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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： liuzhg@sdu.edu.cnliuzhg@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)