BME 311: BIOMEDICAL INSTRUMENTATION ILecturer: Ali Işın

BME 311: BIOMEDICAL INSTRUMENTATION I Lecturer: Ali Işın

Lecture Note 1: Introduction to Instrumentation and

Measurement Systems

FACULTY OF ENGINEERING

DEPARTMENT OF BIOMEDICAL ENGINEERING

Medical devices

• Medical devices can be grouped according to the three areas of medicine:

• Diagnosis

– diagnostic devices

• Therapy

– therapeutic devices – application of energy

• Rehabilitation

– Application of Assisting orthotic-prosthetic devices

Diagnostic devices

• Types of diagnostic devices

– recording and monitoring devices – measurement and analysis devices – imaging devices

• importance of diagnostic devices

– enhance and extend the five human senses to improve to collect data from the patient for diagnosis

– the perception of the physician can be improved by diagnostic instrumentation in many ways:

• amplify human senses

• place the observer's senses in inaccessible environments

• provide new senses

Therapeutic devices

• Objective of therapeutic devices:

– deliver physical substances to the body to treat disease

• Physical substances:

– Voltage, current – Pressure

– Flow – Force

– Ultrasound

– Electromagnetic radiation – Heat

• Therapeutic device categories:

– devices used to treat disorders

– devices to assist or control the physiological functions

Assistive or rehabilitative devices

• Objective of rehabilitative devices

– to assist individuals with a disability

• The disability can be connected to the troubles to

– perform activities of daily living – limitations in mobility

– communications disorders and – sensory disabilities

• Types of rehabilitative devices

– Orthopedic devices

• An orthopedic device is an appliance that aids an existing function

– Prosthetic devices

• A prosthesis provides a substitute

Some characteristics of BMI

• methods and devices are used to solve medical problems

– problems are difficult, diverse, and complex

– solution alternatives are limited and specific to a certain problem

• Therefore we must know

– what we are measuring or studying – what we are treating

– which methodologies are available and applicable

Some characteristics of BMI

• deals with biological tissues, organs and organ systems and their properties and functions

• bio-phenomena:

– bioelectricity, biochemistry, biomechanics, biophysics

• requires their deep understanding and analysis

• Accessibility of data is limited,

• Interface between tissue and instrumentation is needed

• Procedures:

– non-invasive

– minimally invasive – invasive

Physiological measurements

• important application of medical devices

– physiological measurements and recordings

• important for biomedical engineer

– to understand the technology used in these recordings but also – the basic principles and methods of the physiological recordings

• medical fields where physiological recordings play an important role

– clinical physiology

– clinical neurophysiology – cardiology

– intensive care, surgery

important physiological parameters recorded

• parameters related to cardiovascular dynamics:

– blood pressure – blood flow

– blood volumes, cardiac output

• biopotentials:

– electrocardiogram (ECG), – electroencephalogram (EEG), – electromyogram (EMG)

• respiratory parameters:

– lung volumes and capacities, – air flow

• blood gases:

– pressures of blood gases – oxygen saturation

– pH and other ions

Biomedical Instrumentation

• Diagnosis and therapy depend heavily on the use of medical instrumentation.

• Medical procedures:

Medicine can be defined as a multistep

procedure on an individual by a physician,

group of physician, or an institute, repeated

until the symptoms disappear

The Importance of Biomedical Instrumentation

• Medical procedure

1 Collection of data - qualitative and/or quantitative

2 Analysis of data 3 Decision making

4 Treatment planning based on the decision

Biomedical Instrumentation System

• All biomedical instruments must interface with biological materials. That interface can be by direct contact or by indirect contact

Components of BM Instrumentation System…

• A sensor

– Detects biochemical, bioelectrical, or biophysical parameters

– Provides a safe interface with biological materials

• An actuator

– Delivers external agents via direct or indirect contact

– Controls biochemical, bioelectrical, or biophysical parameters

– Provides a safe interface with biologic materials

…Components of BM Instrumentation System…

• The electronics interface

– Matches electrical characteristics of the sensor/actuator with computation unit – Preserves signal to noise ratio of sensor – Preserves efficiency of actuator

– Preserves bandwidth (i.e., time response) of sensor/actuator

– Provides a safe interface with the sensor/actuator – Provides a safe interface with the computation unit

– Provides secondary signal processing functions for the system

…Components of BM Instrumentation System

• The computation unit

– provides primary user interface

– provides primary control for the overall system – provides data storage for the system

– provides primary signal processing functions for the system

– maintains safe operation of the overall system

Problems Encountered in Measuring a Living System

• Many crucial variables in living systems are inaccessible.

