduction 刘忠国： liuzhg@sdu.edu.cn 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

3. 2015-9-4 3 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.

4. 2015-9-4 4 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)

5. 2015-9-4 5 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

6. 2015-9-4 6 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

7. 2015-9-4 7 Zhongguo Liu_Biomedical Engineering_Shandong Univ. Discrete-time Processing  Discrete-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

8. 2015-9-4 8 Zhongguo Liu_Biomedical Engineering_Shandong Univ. Objects of Signal Processing  Process one signal to obtain another signal;  Signal interpretation: Characterization of the input signal. digital preprocessing (filtering,parameter estimation,etc) speech signal pattern recognition expert system phonemic transcription final signal interpretation Example: speech recognition

9. 2015-9-4 9 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.

10. 2015-9-4 10 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.

11. Processing of biomedical signals

12.  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

15. Example: heart rate meters Sensor Signal processing User

16. Example: IST Vivago® WristCare

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

19. 2015-9-4 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.

20. 2015-9-4 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

21. 2015-9-4 21 Zhongguo Liu_Biomedical Engineering_Shandong Univ. Historical Perspective  17 th 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

22. 2015-9-4 22 Zhongguo Liu_Biomedical Engineering_Shandong Univ. Historical Perspective  18 th century  Mathematicians developed methods for numerical integration and interpolation of continuous functions  19 th 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)

23. 2015-9-4 23 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

24. 2015-9-4 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

25. 2015-9-4 25 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 special- purpose digital hardware  Many impractical signal processing algorithms became to be practical

26. 2015-9-4 26 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.

27. 2015-9-4 27 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).

28. 2015-9-4 28 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)