Patent Publication Number: US-2003233204-A1

Title: Systems and methods for calibrating a distorted signal with another signal of known calibration

Description:
BACKGROUND OF THE INVENTION  
       [0001] 1. Field of the Invention  
       [0002] This invention relates to systems and methods for calibrating an uncalibrated measuring device.  
       [0003] 2. Description of Related Art  
       [0004] Sensors, or more generally, transducers, are used in many disciplines to sense a physical phenomenon and to generate an output signal based on the sensed physical phenomenon. Commonly, such sensors or transducers are calibrated based on known simultaneous measurements of the physical phenomenon. In practice, the output signal from the sensor or transducer is calibrated using a measurement of the physical phenomenon made with another instrument. In general, this second instrument has been calibrated itself by some agreed-upon method and against a specified physical object, physical phenomenon, and/or standard.  
       [0005] In some types of measurements, the sensor or transducer of a measurement device cannot be directly coupled to the physical phenomenon to be measured using that sensor or transducer. Such indirect measurements are dependent upon the coupling of the sensor or transducer to the system in which the physical phenomenon being measured occurs. In this case, calibrating the sensor or transducer is dependent both upon the measurement characteristics of the sensor or transducer and upon the coupling of the sensor or transducer to the system being measured. As a result, indirect measurements often require calibrating the sensor or transducer to additional measurements of the system after the sensor or transducer is connected to the system.  
       SUMMARY OF THE INVENTION  
       [0006] However, when attempting to calibrate a sensor or transducer that makes indirect measurements, it is often difficult, if not impossible, to make, at the same location on the system, both the calibration measurements using that sensor or transducer required to calibrate that sensor or transducer and to make the second set of measurements necessary for calibrating such indirect measurements. This typically, although not always, occurs due to the size of the sensors relative to the system being measured and the practical limitations when making measurements on the system being measured.  
       [0007] For example, due to the size of the sensor or transducer, the size of the secondary measurement device and/or the practical limitations of making blood pressure measurements on living beings, it is generally impossible to measure the blood pressure of a blood vessel within a living being using the sensor or transducer to be calibrated, while making the secondary measurements (discussed above) at the same point on the blood vessel using a second measurement device. For example, the known blood pressure measurements, which are used as the “additional measurements” for calibrating indirect measurements, are typically obtained from a human being by placing a cuff over the brachial artery of the upper arm. In contrast, a tonometric sensor to be calibrated is placed against the radial artery at the wrist of the human being. Moreover, the blood pressure cuff will very often be placed on an opposite limb from that on which the blood pressure of the human being is being measured using the tonometric sensor. However, it should be appreciated that this inability to make measurements at the same location relative to the system due to physical constraints is not restricted to measuring blood pressure in a living being.  
       [0008] It should also be appreciated that, if the calibration of the sensor or transducer is to be accurate, it is usually necessary that the sensor or transducer being calibrated be exposed to the identical level of the physical phenomenon as that to which the standard measurement device is exposed. If these physical phenomenon cannot be measured both by the sensor or transducer to be calibrated and by the standard measurement device at the same point and if the values of the physical phenomenon are not the same at the two measurement locations, an error in the calibration can result. The values of the physical phenomenon at the two measurement points can differ due to a time delay of the propagation of the physical phenomenon between the two measurement locations and/or due to a distortion of the physical phenomenon between the two measurement locations.  
       [0009] For example, the physical phenomenon of blood pressure in a vascular system of a living being experiences both of these error-inducing characteristics. That is, the propagation of the blood pressure pulse wave through the vascular system has a finite velocity. As a result, the blood pressure measured at a second, downstream location of the vascular system is delayed in time from the blood pressure that occurs at a first, upstream, measurement location of the vascular system. At the same time, as the blood pressure pulse wave propagates through the vascular system, the physiological characteristics of the vascular system of the living being produces distortions in the blood pressure pulse wave that makes the blood pressure pulse wave take different shapes at the first and second measurement locations.  
       [0010] This invention provides systems and methods for calibrating a sensor or transducer that is indirectly coupled to a phenomenon occurring within a system.  
       [0011] This invention separately provides systems and methods for calibrating sensors or transducers that are indirectly coupled to a system where the secondary measurement occurs at a location separated from the location of the measurements obtained by the sensor or transducer to be calibrated.  
       [0012] This invention separately provides systems and methods for calibrating a sensor or transducer relative to a physical phenomenon that is distorted relative to a measurement of that physical phenomenon by a device of known calibration.  
       [0013] This invention separately provides systems and methods for calibrating a sensor or transducer that is indirectly coupled to a physical phenomenon occurring within a living being.  
       [0014] This invention separately provides systems and methods for calibrating a blood pressure transducer that generates an electric signal from a blood pressure signal occurring within a living being.  
       [0015] This invention separately provides systems and methods for calibrating a blood pressure sensor or transducer that senses a blood pressure signal in a living being relative to a separate measurement of the blood pressure signal within the living being taken at a point separated from the location of the blood pressure sensor or transducer to be calibrated.  
       [0016] This invention separately provides systems and methods for determining the calibration parameters of an uncalibrated device in situ using frequency analysis of the naturally occurring variations of the system being sensed using the uncalibrated device.  
       [0017] In various exemplary embodiments of the systems and methods according to this invention, a first transfer function that defines the transformation of a physical phenomenon between a first location and a second location is defined. Next, a value of that transfer function at a particular frequency is determined. Independently, a second transfer function, defining the conversion of the input physical phenomenon to the output signal generated by the sensor or transducer to be calibrated in response to measuring that physical phenomenon, is defined. Additionally, a relationship between the first transfer function and the second transfer function is also defined. The relationship between these two transfer functions is the reciprocal of a calibration coefficient needed to calibrate the uncalibrated sensor or transducer. By obtaining a value for each of the two transfer functions at a particular time, a particular frequency or the like, the calibration coefficient can be obtained.  
       [0018] Independently, the output of the uncalibrated sensor or transducer is based on the calibration coefficient, the input physical phenomenon and a calibration constant. Because the calibration coefficient is known, and because the input and output signal values can be determined or derived, the calibration constant can be determined. By determining the calibration coefficient and calibration constant for the uncalibrated sensor or transducer in situ using frequency analysis of the naturally occurring variations of the system, inaccuracies occurring as a result of using existing time-domain calibration methods can be reduced, or ideally, eliminated.  
       [0019] These and other features and advantages of this invention are described in, or are apparent from, the following detailed description of various exemplary embodiments of the systems and methods according to this invention. 
     