• Variables measured are seldom deterministic.

• Nearly all biomedical measurements depend on the energy.

• Operation of instruments in the medical

environment imposes important additional

constraints.

The scientific method…

• In the scientific method, a hypothesis is tested by experiment to determine its validity.

P r o b l e m s t a t e m e n t

R e v i e w p r i o r w o r k

S t a t e h y p o t h e s i s

P e r f o r m e x p e r i m e n t s

D e s i g n f u r t h e r e x p e r im e n t s

A n a l y z e d a t a

F i n a l c o n c l u s i o n s M o r e

e x p e r i m e n t s n e c e s s a r y

P r o b l e m s o lv e d

…The scientific method

• In the scientific method, a hypothesis is tested by experiment to determine its validity.

– For example, we might hypothesize that exercise

reduces high blood pressure yet experimentation and analysis are needed to support or refuse the

hypothesis.

• Experiments are normally performed multiple times. Then the results can be analyzed statistically to determine the probability that the results might have been produced by chance.

• Results are reported in scientific journals with enough detail so that others can replicate the experiment to confirm them.

Clinical diagnoses

• Physicians often need instrumentation to obtain data as part of the scientific method.

– For example, a physician obtaining the history of a patient with a complaint of poor vision would list diabetes as one possibility on a differential diagnosis.

C h i e f c o m p l a i n t

O b t a i n h i s t o r y

L i s t t h e d i f f e r e n t i a l

d i a g n o s i s

E x a m i n a t i o n a n d t e s t s

S e l e c t f u r t h e r t e s t s

U s e d a t a t o n a r r o w t h e

d i a g n o s i s

F i n a l d i a g n o s i s M o r e t h a n

o n e l i k e l y

O n l y o n e l i k e l y

T r e a t m e n t a n d e v a l u a t i o n

Feedback in measurement systems…

• Figure shows that the measurand is measured by a sensor converting the variable to an electrical signal, which can undergo signal processing. Sometimes the measurement system provides feedback through an effector to the subject.

S e n s o r

D a t a

c o m m u n i c a t i o n D a t a

d i s p l a y s

E f f e c t o r M e a s u r a n d

S i g n a l c o n d i t i o n i n g

S i g n a l p r o c e s s i n g

D a t a s t o r a g e F e e d b a c k

O u t p u t s

…Feedback in measurement systems

• Figure (a) shows that a patient reading an instrument usually lacks sufficient knowledge to achieve the correct diagnosis.

• Figure (b) shows that by adding the clinician to form an effective feedback system, the correct diagnosis and treatment result.

(a) (b)

I n s t r u m e n t P a t i e n t

I n s t r u m e n t P a t i e n t

C l i n i c i a n

…Feedback in measurement systems

• In certain circumstances, proper training of the patient and a well-designed instrument can lead to self-monitoring and self- control (one of the goals of bioinstrumentation).

– An example of such a situation is the day-to-day monitoring of glucose by people suffering from diabetes. Such an individual will contact a clinician if there is an alert from the monitoring instrument.

I n s t r u m e n t P a t i e n t

C l i n i c i a n

A b n o r m a l r e a d i n g s

Classifications of Biomedical Instruments

• The sensed quantity

• The principle of transduction

• The organ system for measurement

• The clinical medicine specialties

• Based on the activities involved in the medical care, medical instrumentation may be divided into three categories:

– Diagnostic devices – Therapeutic devices – Monitoring devices

Generalized Medical Instrumentation System…

…Generalized Medical Instrumentation System…

• Measurand

– Physical quantity, property, or condition that the system measures

• Biopotantial

• Pressure

• Flow

• Dimension (imaging)

• Displacement (velocity, acceleration, and force)

• Impedance

• Temperature

• Chemical concentrations

…Generalized Medical Instrumentation System…

Measurement Range Frequency, Hz Method

Blood flow 1 to 300 mL/s 0 to 20 Electromagnetic or ultrasonic Blood pressure 0 to 400 mmHg 0 to 50 Cuff or strain gage

Cardiac output 4 to 25 L/min 0 to 20 Fick, dye dilution Electrocardiography 0.5 to 4 mV 0.05 to 150 Skin electrodes Electroencephalography 5 to 300  V 0.5 to 150 Scalp electrodes Electromyography 0.1 to 5 mV 0 to 10000 Needle electrodes

