chapter1 intro of biomedical signal processing

7/31/2019 Chapter1 Intro of Biomedical Signal Processing

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2012/10/31 1 Zhongguo Liu_Biomedical Engineering_Shandong Univ

biomedical Signal processing

Chapter 1 IntroductionZhongguo Liu

Biomedical Engineering

School of Control Science and Engineering, Shandong University


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2012/10/312 Zhongguo Liu_Biomedical Engineering_Shandong Univ.

Self Introduction

[email protected]:88384747

cellphone:18764171197


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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 getan idea of how they are

applied and to which kind of problems

To get to know basic digital signal

processing and analysistechniques commonly applied to biomedical

signals and to

know to which kind of problems each methodis suited for (and for which not)


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biomedical Signal Processing

Signal: any physical quantity that varies as a

function of an independent variable independent variable is usually time but may

be space, distance, ...

Biomedical signal: a signal being obtained froma biologic system /originating from aphysiologic process (human or animal (-medical -> patients))

Processing of biomedical signalsall treatment (of biomedical signals) which

occurs between their origin in a physiologicalprocess and their interpretation by their

observer (e.g. clinician)


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Processing of biomedical signals


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Processing of biomedical signals

Processing of biomedical signals is applicationof signal processing methods on biomedicalsignals

All possible processing algorithms may be

used

Biomedical signal processingrequiresunderstanding the needs (e.g. biomedical

processes and clinical requirements)andselecting and applying suitable methods tomeet these needs


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Rationales for biomedical signalprocessing

1.Acquisition and processing to extract a prioridesired information

2.Interpreting the nature of a physiological

process, based either on

a) observation of a signal (explorative nature),or

b) observation of how the process alters thecharacteristics of a signal (monitoring achange of a predefined characteristic)


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(Some) goals for biomedical signalprocessing

Quantification and compensation for theeffects of measuring devices and noise onsignal

Identification and separation of desiredand unwanted components of a signal Uncovering the nature of phenomena

responsible for generating the signal on thebasis of the analysis of the signalcharacteristics

Related to modelling / inverse modellingbut often more pragmatic


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Example: heart rate meters

Sensor Signal processing User


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Example: IST Vivago WristCare


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Health monitoring

Need for processing to

draw any conclusions

Beat-to-beat heart rate

Systolic and diastolic blood pressure


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Signal processing methods

Noise reduction

PreprocessingSignal validation

Feature extraction

Data compressionSegmentation

Pattern recognition

Trend detectionEvent detection

Decision support

Decision making

Filtering (linear, nonlinear,adaptive, optimal)

Statistical signal processing

Frequency domain analysis

Time-frequency analysisFuzzy logic

Artificial neural networks

Expert systems, rule-basedsystems

Genetic and evolutionarymethods


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Signal processing methods

Signal modellingWavelets and filter banks

PCA, ICA, SVD

Clustering

Higher-order statistics

Chaos and nonlinear dynamics

Complexity and fractals

Choose right method for right problem!


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Biomedical signal classification

On the basis of

signal characteristics: technical point of view

signal source: from where and how the signal

is originated and measured biomedical application: neurophysiology,

cardiology, monitoring, diagnosis,

Classification may be helpful in theselection of processing methods...


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Definitions

Deterministic: may be accurately describedmathematically, Usually predictable (not incase of chaos!)

Periodic: s(t)=s(t+nT)Almost periodic: patterns repeat with some

unregularity

Transient: signal characteristics changewith time


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Definitions

Stochastic: defined by their statisticalproperties (distribution)

Stationary: statistical properties of the

signal do not change over timeErgodic: statistical properties may be

computed along time distributions

(White noise: acf = 0 except for =0where acf=1; flat spectrum)


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Definitions

All real (bio)signals may beconsidered stochastic

almost deterministic signals (e.g. ECG):

wave shapes that (almost) repeatthemselves characterization (often) bydetection of certain measures or waves

truly stochastic (e.g. EEG)

characterization by statistical properties


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Classification by source

biomedical signals differ from othersignals only in terms of the application -signals that are used in the biomedicalfield

Bioelectric signals: generated bynerves cells and muscle cells. Single cellmeasurements (microelectrodes measureaction potential) and gross measurements(surface electrodes measure action ofmany cells in the vicinity)


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Biomagnetic signals: brain, heart,

lungs produce extremely weak magneticfields, this contains additional informationto that obtained from bioelectric signals.

Can be measured using SQUIDs. Bioimpedance signals: tissue

impedance reveals info about tissue

composition, blood volume and distributionand more. Usually two electrodes to injectcurrent and two to measure voltage drop


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Bioacoustic signals: many phenomena

create acoustic noise. For example, flow ofblood through the heart, its valves, orvessels and flow of air through upper andlower airways and lungs, but also digestivetract, joints and contraction of muscles.Record using microphones.

Biomechanical signals: motion and

displacement signals, pressure, tension andflow signals. A variety of measurements(not always simple, often invasivemeasurements are needed).


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Biochemical signals: chemical

measurements from living tissue or samplesanalyzed in a laboratory. For examples, ionconcentrations or partial pressures (pO2 or

pCO2) in blood. (low frequency signals,often actually DC signals)

Biooptical signals: blood oxygenation

by measuring transmitted andbackscattered light from a tissue,estimation of heart output by dye dilution.

