biomedical Signal processing
生物医学信号处理
Chapter 1 Introduction
刘忠国Zhongguo Liu
Biomedical Engineering
School of Control Science and Engineering, Shandong University
2015/4/8
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Self Introduction
刘忠国：liuzhg@sdu.edu.cn
Tel: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 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)
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 -> 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)
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
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)
(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
Example: heart rate meters
Sensor
Signal processing
User
Example: IST Vivago® WristCare
Health monitoring
Systolic and diastolic blood pressure
Need for processing to
draw any conclusions
Beat-to-beat heart rate
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
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!
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...
Definitions
Deterministic: may be accurately described
mathematically, Usually predictable (not in
case of chaos!)
Periodic: s(t)=s(t+nT)
Almost periodic: patterns repeat with some
unregularity
Transient: signal characteristics change
with time
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 τ=0
where acf=1; flat spectrum)
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
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)
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
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).
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.
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
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
Problems in biomedical signal
processing
Accessibility
– Patient safety, preference for
noninvasiveness
– Indirect measurements (variables of
interest are not accessible)
Variance
– Inter-individual, intra-individual
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
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
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
Signal acquisition
Short-term HRV and BPV
signal processing
Applications of signal processing:
entertainment, communications, space
exploration, medicine, archaeology(考
古学), 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
speech digital preprocessing
signal (filtering,parameter
estimation,etc)
pattern
recognition
final signal
interpretation
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phonemic
transcription
exert
system
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
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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 discretetime 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 specialpurpose 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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