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
     [0020] Various exemplary embodiments of this invention will be described in detail, with reference to the following figures, wherein:  
     [0021]FIG. 1 is a schematic diagram of one exemplary embodiment of an uncalibrated device that measures physical phenomenon;  
     [0022]FIG. 2 is a schematic diagram of one exemplary embodiment of a system usable to calibrate the uncalibrated device shown in FIG. 1;  
     [0023]FIG. 3 illustrates differences in the blood pressure signals and the time delays between different sites in a vascular system of a living being;  
     [0024]FIG. 4 is a graph plotting a signal generated by measuring a physical phenomenon at one position in a system and a second signal generated by measuring the physical phenomenon at a second position in the system, where the second signal is calibrated by matching the maximum and minimum of the second signal to the maximum and minimum of the first signal;  
     [0025]FIG. 5 is a schematic diagram of one exemplary embodiment of a physical system that converts a known physical phenomenon to an unknown physical phenomenon and an uncalibrated measurement device usable to measure the unknown physical phenomenon;  
     [0026]FIG. 6 is a graph plotting the magnitude of the brachial-to-radial transfer function as a function of frequency;  
     [0027]FIG. 7 is a schematic diagram of one exemplary embodiment of the uncalibrated measurement device based on an input signal/output signal transfer function between the input signals measured by the calibrated device and the output signals generated by the uncalibrated device;  
     [0028]FIG. 8 illustrates FIG. 7 redrawn for non-zero-frequency signal components;  
     [0029]FIG. 9 is a graph plotting a calibrated input signal measured at a first location of the system being measured, where the zero-frequency component has been removed;  
     [0030]FIG. 10 is a graph plotting the output signal of an uncalibrated measurement device measured at a second location of the system being measured, where the zero-frequency component has been removed;  
     [0031]FIG. 11 is a graph plotting the input signal/output signal transfer function, shown in FIGS. 7 and 8, based on the graphs shown in FIGS. 9 and 10;  
     [0032]FIG. 12 is a graph plotting the value of the input signal measured at a first location of the system using the calibrated device and the value of the input signal measured by the device at a second location, after calibration according to the systems and methods of this invention;  
     [0033]FIG. 13 is a graph plotting the estimated input signal/output signal transfer function obtained using a low-order ARX model;  
     [0034]FIG. 14 is a graph plotting pressure versus time for pressure sensed by a blood pressure measurement device and for arterial blood pressure pulses;  
     [0035]FIG. 15 is a flowchart outlining one exemplary embodiment of a method for calibrating an uncalibrated sensor or transducer according to this invention;  
     [0036]FIG. 16 is a flowchart outlining one exemplary embodiment of a method for ensuring the calibrated sensor device used in the flowchart of FIG. 15 is properly calibrated to the system;  
     [0037]FIG. 17 is a flowchart outlining one exemplary embodiment of a method for calibrating a blood pressure occlusion cuff relative to the brachial artery pressure in a living being and for using the calibrated occlusion cuff to calibrate an uncalibrated radial artery blood pressure sensor according to this invention; and  
     [0038]FIG. 18 is a block diagram of one exemplary system usable to calibrate an uncalibrated sensor or transducer according to this invention. 
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS  
     [0039] As described herein, this invention provides systems and methods for calibrating a first signal obtained at a first location relative to a system with a second signal of known calibration obtained at a second location relative to the system when the phenomenon being measured has different characteristics at the two locations. Moreover, this invention provides systems and methods that adjust the coefficients of the calibration equation such that distortions in the phenomenon between the first and second locations are compensated for. These distortions may make simultaneously-measured values in each signal different due to the differences in the physical phenomenon that occurs at the first and second measurement locations.  
     [0040] It should be appreciated that, in the following detailed discussion, the systems and methods according to this invention may be described relative to calibrating an arterial pressure tonometer using a calibrated non-invasive blood pressure monitor employing an air-filled occlusion cuff. However, it should be appreciated that the systems and methods of this invention are not limited to calibrating an uncalibrated arterial pressure tonometer relative to a previously-calibrated occlusion cuff blood pressure monitor. Rather, the systems and methods of this invention can be used to calibrate any uncalibrated sensor or transducer to a system to be measured, when a calibrated device used in the calibration process measures the system at a location separate from the location of the system measured by the uncalibrated sensor or transducer.  
     [0041] A signal generated by measuring some phenomenon of a system using a first device is generally calibrated by comparing signals representing simultaneous measurements of the phenomenon by the first device and a second, previously-calibrated device. In practice, an uncalibrated device is usually calibrated by measuring a physical phenomenon with a calibrated measuring device that has been calibrated by some agreed-upon method and against a specified standard to generate a calibrated signal. The calibrated signal of the phenomenon generated by the calibrated device is then compared to an uncalibrated signal measuring the same phenomenon generated by an uncalibrated measuring device.  
     [0042] In some types of measurements, the sensor or transducer of the measuring device, whether calibrated or uncalibrated, cannot directly measure the phenomenon to be measured. For example, the transmural pressure changes of a blood vessel may not be directly measured by measuring a pressure across the walls of a blood vessel. Instead, an indirect measurement may be used to measure a blood pressure pulse wave that is transmitted from the blood vessel to the surface of the skin that overlies the blood vessel. For indirect measurements, calibrating an uncalibrated device depends both on the measurement characteristics of the sensor or transducer of the measuring device and on the coupling of the sensor or transducer of the measuring device to the phenomenon being measured. As a result, indirect measurements often require the uncalibrated measuring device be calibrated to a calibrated signal after the uncalibrated measuring device is indirectly coupled to the phenomenon being measured.  
     [0043] A calibration process may include simultaneously measuring the phenomenon using an uncalibrated measuring device and using a calibrated measuring device to obtain two or more generally simultaneous values of the phenomenon for the two devices. It should be appreciated that “generally simultaneous” encompasses both measurements that are roughly contemporaneous, such that the same input signal, within the propagation delay, is measured at each of the two locations. However, this term also encompasses measurements taken at two different times, which could be separated by minutes, hours, or even days, so long as a number of conditions are met.  
     [0044] In particular, the two measurements can be separated by any interval so long as the system which is being measured has not significantly changed between the two measurements. For example, many real-life dynamic mechanical, hydraulic, pneumatic, electrical, and chemical systems experience aging, wear and the like that cause the long-term dynamic response of such systems to change and/or drift. Such systems can also experience inputs that cause long-term changes to the dynamic response of such systems. That is, such systems are generally stable over some limited time interval, but change over longer intervals. It should be appreciated that, as long as the two measurements are made within the time interval over which the system being measured is stable, those two measurements are “roughly simultaneous”.  
     [0045] A mathematical relationship may then be produced that relates the measured signal levels obtained by the uncalibrated measuring device to the measured signal levels obtained by the calibrated measuring device. For an uncalibrated measuring device that transduces an input signal into an output signal by scaling and shifting, this transduction can be represented as:  
       O=KI+O   0,   (1)  
     [0046] where:  
     [0047] O is the resultant output signal;  
     [0048] K is a constant of proportionality;  
     [0049] I is the input signal; and  
     [0050] O 0  is a constant equal to the output signal for an input signal having a value of zero.  
     [0051]FIG. 1 is a schematic diagram that implements Eq. (1) for an input signal I representing a pressure and an output signal O representing a voltage. In various exemplary embodiments, the input signal is, for example, a blood pressure within a blood vessel, while the output signal is a voltage representing the force or pressure applied against a pressure-to-voltage sensor or transducer by the skin of a living being in response to the blood pressure in the blood vessel.  
     [0052] A linear equation can be used to calibrate such an uncalibrated measuring device. One such linear equation is:  
       I=C   c   O+I   0,   (2)  
     [0053] where:  
     [0054] I is the input signal against which the output signal is to be calibrated;  
     [0055] C C  is a calibration coefficient;  
     [0056] O is the output signal to be calibrated to the input signal; and  
     [0057] I 0  is a calibration constant.  
     [0058] As shown in FIG. 2, which corresponds to Eq. ( 2 ), in the case of a pressure-to-voltage transducer, the calibration constant I 0  is an offset pressure P 0 . In various exemplary embodiments, where the input signal is the blood pressure P within a blood vessel, the value of the offset pressure P 0 , is the blood pressure when the value of a measured electrical signal E is zero.  
     [0059] The input signal can be measured using a calibrated device that is able to monitor the input signal. The output signal can be generated by an uncalibrated transducer that converts the input signal to the output signal. In this case, if simultaneous measurements of the input signal and the output signal are made for two different values of the input signal, then the calibration coefficient Cc is:  
                 C   C     =       1   K     =         I   B     -     I   A           O   B     -     O   A             ,           (   3   )                       
 