Fiberoptic technology.


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Biomedical application domains

Information gathering

measurement of phenomena tounderstand the system

Diagnosis

detection of malfunction, pathology, orabnormality

Monitoring to obtain continuous or periodicinformation about the system


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Biomedical application domains

Therapy and control modify the behaviour of the system andensure the result

Evaluation

objective analysis: proof ofperformance, quality control, effect of

treatment


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Problems in biomedical signalprocessing

Accessibility

Patient safety, preference fornoninvasiveness

Indirect measurements (variables ofinterest are not accessible)

Variance

Inter-individual, intra-individual


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Problems in biomedical signalprocessing

Inter-relationships and interactions amongphysiological system

Subsystem of interest may not be isolatedAcquisition interference

Instrumentation and procedures modify

the system or its state


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Artefacts and interference

Interference from other physiologicalsystems (e.g. muscle artifacts in EEGrecordings)

Low-level signals (e.g. microvolts in EEG)require very sensitive amplifiers; they areeasily sensitive to interference, too!

Limited possibilities for shielding or otherprotection Nonlinearity and obscurity of thesystem under study


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Artefacts and interference

basically all biological systems exhibitnonlinearities while most of the methodsare based on the assumption of linearity

approximation exact structures and true function of

many physiological systems are often not

known


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Signal acquisition


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Short-term HRV and BPV

i l i


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signal processing

Applications of signal processing:entertainment, communications, space

exploration, medicine, archaeology(

), etc.Driven by the convergence of

communications, computers and signal

processing.

i l i


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signal processing

Signal processing is benefited from aclose coupling between theory,

application, and technologies for

implementing signal processingsystems.

Signal processing is concerned with the

representation, transformation, andmanipulation of signals and the

information they contain.


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Continuous and Digital Signal Processing

Prior to 1960: continuous-time analogsignal processing.

Digital signal processing is caused by:

the evolution of digital computers andmicroprocessors

Important 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 usingdigital computation

Digital signal processing is a specialcase 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

Di t ti P i


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Discrete-time Processing

Discrete-time processing of continuous-time signal

Real-time operation is often desirable:output is computed at the same rate atwhich the input is sampled

Obj t f Si l P i


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Objects of Signal Processing

Process one signal to obtain another signal

Signal interpretation: Characterization of theinput signal,

Example: speech recognition

digital preprocessing(filtering,parameter

estimation,etc)

speech

signalpatternrecognition

exertsystem

phonemictranscription

final signal

interpretation

Objects of Signal P ocessing


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Objects of Signal Processing

Symbolic manipulation of signalprocessing expression: signal and

systems are represented and

manipulated as abstract data objects,

without explicitly evaluating the data

sequence

Why do We Learn DSP


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Why do We Learn DSP

Software, such as Matlab, has manytools for signal processing

It seems that it is not necessary toknow the details of these algorithms,such as FFTA good understanding of the concepts

of algorithms and principles is essential

for intelligent use of the signalprocessing software tools

Extension


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Extension

Multidimensional signal processing

image processing

Spectral Analysis

Signal modeling

Adaptive signal processingSpecialized filter design

Specialized algorithm for evaluation of

Fourier transformSpecialized filter structure

Multirate signal processing

Walet transform

Historical Perspective


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17th century

The invention of calculusScientist developed models of physical

phenomena in terms of functions of

continuous variable and differentialequations

Numerical technique is used to solvethese equations

Newton used finite-difference methodswhich are special cases of some discrete-time systems



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18th century

Mathematicians developed methods fornumerical integration and interpolation ofcontinuous functions

Gauss (1805)discovered the fundamentalprinciple of the Fast Fourier Transform(FFT) even before the publication(1822)of Fourier's treatise on harmonic seriesrepresentation of function (proposed in1807)



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Early 1950s

signal processing was done with analogsystem, implemented with electronicscircuits or mechanical devices.first uses

of digital computers in digital signalprocessing was in oil prospecting.

Simulate signal processing system on adigital computer before implementing it

in analog hardware, ex. vocoder



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With flexibility the digital computer was

used to approximate, or simulate, an analogsignal processing system

The digital signal processing could not bedone in real time

Speed, cost, and size are three of theimportant factors of the use of analogcomponents.

Some digital flexible algorithm had nocounterpart in analog signal processing,impractical. all-digital implementationtempting



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FFT discovered by Cooley and Tukey in

1965

an efficient algorithm for computationof Fourier transforms, which reduce thecomputing time by orders of magnitude.

FFT might be implemented in special-

purpose digital hardwareMany impractical signal processingalgorithms became to be practical



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FFT is an inherently discrete-timeconcept. FFT stimulated a reformulationof many signal processing concepts and

algorithms in terms of discrete-timemathematics, which formed an exact setof relationships in the discrete-time

domain, so there emerged a field ofdiscrete-time signal processing.



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The invention and proliferation of themicroprocessor paved the way for low-costimplementations of discrete-time signalprocessing systems

The mid-1980s, IC technology permittedthe implementation of very fast fixed-pointand floating-point microcomputer.

The architectures of these microprocessorare specially designed for implementingdiscrete-time signal processing algorithm,named as Digital Signal Processors(DSP).

chapter1 intro of biomedical signal processing

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