1、2022-2-121Zhongguo Liu_Biomedical Engineering_Shandong Univ.biomedical Signal processing生物醫學信號處理Chapter 1 Introduction劉忠國Zhongguo LiuBiomedical EngineeringSchool of Control Science and Engineering, Shandong University2022-2-122Zhongguo Liu_Biomedical Engineering_Shandong Univ.Self IntroductionGoals

2、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 areapplied and to which kind of problems To get to know basic digital signal processing and analysistechnique

3、s commonly applied to biomedical signals and toknow to which kind of problems each method is suited for (and for which not)biomedical Signal ProcessingSignal: any physical quantity that varies as a function of an independent variable independent variable is usually time but may be space, distance, .

4、Biomedical signal: a signal being obtained from a biologic system /originating from a physiologic process (human or animal (-medical - patients)Processing of biomedical signalsall treatment (of biomedical signals) which occurs between their origin in a physiological process and their interpretation

5、by their observer (e.g. clinician)Processing of biomedical signalsProcessing of biomedical signalsuProcessing of biomedical signals is application of signal processing methods on biomedical signalsuAll possible processing algorithms may be useduBiomedical signal processing requires understanding the

6、 needs (e.g. biomedical processes and clinical requirements) and selecting and applying suitable methods to meet these needsRationales for biomedical signal processing1.Acquisition and processing to extract a priori desired information2.Interpreting the nature of a physiological process, based eithe

7、r ona) observation of a signal (explorative nature), orb) observation of how the process alters the characteristics of a signal (monitoring a change of a predefined characteristic)(Some) goals for biomedical signal processingu Quantification and compensation for the effects of measuring devices and

8、noise on signalu Identification and separation of desired and unwanted components of a signalu Uncovering the nature of phenomena responsible for generating the signal on the basis of the analysis of the signal characteristicsu Related to modelling / inverse modelling but often more pragmaticExample

9、: heart rate metersSensorSignal processingUserExample: IST Vivago WristCareHealth monitoringNeed for processing todraw any conclusionsBeat-to-beat heart rateSystolic and diastolic blood pressureSignal processing methodsNoise reductionPreprocessingSignal validationFeature extractionData compressionSe

10、gmentationPattern recognitionTrend detectionEvent detectionDecision supportDecision makingFiltering (linear, nonlinear, adaptive, optimal)Statistical signal processingFrequency domain analysisTime-frequency analysisFuzzy logicArtificial neural networksExpert systems, rule-based systemsGenetic and ev

11、olutionary methodsSignal processing methodsSignal modellingWavelets and filter banksPCA, ICA, SVDClusteringHigher-order statisticsChaos and nonlinear dynamicsComplexity and fractals Choose right method for right problem!Biomedical signal classificationuOn the basis of signal characteristics: technic

12、al point of view signal source: from where and how the signal is originated and measured biomedical application: neurophysiology, cardiology, monitoring, diagnosis,uClassification may be helpful in the selection of processing methods.DefinitionsuDeterministic: may be accurately described mathematica

13、lly, Usually predictable (not in case of chaos!)uPeriodic: s(t)=s(t+nT)uAlmost periodic: patterns repeat with some unregularityuTransient: signal characteristics change with timeDefinitionsuStochastic: defined by their statistical properties (distribution)uStationary: statistical properties of the s

14、ignal do not change over timeuErgodic: statistical properties may be computed along time distributionsu(White noise: acf = 0 except for =0 where acf=1; flat spectrum)DefinitionsuAll real (bio)signals may be considered stochasticu almost deterministic signals (e.g. ECG): wave shapes that (almost) rep

15、eat themselves characterization (often) by detection of certain measures or wavesu “truly” stochastic (e.g. EEG) characterization by statistical propertiesClassification by sourceu biomedical signals differ from other signals only in terms of the application - signals that are used in the biomedical

16、 fieldu Bioelectric signals: generated by nerves cells and muscle cells. Single cell measurements (microelectrodes measure action potential) and gross measurements (surface electrodes measure action of many cells in the vicinity)Classification by sourceu Biomagnetic signals: brain, heart, lungs prod

17、uce extremely weak magnetic fields, this contains additional information to that obtained from bioelectric signals. Can be measured using SQUIDs.u Bioimpedance signals: tissue impedance reveals info about tissue composition, blood volume and distribution and more. Usually two electrodes to inject cu

18、rrent and two to measure voltage dropClassification by sourceu Bioacoustic signals: many phenomena create acoustic noise. For example, flow of blood through the heart, its valves, or vessels and flow of air through upper and lower airways and lungs, but also digestive tract, joints and contraction o

19、f muscles. Record using microphones.u Biomechanical signals: motion and displacement signals, pressure, tension and flow signals. A variety of measurements (not always simple, often invasive measurements are needed).Classification by sourceu Biochemical signals: chemical measurements from living tis

20、sue or samples analyzed in a laboratory. For examples, ion concentrations or partial pressures (pO2 or pCO2) in blood. (low frequency signals, often actually DC signals)u Biooptical signals: blood oxygenation by measuring transmitted and backscattered light from a tissue, estimation of heart output

21、by dye dilution. Fiberoptic technology.Biomedical application domainsu Information gathering measurement of phenomena to understand the systemu Diagnosis detection of malfunction, pathology, or abnormalityu Monitoring to obtain continuous or periodic information about the systemBiomedical applicatio