     [0060] where:  
     [0061] K is the constant of proportionality (from Eq. (1));  
     [0062] I A  is a first calibrated input measurement;  
     [0063] I B  is a second calibrated input measurement;  
     [0064] O A  is a first uncalibrated output measurement corresponding to the first calibrated input measurement I A ; and  
     [0065] O B  is a second uncalibrated output measurement corresponding to the second calibrated input measurement I B .  
     [0066] The calibration constant I 0  can then be determined by using the calibration coefficient C C  and the measured input signals and the measured output signals. In particular, given the value for the calibration coefficient C c , determined using Eq. (3) and the values for the input signal and the output signal used in Eq. (3), Eq. (2) can be rewritten as:  
       I   0   =I   A   −C   C   O   A =I B   −C   C   OB   (4)  
     [0067] In some cases, when a coupling of the uncalibrated measuring device to the system cannot be readily adjusted, the uncalibrated measuring device must be calibrated after it is coupled to that system. In such systems that measure a time-varying phenomenon, such as, for example, blood pressure, the time variation in the measured phenomenon can be used for calibration purposes. The input signal obtained by sensing a time-varying phenomenon, such as blood pressure, includes a constant average component and a time-varying component. Thus, the total measured value of the input signal is:  
       I ( t )= {overscore (I)}+i ( t ),  (5)  
     [0068] where:  
     [0069] I(t) is the total measured value of the input signal;  
     [0070] {overscore (I)} is the constant average component of the input signal; and  
     [0071] i(t) is the time-varying component of the input signal.  
     [0072] For a transducer that converts an input signal into an output signal, a time-varying output signal generated in response to a time-vary input signal can also be represented by a constant average output component and a time-varying output component. Accordingly, based on Eq. (5), Eq. (2) can be rewritten as:  
       I ( t )= C   c   O ( t )+ I   0   =C   c   {overscore (O)}+C   c   o ( t )+ I   0 .  (6)  
     [0073] where:  
     [0074] I(t) is the total measured value of the input signal;  
     [0075] C C  is the calibration coefficient;  
     [0076] O(t) is the total measured value of the output signal;  
     [0077] {overscore (O)} is the constant average component of the output signal; and  
     [0078] o(t) is the time-varying component of the output signal.  
     [0079] If I(t) and O(t) are periodic functions with equal periods, then the constant average component of the input signal is:  
                 I   _     =       1   T            ∫     t   o         t   o     +   kT              I        (   t   )                          t             ,           (   7   )                       
 
     [0080] and the constant average component of the output signal is:  
                 O   _     =       1   T            ∫     t   o         t   o     +   kT              O        (   t   )                          t             ,           (   8   )                       
 
     [0081] where:  
     [0082] t 0  is an arbitrary time;  
     [0083] k is a non-zero integer; and  
     [0084] T is the period of the time varying periodic functions I(t) and O(t).  
     [0085] Eq. (6) can be used in two ways to calibrate the output signal in terms of the input signal. First, two time-varying input signals I 1 (t) and I 2 (t), and the corresponding time-varying output signals O 1 (t) and O 2 (t), can be measured over different time periods. Then, provided that the two time-varying input signals I 1 (t) and I 2 (t) have different values for the average component, the calibration coefficient C C  is:  
                 C   C     =           I   _     2     -       I   _     1             O   _     2     -       O   _     1           ,           (   9   )                       
 
