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BIEN 1100 midterm

ECGs, Data Acquisition, Bioinstrumentation

Bioinstrumentation Transduction and measurement of physiological quantities
Diagnostic converts information about living system into a form humans can perceive
Example of Diagnostic Instrument stethoscope
Therapeutic Used to arrest or control a physiological process
Example of therapeutic device pacemaker
Assistive Make up for diminished or lost function
Example of Assistive device Hearing Aid
Measurand physical quantity, property or condition being measured.
Examples of Measurands voltage, current, impedance, pressure, flow, displacement, temperature, chemistry
One Bioinstrumentation challenge finding a transduceable property of the event that distinguishes it from other events/substances
Transducers convert energy from one form to another
Transducer Considerations must not alter event being measured, minimally invasive, external power supply,amplitude linearity/ amplitude range, transducer output, adequate frequency response, non biologically reactive,
Signal Conditioner operates on signal produced by transducer
Operations of Signal Conditioner amplify, filter, impedance match (minimize loading reflection), digitize, compensate for undesirable transducer characteristics (non-linearity)
Output Objective Output or convey information in form that can be understood by human senses, reduce noise
Output Objective Types visual, auditory, tactile
Special Requirements for Implantable Devices power supply, size, computational capacity, biological reacitivity, communication with outside world
Constraints for Bioinstrumentation design measured quantities vary with time (nonstationary), patient to patient variation, complex interactions with physiological system (hard to control for), safety, corrosive environments
Examples of Automated Diagnosis Implantable devices, emergency personnel, monitoring outside of clinic, typically computer based (CAD).
Automated Detection and Diagnosis Challenge: Identify the unique features that separate one population (disease) from another. Reality- there are always trade-offs
Typical features of Automated Diagnosis amplitude (not good indicator), slopes (pretty good), time intervals (important), sequence of events, shapes (sequence of slopes, lines and curves).
Development of Automated Detection Schemes Training set- typical and atypical examples of population to be classified, set thresholds for measures
Test Set typical and atypical examples, tester blinded, schemes/algorithms evaluated for performance
Sensitivity when condition is truly present, can you detect it?
Specificity When you detect, are you correct?, is the condition really present?
Accuracy combination of specificity and sensitivity
True Positive Actual positive, Tested positive
False Positive Actually negative, tested positve Type I error/ misdiagnosis
True Negative Actually negative, tested negative
False Negative Actually positive, tested negative Type II error, missing the diagnosis)
Sensitivity Equation TP/(FN+TP)
Specificity Equation TN/(TN+FP)
Accuracy Equation (TP+TN)/(TP+FP+FN+TN)
digital signal Means voltage level is digitized, set number of data points in time
EMG electrical signal [voltage] that comes from muscle [myo] (electromyogram- used by surgeons, doctors, physical trainers, physical therapists to determine whether muscle is working (nerve conduction study)
action potential is when aceytocholine moves down charge of muscle
central Difference Formula: xi+1-xi-1 ---------- 2Δt
Trapezoid Rule {xi+xi+1}Δt/2
d(sin ax)/dx a cos ax
∫(cos ax)dx 1/a sin ax
Aliasing- acquiring data with incorrect frequency content where higher frequency shows up as lower frequency
Example of Aliasing 10Hz looks like 1Hz
Nyquist Criteria sample at least 2 times the highest frequency in the signal
Numerical Nyquist Criteria FN=2(highest frequency)
Amplitude Resolution n=number of bits,# of levels = 2n , Higher bits= Higher resolution
Gain the constant that the input signal is multiplied by by in order to amplify your output
Analog signal is continuous existing over infinite number of intervals over duration of data
Sampling Frequency Fs dictates time interval between data points, if too low digital signal will poorly approximate, aliasing may occur. If too high digital signal file may be excessively large
Fast Fourier Transform FFT indicate frequencies of different sine waves, also indicates power of frequency (how much influence the sine wave with that frequency has on the signal)
Resolution R/2n=Resolution
QRS Complex Ventricular Contraction, atrial repolarization
Systolic Blood Pressure Higher Number in mmHg, amount of force driving the flow of blood when the heart contracts, left ventricle to aorta, first whooshing, increases with exercise
Diastolic Blood Pressure- Lower Number, force that drives the flow of blood when heart is relaxed, whooshing ends
Heart Rate (Chart Speed*seconds/min)/ R-R Spacing
Normal electrical axis= 59° deviations help physicians diagnose problems. It can indicate blocks in electrical conduction of the heart, enlarged heart muscle and difficulties with high blood pressure.
Differences between rest and exercise? During exercise R-R spacing change, heart contracts more often, requires more blood, pumps more often, HR increases, intervals stay the same but frequencies of waveforms increase, no difference in electrical axis because it measures general direction
Maximum Voluntary Ventilation MVV- pulmonary function test that combines volume and flow rates to acess overall pulmonary ventilation
Created by: 600042666