22、n domainsu Therapy and control modify the behaviour of the system and ensure the resultu Evaluation objective analysis: proof of performance, quality control, effect of treatmentProblems in biomedical signal processinguAccessibility Patient safety, preference for noninvasiveness Indirect measurement

23、s (variables of interest are not accessible)uVariance Inter-individual, intra-individualProblems in biomedical signal processinguInter-relationships and interactions among physiological system Subsystem of interest may not be isolateduAcquisition interference Instrumentation and procedures modify th

24、e system or its stateArtefacts and interferenceu Interference from other physiological systems (e.g. muscle artifacts in EEG recordings)u Low-level signals (e.g. microvolts in EEG) require very sensitive amplifiers; they are easily sensitive to interference, too!u Limited possibilities for shielding

25、 or other protection Nonlinearity and obscurity of the system under studyArtefacts and interferenceu basically all biological systems exhibit nonlinearities while most of the methods are based on the assumption of linearity approximationu exact structures and true function of many physiological syst

26、ems are often not knownSignal acquisitionShort-term HRV and BPV2022-2-1234Zhongguo Liu_Biomedical Engineering_Shandong Univ.signal processinguApplications of signal processing: entertainment, communications, space exploration, medicine, archaeology(考古學), etc. uDriven by the convergence of communicat

27、ions, computers and signal processing.2022-2-1235Zhongguo Liu_Biomedical Engineering_Shandong Univ.signal processinguSignal processing is benefited from a close coupling between theory, application, and technologies for implementing signal processing systems.uSignal processing is concerned with the

28、representation, transformation, and manipulation of signals and the information they contain.2022-2-1236Zhongguo Liu_Biomedical Engineering_Shandong Univ.Continuous and Digital Signal ProcessinguPrior to 1960: continuous-time analog signal processing.uDigital signal processing is caused by:uthe evol

29、ution of digital computers and microprocessorsuImportant theoretical developments such as the fast Fourier transform algorithm (FFT) 2022-2-1237Zhongguo Liu_Biomedical Engineering_Shandong Univ.Digital and Discrete-time Signal ProcessinguIn digital signal processinguSignals are represented by sequen

30、ces of finite-precision numbersuProcessing is implemented using digital computationuDigital signal processing is a special case of discrete-time signal processing2022-2-1238Zhongguo Liu_Biomedical Engineering_Shandong Univ.Digital and Discrete-time Signal ProcessinguContinuous-time signal processing

31、: time and signal are continuousuDiscrete-time signal processing: time is discrete, signal is continuousuDigital signal processing: time and signal are discrete2022-2-1239Zhongguo Liu_Biomedical Engineering_Shandong Univ.Discrete-time ProcessinguDiscrete-time processing of continuous-time signaluRea

32、l-time operation is often desirable: output is computed at the same rate at which the input is sampled2022-2-1240Zhongguo Liu_Biomedical Engineering_Shandong Univ.Objects of Signal ProcessinguProcess one signal to obtain another signaluSignal interpretation: Characterization of the input signal, uEx

33、ample: speech recognitiondigital preprocessing(filtering,parameter estimation,etc)speechsignal pattern recognitionexert systemphonemic transcriptionfinal signal interpretation2022-2-1241Zhongguo Liu_Biomedical Engineering_Shandong Univ.Objects of Signal ProcessinguSymbolic manipulation of signal pro

34、cessing expression: signal and systems are represented and manipulated as abstract data objects, without explicitly evaluating the data sequence2022-2-1242Zhongguo Liu_Biomedical Engineering_Shandong Univ.Why do We Learn DSPuSoftware, such as Matlab, has many tools for signal processinguIt seems tha

35、t it is not necessary to know the details of these algorithms, such as FFTuA good understanding of the concepts of algorithms and principles is essential for intelligent use of the signal processing software tools2022-2-1243Zhongguo Liu_Biomedical Engineering_Shandong Univ.ExtensionuMultidimensional

36、 signal processinguimage processinguSpectral AnalysisuSignal modelinguAdaptive signal processinguSpecialized filter designuSpecialized algorithm for evaluation of Fourier transformuSpecialized filter structureuMultirate signal processinguWalet transform2022-2-1244Zhongguo Liu_Biomedical Engineering_

37、Shandong Univ.Historical Perspectiveu17th centuryuThe invention of calculusuScientist developed models of physical phenomena in terms of functions of continuous variable and differential equationsuNumerical technique is used to solve these equationsuNewton used finite-difference methods which are sp

38、ecial cases of some discrete-time systems2022-2-1245Zhongguo Liu_Biomedical Engineering_Shandong Univ.Historical Perspectiveu18th centuryuMathematicians developed methods for numerical integration and interpolation of continuous functionsuGauss (1805)discovered the fundamental principle of the Fast

39、Fourier Transform (FFT) even before the publication(1822) of Fouriers treatise on harmonic series representation of function (proposed in 1807)2022-2-1246Zhongguo Liu_Biomedical Engineering_Shandong Univ.Historical PerspectiveuEarly 1950susignal processing was done with analog system, implemented wi

40、th electronics circuits or mechanical devices.first uses of digital computers in digital signal processing was in oil prospecting.uSimulate signal processing system on a digital computer before implementing it in analog hardware, ex. vocoder2022-2-1247Zhongguo Liu_Biomedical Engineering_Shandong Uni

41、v.Historical PerspectiveuWith flexibility the digital computer was used to approximate, or simulate, an analog signal processing systemuThe digital signal processing could not be done in real timeuSpeed, cost, and size are three of the important factors of the use of analog components.uSome digital flexible