     [0086] where:  
     [0087] {overscore (I)} 1  is the constant average component of the first time-varying input signal I 1 (t);  
     [0088] {overscore (I)} 2  is the constant average component of the second time-varying input signal I 2 (t) and ({overscore (I)} 1 ≠{overscore (I)} 2 );  
     [0089] {overscore (O)} 1  is the constant average component of the first output signal O 1 (t) obtained based on the first input signal I 1 (t); and  
     [0090] {overscore (O)} 2  is the constant average component of the second output signal O 2 (t) obtained based on the first input signal I 2 (t) and {overscore (O)} 1 ≠{overscore (O)} 2 .  
     [0091] Alternatively, the time-varying input signal I(t) and the corresponding output signal O(t) can be measured at two distinct times t 1  and t 2 . Then, provided that the time-varying output signal O(t) has different values for the time-varying component o(t) at the two times t 1  and t 2 , the calibration coefficient C C  is:  
                 C   C     =           I        (     t   2     )       -     I        (     t   1     )             O        (     t   2     )       -     O        (     t   1     )           =           I   _     +     i        (     t   2     )       -     I   _     -     i        (     t   1     )             O   _     +     o        (     t   2     )       -     O   _     -     o        (     t   1     )           =         i        (     t   2     )       -     i        (     t   1     )             o        (     t   2     )       -     o        (     t   1     )                 ,           (   10   )                       
 
     [0092] where:  
     [0093] I(t 1 ) and I(t 2 ) are the values of the time-varying input signal I(t) at the first and second times t 1  and t 2 ;  
     [0094] O(t 1 ) and O(t 2 ) are the values of the time-varying output signal O(t) at the first and second times t 1  and t 2 ;  
     [0095] {overscore (I)} is the constant average component of the time-varying input signal I(t);  
     [0096] {overscore (O)} is the constant average component of the electrical signal O(t) obtained based on the input signal I(t);  
     [0097] i(t 1 ) and i(t 2 ) are the values of the time-varying component of the time-varying input signal I(t) at the first and second times t 1  and t 2 ; and  
     [0098] o(t 1 ) and o(t 2 ) are the values of the time-varying component of the time-varying output signal O(t) at the first and second times t 1  and t 2  and o(t 2 )≠o(t 1 ).  
     [0099] Sometimes, it is not possible to measure the value of a phenomenon using a calibrated device at the same location where an uncalibrated sensor or transducer, that converts the value of a phenomenon to an output signal, is placed. For example, it is often impossible to measure the blood pressure of a living being using a calibrated blood pressure monitor at the same anatomical site on the living being where an electrical transducer is placed that converts the sensed blood pressure to an electrical signal. Typically, the calibrated blood pressure measurement is made using an air-filled occlusive cuff placed over the brachial artery of a forelimb of the living being, while an uncalibrated tonometer, which produces electrical signals in response to pressure changes, may be placed over the radial artery of the living being near the end of that forelimb of the living being. Often, the blood pressure cuff may be placed on a forelimb opposite to the forelimb on which the tonometer is placed.  
     [0100] Desirably, if the calibration is to be accurate, the uncalibrated device should measure the same physical phenomenon as that measured by the calibrated measuring device. When the calibrated device and the uncalibrated device are placed at two spatially separated locations, such as when the uncalibrated blood pressure measuring device and the calibrated blood pressure measuring device cannot make measurements at the same anatomical site, an error in calibration can result if the phenomenon being sensed, such as the blood pressure, is not the same at the two locations. The two measures of the phenomenon will differ if there is a time delay between the occurrence of the phenomenon between the two locations or there exists a distortion of the phenomenon that occurs at the location of the uncalibrated device with respect to phenomenon that occurs at the location of the calibrated device, or both.  
     [0101] For example, as shown in FIG. 3, in a living being, the blood flows from the heart to the smaller arterial branches of the living being&#39;s vascular system. The blood pressure pulse wave, originating due to the pulsatile contractions of the heart, has a finite propagation velocity through the vascular system. Thus, the waveform of the blood pressure pulse wave sensed at the radial artery is delayed relative to the blood pressure pulse wave sensed at the brachial artery. Additionally, as shown in FIG. 3, as the blood pressure pulse wave propagates through the arterial branches, a distortion in the pulse waveform occurs that makes the shapes of the blood pressure pulse waveforms different when measured at the brachial artery and at the radial artery.  
     [0102] When the blood pressure of the living being is measured at other locations or sites of the living being, the distortion in the shape of the pulse waveform locations may be negligible or non-existent. The blood pressure pulse waveforms may then be assumed to be the same shape at these locations, except for a possible time delay. However, any time delay precludes using pressure measurements P(t 1 ) and P(t 2 ) and the obtained electrical signal measurements E(t 1 ) and E(t 2 ) as the input and output signals I(t) and O(t), respectively, to determine the calibration coefficient using Eq. (10). When there is a time delay between measuring locations in the phenomenon as it occurs at those measuring locations, the time-varying signals may be shifted relative to each other, such that characteristic features, for example, maxima and minima, of the time-varying signal may be used as values by which a calibration coefficient maybe obtained using Eq. (10).  
     [0103] A tonometer placed over the radial artery of a living being has been calibrated assuming that the distortion is negligible. FIG. 4 shows a brachial artery blood pressure pulse waveform, shown by the solid line, measured by a calibrated blood pressure monitor. The radial artery blood pressure pulse waveform is shown by the dotted line and was measured by a tonometer that is calibrated by matching the maximum and the minimum of the two waveforms. However, the obvious difference in the shapes of the two waveforms of FIG. 4 demonstrates that the distortion measured at the radial artery is not negligible.  
     [0104] As shown in FIG. 3, the amplitude of the time-varying (AC) components of the blood pressure signal at the radial artery differs greatly from that the brachial artery due to the pulse transmission characteristics of the vascular system. As shown in FIG. 4, distortion, that is, a change in shape of the blood pressure signal at the radial artery relative to the brachial artery, is characteristic of the blood pressure pulse transmission. A technique that matches features of the two blood pressure signals, such as, peak amplitudes, which are used in the calibration process shown in FIG. 4, is not sufficient for accurate calibration.  
     [0105] The changes in a physical phenomenon between a first measurement location “a” of a system and a second measurement location “b” of the system can be described by a frequency-domain transfer function Ĥ(f):  
                     H   ^     ab          (   f   )       =           I   ^     2          (   f   )             I   ^     1          (   f   )           ,           (   11   )                       
 