Electroretinography 0 to 900  V 0 to 50 Contact lens electrodes

pH 3 to 13 pH units 0 to 1 pH electrode

pCO₂ 40 to 100 mmHg 0 to 2 pCO₂ electrode

pO₂ 30 to 100 mmHg 0 to 2 pO₂ electrode

Pneumotachography 0 to 600 L/min 0 to 40 Pneumotachometer Respiratory rate 2 to 50

breaths/min 0.1 to 10 Impedance

…Generalized Medical Instrumentation System…

• Sensor

– Converts a physical measurand to an electrical output

• Should respond only to the form of energy present in the measurand

• Should be minimally invasive (ideally noninvasive)

…Generalized Medical Instrumentation System…

• The specifications for a typical blood pressure sensor.

– Sensor specifications for blood pressure sensors are

determined by a committee composed of individuals from academia, industry, hospitals, and government

Specification Value

Pressure range –30 to +300 mmHg

Overpressure without damage –400 to +4000 mmHg

Maximum unbalance ±75 mmHg

Linearity and hysteresis ± 2% of reading or ± 1 mmHg

Risk current at 120 V 10 A

Defibrillator withstand 360 J into 50 

…Generalized Medical Instrumentation System…

• A hysteresis loop.

– The output curve obtained when increasing the

measurand is different

from the output obtained when decreasing the

measurand.

S e n s o r s i g n a l

M e a s u r a n d

…Generalized Medical Instrumentation System…

• (a) A low-sensitivity sensor has low gain. (b) A high sensitivity sensor has high gain.

(a) (b)

S e n s o r s i g n a l

M e a s u r a n d

S e n s o r s i g n a l

M e a s u r a n d

…Generalized Medical Instrumentation System…

• Most sensors are analog and provide a continuous range of amplitude values for output (a).

• Other sensors yield the digital output (b).

– Digital output has poorer resolution, but does not require conversion before being input to digital computers and is more immune to

interference

(a) (b)

T i m e

Amplitude

T i m e

Amplitude

…Generalized Medical Instrumentation System…

• Bioinstrumentation should be designed with a specific signal in mind.

– Table shows a few specifications for an electrocardiograph

– The values of the specifications, which have been agreed upon by a committee, are drawn from research, hospitals, industry, and government.

Specification Value

Input signal dynamic range ±5 mV

Dc offset voltage ±300 mV

Slew rate 320 mV/s

Frequency response 0.05 to 150 Hz

Input impedance at 10 Hz 2.5 M

Dc lead current 0.1 A

Return time after lead switch 1 s Overload voltage without damage 5000 V

…Generalized Medical Instrumentation System…

(a) An input signal which exceeds the dynamic range.

(b) The resulting amplified signal is saturated at 1 V.

(a)

(b)

…Generalized Medical Instrumentation System…

• DC offset voltage is the amount a signal may be moved from its baseline and still be amplified properly by the system.

Figure shows an input signal without (a) and with (b) offset.

(a)

A m p l i t u d e

D c o f f s e t (b)

…Generalized Medical Instrumentation System…

• The frequency response of a device is the range of frequencies of a measurand that it can handle.

• Frequency response is usually plotted as gain versus frequency.

• Figure shows Frequency response of the electrocardiograph.

0 . 0 5 H z 1 5 0 H z F r e q u e n c y

A m p l i t u d e 1 . 0

0 . 1

…Generalized Medical Instrumentation System…

• Linearity is highly desirable for simplifying signal processing (a)A linear system fits the equation y = mx + b.

(b)A nonlinear system does not fit a straight line.

(a) (b)

O u t p u t

I n p u t

O u t p u t

I n p u t

…Generalized Medical Instrumentation System…

• All bioinstrumentation observes the measurand either continuously or periodically. However, computer-based systems require periodic

measurements since by their nature, computers can only accept discrete numbers at discrete intervals of time.

(a) Continuous signals have values at every instant of time.

(b) Discrete-time signals are sampled periodically and do not provide values between these sampling times.

