ECGs, Data Acquisition, Bioinstrumentation
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| Bioinstrumentation | Transduction and measurement of physiological quantities
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| Diagnostic | converts information about living system into a form humans can perceive
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| Example of Diagnostic Instrument | stethoscope
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| Therapeutic | Used to arrest or control a physiological process
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| Example of therapeutic device | pacemaker
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| Assistive | Make up for diminished or lost function
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| Example of Assistive device | Hearing Aid
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| Measurand | physical quantity, property or condition being measured.
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| Examples of Measurands | voltage, current, impedance, pressure, flow, displacement, temperature, chemistry
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| One Bioinstrumentation challenge | finding a transduceable property of the event that distinguishes it from other events/substances
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| Transducers | convert energy from one form to another
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| 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,
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| Signal Conditioner | operates on signal produced by transducer
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| Operations of Signal Conditioner | amplify, filter, impedance match (minimize loading reflection), digitize, compensate for undesirable transducer characteristics (non-linearity)
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| Output Objective | Output or convey information in form that can be understood by human senses, reduce noise
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| Output Objective Types | visual, auditory, tactile
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| Special Requirements for Implantable Devices | power supply, size, computational capacity, biological reacitivity, communication with outside world
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| 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
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| Examples of Automated Diagnosis | Implantable devices, emergency personnel, monitoring outside of clinic, typically computer based (CAD).
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| Automated Detection and Diagnosis | Challenge: Identify the unique features that separate one population (disease) from another.
Reality- there are always trade-offs
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| 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).
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| Development of Automated Detection Schemes | Training set- typical and atypical examples of population to be classified, set thresholds for measures
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| Test Set | typical and atypical examples, tester blinded, schemes/algorithms evaluated for performance
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| Sensitivity | when condition is truly present, can you detect it?
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| Specificity | When you detect, are you correct?, is the condition really present?
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| Accuracy | combination of specificity and sensitivity
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| True Positive | Actual positive, Tested positive
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| False Positive | Actually negative, tested positve
Type I error/ misdiagnosis
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| True Negative | Actually negative, tested negative
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| False Negative | Actually positive, tested negative
Type II error, missing the diagnosis)
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| Sensitivity Equation | TP/(FN+TP)
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| Specificity Equation | TN/(TN+FP)
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| Accuracy Equation | (TP+TN)/(TP+FP+FN+TN)
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| digital signal | Means voltage level is digitized, set number of data points in time
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| 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)
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| action potential | is when aceytocholine moves down charge of muscle
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| central Difference Formula: | xi+1-xi-1
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2Δt
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| Trapezoid Rule | {xi+xi+1}Δt/2
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| d(sin ax)/dx | a cos ax
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| ∫(cos ax)dx | 1/a sin ax
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| Aliasing- | acquiring data with incorrect frequency content where higher frequency shows up as lower frequency
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| Example of Aliasing | 10Hz looks like 1Hz
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| Nyquist Criteria | sample at least 2 times the highest frequency in the signal
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| Numerical Nyquist Criteria | FN=2(highest frequency)
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| Amplitude Resolution | n=number of bits,# of levels = 2n , Higher bits= Higher resolution
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| Gain | the constant that the input signal is multiplied by by in order to amplify your output
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| Analog signal | is continuous existing over infinite number of intervals over duration of data
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| 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
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| 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)
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| Resolution | R/2n=Resolution
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| QRS Complex | Ventricular Contraction, atrial repolarization
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| 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
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| Diastolic Blood Pressure- | Lower Number, force that drives the flow of blood when heart is relaxed, whooshing ends
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| Heart Rate | (Chart Speed*seconds/min)/ R-R Spacing
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| 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.
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| 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
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| Maximum Voluntary Ventilation MVV- | pulmonary function test that combines volume and flow rates to acess overall pulmonary ventilation
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