     [0106] where:  
     [0107] Ĥ ab (f) is the frequency-domain transfer function of the input signal between the first measurement location “a” and the second measurement location “b”;  
     [0108] Î 1 (f) is the Fourier transform of the first time-varying input signal I 1 (t) measured at the first measurement location “a”; and  
     [0109] Î 2 (f) is the Fourier transform of the second time-varying input signal I 2 (t) measured at the second measurement location “b”.  
     [0110] Eq. (11) can be reformulated to represent the changes in a blood pressure pulse wave of a living being as the blood pressure pulse wave travels from a brachial artery of the living being to a radial artery of the living being. In particular, the changes can be described in terms of an arterial pressure transfer function:  
                     H   ^     br          (   f   )       =           P   ^     b          (   f   )             P   ^     r          (   f   )           ,           (     11      a     )                       
 
     [0111] where:  
     [0112] Ĥ br (f) is the arterial pressure transfer function of the input signal between the brachial artery measurement location and the radial artery measurement location  
     [0113] {circumflex over (P)} b (f) is the Fourier transform of the brachial time-varying blood pressure pulse wave P b (t) at the brachial artery measurement location; and  
     [0114] {circumflex over (P)} r (f) is the Fourier transform of the radial time-varying blood pressure pulse wave P r (t) at the radial artery measurement location.  
     [0115]FIG. 5 shows a schematic diagram of one exemplary embodiment of a physical system that converts a known physical phenomenon to an unknown physical phenomenon and an uncalibrated device that can be used to measure the unknown physical phenomenon. In particular, in FIG. 5, a blood pressure pulse wave is used as the physical phenomenon. The known physical phenomenon is the brachial blood pressure pulse wave P b (t) that can be measured by a calibrated measurement device, such as an occlusion cuff. The brachial artery-to-radial artery transfer function Ĥ br (f), as shown in Eq. (11a), converts the known brachial blood pressure pulse wave P b (t) to the unknown radial blood pressure pulse wave P r (t). The uncalibrated measurement device converts the unknown radial blood pressure pulse wave P r (t) to a uncalibrated measurement signal E r (t) based on an unknown proportionally constant K and an unknown calibration offset E 0 , as defined using Eq. (1).  
     [0116] It should be appreciated that determining the calibration coefficient C C  and the calibration constant I 0  of Eq. (2) is impossible until at least one frequency value for the transfer function Ĥ ab (f) between the first and second measurement locations, such as the brachial-radial blood pressure transfer function Ĥ br (f), is known.  
     [0117]FIG. 6 shows an experimentally-determined frequency response of the brachial-radial blood pressure transfer function Ĥ br (f) between the brachial artery blood pressure pulse wave and the radial artery blood pressure pulse wave. As shown in FIG. 6, the brachial-radial blood pressure transfer function Ĥ br (f) reveals that the propagation characteristics of the blood pressure pulse wave are those of a resonant system. In a resonant system, the frequency components of the blood pressure pulse wave near the resonance frequency, that is, near the peak of the brachial-radial blood pressure transfer function {fraction (H)} br (f), are amplified relative to those frequency components at other frequencies. The brachial-radial blood pressure transfer function Ĥ br (f) shown in FIG. 6 illustrates the distortion of various frequency components of the blood pressure pulse wave measured at the radial artery measurement location relative to the frequency components of the blood pressure pulse wave measured at the brachial artery measurement location.  
     [0118] Additionally, the brachial-radial blood pressure transfer function Ĥ br (f) shown in FIG. 6 depicts a useful characteristic of the system. This useful characteristic is that the brachial-radial blood pressure transfer function Ĥ br (f) approaches a value of one as the frequency approaches zero, which may be described by the equations:  
                   H   ^     br          (   0   )       =             P   ^     r          (   0   )             P   ^     b          (   0   )         =           P   _     r         P   _     b       =       1                 or                     P   _     b       ≈       P   _     r                   (   12   )                       
 
     [0119] where:  
     [0120] Ĥ br (0) is the value of the brachial-radial blood pressure transfer function H a (f) for a frequency of zero;  
     [0121] {circumflex over (P)} b (0) is the value of the Fourier transform of the time-varying brachial artery blood pressure pulse wave signal P b (t) for a frequency of zero;  
     [0122] {overscore (P)} b  is the mean value of P b (t);  
     [0123] {circumflex over (P)} r (0) is the value of the Fourier transform of the time-varying radial brachial artery blood pressure pulse wave signal P r (t) for a frequency of zero; and  
     [0124] {overscore (P)} r  is the mean value of P r (t).  
     [0125] In essence, Eqs. (11a) and (12) mean that the average blood pressures or DC components of the radial and brachial arterial blood pressure pulse waves are equal. This statement is valid for the large arteries of the body, such as the brachial and radial arteries, used in an exemplary embodiment of the systems and methods according to this invention. Although the brachial-radial blood pressure transfer function Ĥ br (f) is generally unknown, Eq. (12) provides, for many cases, the amplitude values of frequency components of a blood pressure signal approaching zero frequency when a blood pressure pulse is transmitted a distance through the vascular system. This information is used by various exemplary embodiments of the systems and methods according to this invention to calibrate an uncalibrated blood pressure measuring device.  
     [0126] More generally, if the general transfer function Ĥ ab (f) has one or more frequency components (f 1 , f 2 , . . . ) having a known or determinable relationship, such as that shown in FIG. 6 for the zero-frequency components of the system shown in FIG. 5 for the brachial-radial blood pressure pulse wave transfer function Ĥ br (f), then Eq. (12) can be used for those one or more frequency components (f 1 , f 2 , . . . ) to determine the amplitude values for those one or more frequency components (f 1 , f 2 , . . . ) of the transfer function Ĥ ab (f).  
     [0127] An input signal-to-output signal transfer function, ĤIO(f)=KĤ ab (f), can be defined for the input signal measured by a calibrated measuring device located at the first measurement location “a” and an output signal generated by an uncalibrated transducer located at the second measurement location “b”.  
     [0128] For the one or more frequency components, such as an “f 1 ” frequency component, of the input and output signals for which the transfer function Ĥ ab (f) has a known or determinable value, the value of the input signal-to-output signal transfer function Ĥ IO (f 1 ) is:  
       Ĥ   IO ( f   1 )= KĤ   ab ( f   1 ).  (13)  
     [0129] where:  
     [0130] Ĥ IO (f 1 ) is the value of the transfer function Ĥ IO (f) for the frequency fi;  
     [0131] K is the constant of proportionality (from Eq. (1)); and  
     [0132] Ĥ ab (f 1 ) is the value of the transfer function Ĥ ab (f) for the frequency of fi.  
     [0133] For the system shown in FIG. 5, the input signal-to-output signal transfer function Ĥ IO (f) is a pressure-to-voltage signal transfer function Ĥ pv (f) As indicated above, the first location-to-second location transfer function Ĥ ab (f) for this system is the brachial-radial blood pressure transfer function Ĥ br (f). As illustrated in FIG. 6 and indicated by Eq. (12), {overscore (P)} r ={overscore (P)} b . Thus, Eq. (13) can be rewritten for the DC components of the brachial artery blood pressure pulse wave and the generated electrical signal as:  
       Ĥ   pv (0)= KĤ   br (0)= K.   (13a)  
     [0134] where:  
     [0135] Ĥ pv (0) is the value of the pressure-to-voltage transfer function Ĥ pv (f) for a frequency of zero;  
     [0136] K is the constant of proportionality (from Eq. (1)); and  
     [0137] Ĥ br (0) is the value of the brachial-radial blood pressure transfer function Ĥ br (f) for a frequency of zero.  
     [0138] If the technique used for estimating the input signal-to-output signal transfer function Ĥ IO (f) does not estimate a phase angle, then the sign of K must be assigned from knowledge of the data. The value of K will be positive in most cases because the measured output signal O 2 (t) will be in phase with the calibrated input signal I 1 (t). Thus, when O 2 (t) and I 1 (t) are in phase:  
             K   =                  H   ^     IO          (     f   1     )                       H   ^     AB          (     f   1     )              .             (   14   )                       
 