(a) T i m e (b)

Amplitude

T i m e

Amplitude

…Generalized Medical Instrumentation System…

• Signal conditioning

– Amplify, filter, match the impedance of the sensor to the display

– Convert analog signal to digital – Process the signal

…Generalized Medical Instrumentation System…

• Output display

– Results must be displayed in a form that the human operator can perceive

• Numerical, Graphical, Discrete or continuous, Permanent or temporary, Visual or acoustical

• Auxiliary elements

– Data storage

– Data transmission

– Control and feedback – Calibration signal

…Generalized Medical Instrumentation System…

• Panels and series

• Certain groups of measurements are often ordered together because they are very

commonly used or because they are related.

• This may occur even if the measurements are based on different principles or are taken with different sensors.

• Table in next slide is an example of one of these groups of measurements, which are called panels or series.

…Generalized Medical Instrumentation System…

• Complete blood count for a male subject.

Laboratory test Typical value

Hemoglobin 13.5 to 18 g/dL

Hematocrit 40 to 54%

Erythrocyte count 4.6 to 6.2  10⁶/ L

Leukocyte count 4500 to 11000/ L

Differential count

Neutrophil 35 to 71%

Band 0 to 6%

Lymphocyte 1 to 10%

Monocyte 1 to 10%

Eosinophil 0 to 4%

Basophil 0 to 2%

…Generalized Medical Instrumentation System

• Hemoglobin is the protein which caries oxygen in the bloodstream.

• Hematocrit is the percent of solid material in a given volume of blood after it has been centrifuged.

• An erythrocyte is a red blood cell.

• A leukocyte is a white blood cell.

– The differential count tells how many of each type of white blood cell there are in one microliter of blood.

– Unusual values for different leukocytes can be indicative of the immune system fighting off foreign bodies.

Errors in measurements…

• When we measure a variable, we seek to determine the true value, as shown in Figure (next slide) .

• This true value may be corrupted by a variety of errors. For example

– Movement of electrodes on the skin may cause an undesired added voltage called an artifact.

– Electric and magnetic fields from the power lines may couple into the wires and cause an undesired added voltage called interference

– Thermal voltages in the amplifier semiconductor junctions may cause an undesired added random voltage called noise. Temperature changes in the amplifier electronic components may cause undesired slow changes in

voltage called drif.

• We must evaluate each of these error sources to determine their size and what we can do to minimize them.

…Errors in measurements…

(a) Signals without noise are uncorrupted.

(b) Interference superimposed on signals causes error.

Frequency filters can be used to reduce noise and interference.

(a) (b)

…Errors in measurements…

(a) Original waveform.

(b) An interfering input may shift the baseline.

(c) A modifying input may change the gain.

(a) (b) (c)

Accuracy and precision…

• Resolution

– the smallest incremental quantity that can be reliably measured.

• a voltmeter with a larger number of digits has a higher resolution than one with fewer digits.

– However, high resolution does not imply high accuracy.

• Precision

– the quality of obtaining the same output from repeated

measurements from the same input under the same conditions.

– High resolution implies high precision.

• Repeatability

– the quality of obtaining the same output from repeated measurements from the same input over a period of time.

…Accuracy and precision…

• Data points with

(a) low precision and (b) high precision.

…Accuracy and precision…

• Accuracy

– Generally defined as the largest expected error between actual and ideal output signals.

– the difference between the true value and the measured value divided by the true value.

• Obtaining the highest possible precision,

repeatability, and accuracy is a major goal in bioinstrumentation design.

…Accuracy and precision…

• Data points with

(a) low accuracy and (b) high accuracy

Calibration…

• Measuring instruments should be calibrated against a standard that has an accuracy 3 to 10 times better than the desired calibration

accuracy.

• The accuracy of the standard should be traceable to the institutions regulating the

standards (National Institute of Standards and

Technology, TSI, etc.) .

Calibration…

• If the instrument is linear,

– its output can be set to zero for zero input. Then a one-point calibration defines the calibration curve that plots output versus input (next slide).

• If the linearity is unknown,

– a two-point calibration should be performed and these two points plus the zero point plotted to ensure linearity (next slide).

• If the resulting curve is nonlinear,

– many points should be measured and plotted to obtain the calibration curve.

• If the output cannot be set to zero for zero input,

– measurements should be performed at zero and full scale for linear instruments and at more points for nonlinear instruments.

• Calibration curves should be obtained at several expected temperatures to determine temperature drift of the zero point and the gain.

…Calibration

(a) The one-point calibration may miss nonlinearity.

(b) The two-point calibration may also miss nonlinearity.

