biomedical Signal processing ???????? Chapter 1 Introduction

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biomedical Signal processing????????Chapter 1
Introduction

• ???Zhongguo Liu
• Biomedical Engineering
• School of Control Science and Engineering,
  Shandong University

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Self Introduction
???liuzhg_at_sdu.edu.cn Tel[phone] cellphone18764
171197
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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 to which kind of problems each method is
  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 from a
  biologic system /originating from a physiologic
  process (human or animal (-medical -gt patients))
• Processing of biomedical signals
• all treatment (of biomedical signals) which
  occurs between their origin in a physiological
  process 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 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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Rationales for biomedical signal processing

• 1.Acquisition and processing to extract a priori
  desired 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 the
  characteristics of a signal (monitoring a change
  of a predefined characteristic)

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(Some) goals for biomedical signal processing

• Quantification and compensation for the effects
  of measuring devices and noise on signal
• Identification and separation of desired and
  unwanted components of a signal
• Uncovering the nature of phenomena responsible
  for generating the signal on the basis of the
  analysis of the signal characteristics
• Related to modelling / inverse modelling but
  often more pragmatic

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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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Signal processing methods

• Noise reduction
• Preprocessing
• Signal validation
• Feature extraction
• Data compression
• Segmentation
• Pattern recognition
• Trend detection
• Event detection
• Decision support
• Decision making

Filtering (linear, nonlinear, adaptive,
optimal) Statistical signal processing Frequency
domain analysis Time-frequency analysis Fuzzy
logic Artificial neural networks Expert systems,
rule-based systems Genetic and evolutionary
methods
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Signal processing methods
Signal modelling Wavelets 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 the selection of
  processing methods...

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Definitions

• Deterministic may be accurately described
  mathematically, Usually predictable (not in case
  of chaos!)
• Periodic s(t)s(tnT)
• Almost periodic patterns repeat with some
  unregularity
• Transient signal characteristics change with time

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Definitions

• Stochastic defined by their statistical
  properties (distribution)
• Stationary statistical properties of the signal
  do not change over time
• Ergodic statistical properties may be computed
  along time distributions
• (White noise acf 0 except for t0 where acf1
  flat spectrum)

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Definitions

• All real (bio)signals may be considered
  stochastic
• almost deterministic signals (e.g. ECG) wave
  shapes that (almost) repeat themselves ?
  characterization (often) by detection 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 other signals
  only in terms of the application - signals that
  are used in the biomedical field
• 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)

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Classification by source

• Biomagnetic signals brain, heart, lungs
  produce extremely weak magnetic fields, this
  contains additional information to that obtained
  from bioelectric signals. Can be measured using
  SQUIDs.
• Bioimpedance signals tissue impedance reveals
  info about tissue composition, blood volume and
  distribution and more. Usually two electrodes to
  inject current and two to measure voltage drop

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Classification by source

• 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 of muscles. Record using microphones.
• Biomechanical signals motion and displacement
  signals, pressure, tension and flow signals. A
  variety of measurements (not always simple, often
  invasive measurements are needed).

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Classification by source

• Biochemical signals chemical measurements from
  living tissue 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)
• Biooptical signals blood oxygenation by
  measuring transmitted and backscattered 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 to understand
  the system
• Diagnosis
• detection of malfunction, pathology, or
  abnormality
• Monitoring
• to obtain continuous or periodic information
  about the system

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Biomedical application domains

• Therapy and control
• modify the behaviour of the system and ensure
  the result
• Evaluation
• objective analysis proof of performance,
  quality control, effect of treatment

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Problems in biomedical signal processing

• Accessibility
• Patient safety, preference for
  noninvasiveness
• Indirect measurements (variables of interest
  are not accessible)
• Variance
• Inter-individual, intra-individual

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Problems in biomedical signal processing

• Inter-relationships and interactions among
  physiological system
• Subsystem of interest may not be isolated
• Acquisition interference
• Instrumentation and procedures modify the
  system or its state

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Artefacts and interference

• Interference from other physiological systems
  (e.g. muscle artifacts in EEG recordings)
• Low-level signals (e.g. microvolts in EEG)
  require very sensitive amplifiers they are
  easily sensitive to interference, too!
• Limited possibilities for shielding or other
  protection Nonlinearity and obscurity of the
  system under study

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Artefacts and interference

• basically all biological systems exhibit
  nonlinearities while most of the methods are
  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
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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.

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signal processing

• Signal processing is benefited from a close
  coupling between theory, application, and
  technologies for implementing signal processing
  systems.
• Signal processing is concerned 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
• 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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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
• Walet 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
• 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 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).