     [0139] When O 2 (t) is inverted relative to the calibrated input signal I 1 (t), K is negative. That is:  
             K   =                  H   ^     IO          (     f   1     )                       H   ^     AB          (     f   1     )              .             (   15   )                       
 
     [0140] Since O 2 (t) depends on a time-varying component, O 2 (t), and an unknown zero frequency component, O 0 , determining Ĥ IO (f 1 ) for the frequency f, that has known or determinable values is impossible from the signals O 2 (t) and I 1 (t). However, if Ĥ IO (f) is a well-behaved function of frequency, it is possible to take advantage of that fact. The input signal-to-output signal transfer function Ĥ IO (f) can be experimentally determined for frequencies approaching the one or more of the frequency components (f 1 , f 2 , . . . ) that have known or determinable values. For example, for the pressure-to-voltage transfer function Ĥ pv (f), the values for this transfer function Ĥ pv (f) can be experimentally determined for frequencies approaching zero. For the input signal-to-output signal transfer function Ĥ IO (f), the f 1  frequency value of the input signal-to-output signal transfer function Ĥ IO (f 1 ) can be estimated from determining the frequencies approaching f 1 . For example, for the pressure-to-voltage transfer function Ĥ pv (f), the zero frequency value can be estimated from this determination. For the pressure-to-voltage transfer function Ĥ pv (f) or for any other transfer function that uses the zero-frequency component, the addition of O 0  has no effect on the AC components of the signal. Therefore, FIG. 8 shows FIG. 7 redrawn for AC-only analysis.  
     [0141] Determining the input signal-to-output signal Ĥ IO (f) from O 2 (t) and I 1 (t) is a system identification problem that may be addressed by various techniques. Once K is determined from the absolute value of the estimate of the input signal-to-output signal Ĥ IO (f) at f≈f 1 , the calibration coefficient C C  is equal to the reciprocal of K, as given in Eq. (3).  
     [0142] Having obtained the calibration coefficient C c , the calibration constant of the offset input signal value I 0  can be obtained by using estimates of the average values of the measured input signals, as described below. For transfer functions, such as the transfer function Ĥ pv (f), for which Eq. (12) holds, Eq. (12) may be rewritten and extended as:  
     I 1 =I 2 .  (16)  
     [0143] Similarly, Eq. 2 can be rewritten in view of I 2  and O 2  as:  
       I   2   =C   c   O   2   +I   0 .  (17)  
     [0144] Combining Eqs. (16) and (17) and solving for I 0  yields:  
       I   0   ={overscore (I)}   1   −C   C   O   2 .  (18)  
     [0145] Thus, for transfer functions, such as the transfer function Ĥ pv (f), for which Eq. (12) holds, the calibration constant of the offset input signal value I 0  can be determined from the derived calibration coefficient C C  and the average values of the input signals {overscore (I)} 1  and {overscore (I)} 2  used to determine the calibration coefficient C C .  
     [0146] In general, determining the calibration coefficient C C  based on Eqs. (13)-(17) is most easily used with experimental data that characterizes the input signal-to-output signal transfer function Ĥ IO (f) for frequencies approaching zero. There are techniques for characterizing transfer functions from a sample of input and output data. Generally, such techniques are either parametric or non-parametric transfer function estimations. FIG. 9 is a graph plotting experimental data of the time-varying components of the calibrated brachial artery blood pressure signal, p 1 (t). In the graph shown in FIG. 9, the average DC component {overscore (P)} 1 , which is equal to 70 mm of Hg, has been removed. FIG. 10 is a graph plotting the time-varying components of the uncalibrated radial artery blood pressure signal e 2 (t). Again, in the graph shown in FIG. 10, the average DC component {overscore (E)} 2 , which is equal to 0.33 mV, has been removed. The signals shown in FIGS. 9 and 10 are digitized samples of the continuous signals with a sampling rate of 250 samples/sec.  
     [0147] Non-parametric techniques are generally based on transforming the input and output time functions into the frequency domain. The most common transformation for digitized data is the Discrete Fourier Transform (DFT). When the duration of the signal is equal to a power of 2, the Discrete Fourier Transform can be implemented efficiently using a Fast Fourier Transform (FFT) algorithm. The most commonly used Discrete Fourier Transform-based method for determining the estimated input signal-to-output signal transfer function ĤIO(f) is:  
                     H   ^     IO          (   f   )       =       DFT        {       E        [         o   2          (   t   )              o   2     (     t   +   τ     }       ]            W        (   τ   )         }         DFT        {       E        [         o   2          (   t   )              i   1          (     t   +   τ     )         ]            W        (   τ   )         }           ,           (   19   )                       
 