(a) (b)

O u t p u t

I n p u t

O u t p u t

I n p u t

Statistics

• Mean

– If we make n measurements of x, for example of the weights of a population, we may wish to

report an estimate of them in a condensed way.

– The simplest statistic is the estimated sample mean

where i = 1, 2,…n.

n

x _  x

Statistics

• Standard Deviation

– A measure of the spread of data about the mean is the estimated sample standard deviation

• Used with the mean for symmetric distributions of numerical data.

1 )

(



   n

x

s x

Statistics

• Standard deviation of the mean (standard error of the mean (SEM))

– Expresses the variability to be expected among the means in future samples, whereas the

standard deviation describes the variability to be expected among individuals in future samples.

1





n s_x s

Gaussian Distribution…

• The spread (distribution) of data may be rectangular, skewed, Gaussian, or other.

• The Gaussian distribution is given by

where μ is the true mean and σ is the true standard deviation of a very large number of measurements.









) 2 (

) 2 /(

)

(  ^{2 2}

 e

X

f

…Gaussian Distribution

• For the normal distribution, 68% of the data lies within ±1 standard

deviation. By measuring samples and averaging, we obtain the estimated mean , which has a smaller standard deviation sx.  is the tail probability that xs does not differ from  by more than .

F r e q u e n c y

P o p u l a t io n s t a n d a r d d e v ia t i o n  E s t i m a t e d m e a n x_s

s t a n d a r d d e v ia t io n s _x

 

 +  x

 

M e a n

Poisson Probability…

• The Poisson probability density function is another type of distribution.

– It can describe, among other things, the probability of radioactive

decay events, cells flowing through a counter, or the incidence of light photons.

• The probability that a particular number of events K will occur in a measurement (or during a time) having an average

number of events m is

• The standard deviation of the Poisson distribution is

) ! ,

( K

m m e

K p

K

m



m

…Poisson Probability

• A typical Poisson distribution for m = 3.

0 . 2

0 . 1

0

0 1 2 3 4 5

p

K x

x

x x

x

x

Hypothesis testing…

• In hypothesis testing, there are two hypotheses.

– H0, the null hypothesis, is a hypothesis that assumes that the variable in the experiment will have no effect on the result

– Ha is the alternative hypothesis that states that the variable will affect the results.

• For any population, one of the two hypotheses must be true.

• The goal of hypothesis testing is to find out which hypothesis is true by sampling the population.

• In reality, H₀ is either true or false and we draw a conclusion from our tests of either true or false. This leads to four

possibilities (next slide)

…Hypothesis testing…

• The four outcomes of hypothesis testing.

Conclusion Real situation

H₀ true H_a true

Accept H₀ Correct decision Type II error, p = b Reject H₀ Type I error, p = a Correct decision

…Hypothesis testing…

• Equivalent table of the table given in previous slide for results relating to a condition or disease.

Test result Has condition?

No Yes

Negative True negative (TN) False negative (FN) Postitive False positive (FP) True positive (TP)

…Hypothesis testing…

• The terms in the Table in previous slide are

useful for defining measures that describe the proportion of, for example, a disease in a

population and the success of a test in identifying it.

• Incidence

– is the number of cases of a disease during a stated period, such as x cases per 1000 per year.

…Hypothesis testing…

• Prevalence

– the number of cases of a disease at a given time such as y cases per 1000.

• It is all diseased persons divided by all persons.

FP FN

TP TN

FN Prevalence TP







 

…Hypothesis testing…

• Sensitivity

– the probability of a positive test result when the disease is present.

– Among all diseased persons, it is the percent who test positive.

• Specificity

– the probability of a negative diagnostic result in the absence of the disease.

– Among all normal persons, it is the percent who test negative.

% FN100

TP y TP

Sensitivit

 

% FP100

TN y TN

Specificit

 

…Hypothesis testing…

• Considering only those who test positive, positive predictive value (PPV) is the ratio of patients who have the disease to all who test positive.

• Considering only those who test negative, negative predictive value (NPV) is the ratio of nondiseased patients to all who test negative.

% FP100

TP PPV TP

 

% FN100

TN NPV TN

 

…Hypothesis testing

• The test result threshold is set to minimize false positives and false negatives.

N o r m a l p o p u l a t i o n

D i s e a s e d p o p u l a t i o n T r u e

n e g a t i v e

F a l s e p o s i t i v e , p = 

T r u e p o s i t i v e T h r e s h o l d

F a l s e n e g a t i v e , p = b