     [0148] where:  
     [0149] E[. . . ] is the statistical expectation operator; and  
     [0150] W(T) is a windowing function, for example, a Hamming window.  
     [0151] In this exemplary embodiment, the function E[o 2 (t)o 2 (t+τ)] is the auto-correlation function of O 2 (t) and the function E[o 2 (t)i 1 (t+T)] is the cross-correlation of o 2 (t) with i 1 (t). In Eq. ( 19 ), the effect of multiplying the auto-correlation and cross-correlation functions by the window function W(τ) is to smooth the frequency estimates, where the amount of frequency smoothing is inversely proportional to the width of the window function.  
     [0152]FIG. 11 shows the absolute value of the estimated pressure-to-voltage transfer function Ĥ pv (f) obtained using the data shown in FIGS. 9 and 10. Using the estimated pressure-to-voltage transfer function Ĥ pv (f) provided by the data shown in FIG. 11, K is 0.0172 volts/mm of Hg and is derived based on Eq. (13a). Once K is determined, based on Eq. (3), the calibration coefficient C C  is 58.1 mm of Hg/volt. Once the calibration coefficient Cc is determined, based on Eq. (4) or Eq. (18), the calibration constant I 0  is 69.98 mm of Hg. FIG. 12 is a graph plotting the calibrated brachial blood pressure pulse wave P 1 (t) and the radial blood pressure pulse wave P 2 (t) calibrated using the calibration coefficient C C , where C C =58.1 mm of Hg/volt, as determined above.  
     [0153] One parametric technique for determining a transfer function H(f) is the auto-regression (AR) technique. In the auto-regression technique, a transfer function H(z) in the form of a fraction of polynomials in the complex z-domain is determined. The z-domain transfer function H(z) will have a polynomial numerator of order N b −1 and a polynomial denominator of order N a  and will be in the form:  
                   H   ^     IO          (   z   )       =           ∑     n   =   1       N   b              b   n          z     -     (     n   -   1     )               1   +       ∑     n   =   1       N   a              a   n          z     -   n               .             (   20   )                       
 
     [0154] The coefficients a n  and b n  are determined so that the estimated input signal-to-output signal transfer function Ĥ IO (z) is the optimal polynomial transfer function, according to a least-squares method.  
     [0155] A property of the z-transform is that the Fourier transform can be found from the z-transform by simply substituting:  
     z=e j2πf   (21)  
     [0156] Using this substitution, FIG. 13 shows the absolute value of the estimated pressure-to-voltage transfer function |Ĥ pv (f)|, determined using the data shown in FIGS. 9 and 10, with the auto-regression model described in Eq. (20), where N a =2 and N b =1. Eq. (21) and Eq. (14), rewritten for Ĥ pv (f), may be combined to give:  
             K   =           H   ^     pv          (     f   =   0     )       =           H   ^     pv          (     z   =   1     )       =           ∑     n   =   1       N   b            b   n         1   +       ∑     n   =   1       N   a            a   n           .                 (   22   )                       
 
     [0157] Using the estimated input signal-to-output signal transfer function Ĥ IO (z) determined by the auto-regression technique, as shown in FIG. 13, when Eq. (22) is used, with N a =2 and N b =1, K is equal to 0.0164 volts/mm of Hg.  
     [0158] In various exemplary embodiments of the systems and methods according to this invention, the uncalibrated sensor or transducer may be calibrated using either parametric or non-parametric techniques to determine an estimated transfer function. The particular technique implemented will depend largely on the application and/or the measuring device being calibrated. The non-parametric technique is usually more computationally intensive, but it may be more generally applied to systems where the nature of the transfer function is not well known. In systems where the general nature of the transfer function is known and the transfer function may be modeled by a relatively low-order polynomial fraction, a parametric technique can be very computationally efficient. For example, when calibrating the electronic tonometer placed over the radial artery based on the data shown in FIGS.  9  and  10 , the pressure-to-voltage transfer function H pv (f) at frequencies near zero is well approximated by a second-order function of polynomials, i.e., by setting N a =2 and N b =1.  
     [0159] An exemplary embodiment of the systems and methods according to this invention has been described relative to calibrating an electronic tonometer using an oscillometric blood pressure cuff monitor as the calibration standard. In various other exemplary embodiments, other volumetric or pressure transducers that are to be calibrated may be used to measure the blood pressure and calibrated devices other than the calibrated oscillometric blood pressure cuff may be used. The electronic tonometer may be placed, for example, on the wrist of an individual, where the tonometer will produce an electrical signal E 2 (t) proportional to a blood pressure signal P 2 (t) of the radial artery. The electrical signal E 2 (t) may be continuous or discrete. The blood pressure signal output by the electronic tonometer has an average constant component {overscore (E)} 2  that is a function of the electronic circuitry of the tonometer, the average measured arterial blood pressure, and the average transmural pressure of the artery being measured.  
     [0160] As described above, in various exemplary embodiments, the calibrated oscillometric blood pressure monitor is an air-filled occlusive cuff, which may determine the maximum (systolic), average, and minimum (diastolic) blood pressures of, for example, the brachial artery. These blood pressure measures are produced by time-varying changes in the transmural blood pressure of the artery. The time-varying changes in the transmural blood pressure in turn cause volumetric changes of the artery that are transmitted through the overlying tissues to the surface of the skin, where the air-filled occlusive cuff responds to volumetric changes by producing pressure fluctuations in the air-filled occlusive cuff. The amplitude of the pressure fluctuations in the air-filled occlusive cuff and thus, the pressure signals of the oscillometric blood pressure monitor, are a function of the volume of the air-filled occlusive cuff, the differences between the cuff pressures and the arterial pressures (the transmural pressures), the elasticity of the arterial wall, the electrical characteristics of oscillometric blood pressure monitor, and the frequency responses of the air-filled occlusive cuff, the overlying tissues, the air-filled connector tubing. The oscillometric blood pressure monitor may measure the blood pressure continuously or discretely.  
     [0161] When the transmural pressure differences are small, the shape of the blood pressure signals measured by the oscillometric blood pressure monitor will closely approximate those of the internal blood pressure signal of the artery. The technique of measuring the blood pressure signal when the transmural pressure differences are small is known as plethysmographic measurement. As shown in FIG. 14, plethysmographic measurements are made at the end of the oscillometric blood pressure measurement cycle. If the plethysmographic measurements are digitized, the measures may be used directly, after possible scaling, in determining the calibration coefficient and the calibration constant, using, for example, a general purpose computer, a personal computer, a microprocessor, a digital signal processor or any equivalent device or circuit. If the plethysmographic measurements are analog, the measures must first be digitized, for example, by an analog-to-digital converter, before the digitized measurements can be used. By measuring an electrical signal E(t) from an electronic tonometer, the blood pressure P(t) is:  
       P ( t )= C   pv   E ( t )+ P   0 ,  (23)  
     [0162] where:  
     [0163] P(t) is the time-varying blood pressure of a living being;  
     [0164] C pv  is the calibration coefficient for a blood pressure-to-voltage transducer;  
     [0165] E(t) is the time-varying electrical signal generated by sensing the time-varying blood pressure of the living being; and  
     [0166] P 0  is calibration constant for this blood pressure-to-voltage transducer.  
     [0167] The blood pressure-to-voltage calibration coefficient CPV and blood pressure calibration constant P 0  may be found by using the average, diastolic and systolic pressures measured by a non-invasive blood pressure (NIBP) monitor, as described above relative to an air-filled occlusion cuff.  
     [0168] For a plethysmographic measurement, if the average blood pressure is {overscore (P)}, the calibration coefficient C pv  is:  
                 C   pv     =         P   S     -     P   D           E   S     -     E   D           ,           (   24   )                       
 
     [0169] where:  
     [0170] P S  is the systolic blood pressure of the living being;  
     [0171] P D  is the diastolic blood pressure of the living being;  
     [0172] E S  is the value of the time-varying electrical signal that corresponds to the systolic blood pressure P S  of the living being; and  
     [0173] E D  is the value of the time-varying electrical signal that corresponds to the diastolic blood pressure P D  of the living being.  
     [0174] Accordingly, based on rewriting Eq. (23), the calibration constant P 0  is:  
       P   0   ={circumflex over (P)}−C   pv   {overscore (E)}.   (25)  
     [0175]FIG. 15 is a flowchart outlining one exemplary embodiment of a method for calibrating an uncalibrated sensor or transducer according to this invention. In particular, in the flowchart shown in FIG. 15, the uncalibrated sensor is located at a second location B relative to the system being sensed that is spaced away from a first location A of a calibrated sensor that has been previously calibrated to the system being sensed.  
     [0176] As shown in FIG. 15, operation of the method begins in step S 100 , and continues to step S 110 , where input values I 1 (t) of the physical phenomenon are obtained from the calibrated sensor at the first location A over at least one full cycle of the periodic physical phenomenon of the system that is being sensed by the calibrated and uncalibrated sensors. Then, in step S 120 , the output signal O 2 (t) is obtained from the uncalibrated sensor at the second location B for at least one full cycle of the physical phenomenon of the system. In general, steps S 110  and  120  will often occur simultaneously, so that the system being sensed is in the same state for both measurements. However, it should be appreciated that steps S 110  and  120  do not necessarily need to be performed simultaneously so long as the system being sensed is in substantially the same state when each of steps S 110  and  120  are performed. Operation then continues to step S 130 .  
     [0177] In step S 130 , the mean values of {overscore (I)} and {overscore (O)} of the input signal I 1 (t) and the output signal O 2 (t) are determined. Next, in step S 140 , the input signal-to-output signal transfer function H IO (f) is estimated. It should be appreciated that the estimated input signal-to-output signal transfer function can be estimated using either parametric or non-parametric methods. In particular, any known or later-developed method for estimating the estimated input signal-to-output signal transfer function can be used. Then, in step S 150 , the value of K is determined based on the zero-frequency value for the estimated input signal-to-output signal transfer function Ĥ IO (f). Operation then continues to step S 160 .  
     [0178] In step S 160 , the sign of K is determined. As indicated above, if the measured output signal O 2 (t) follows the input signal I 1 (t), the sign of K will be positive. In contrast, if the output signal O 2 (t) is inverted relative to the input signal I 1 (t), K will be negative. If the phase of the input signal-to-output signal transfer function Ĥ IO (f) is known, the sign of K can be readily determined. If the phase of the transfer function is not known, the phase of the estimated input signal-to-output signal transfer function can be determined to determine the sign of K. Operation then continues to step S 170 .  
     [0179] In step S 170 , the calibration coefficient C C  is determined as the reciprocal of K. Next, in step S 180 , the calibration constant O 0  is determined based on the determined calibration coefficient C C  and the determined mean input signal and output signal values {overscore (I)} and Ô. Then in step S 190 , operation of the method ends.  
     [0180]FIG. 16 is a flowchart outlining one exemplary embodiment of a method for ensuring that the calibrated first sensor placed at the first location A relative to the system being sensed is properly calibrated to the system being sensed. As outlined above, the flowchart outlined with respect to FIG. 15 assumes that the calibrated sensor is properly calibrated to the system being sensed. The method outlined in FIG. 16 can be used to calibrate a sensor so that that sensor can be used as the calibrated sensor in the method outlined in FIG. 15.  
     [0181] As shown in FIG. 16, operation of the method begins in step S 200  and continues to step S 210 , wherein input values I 1 (t) of the physical phenomenon being sensed at location A are obtained for at least first and second times t 1  and t 2  using the first sensor placed at the first location A relative to the system being sensed. Then, in step S 220 , output signals O 1 (t) from the first sensor are generated or obtained in response to the physical phenomenon being sensed in the system being sensed at the first location A and for at least the times t 1  and t 2  using the first sensor. Next, in step S 230 , the calibration coefficient C C  for the first sensor is determined based on the input values I 1 (t 1 ) and I 1 (t 2 ) of the physical phenomenon being sensed and the corresponding generated output signals O 1 (t 1 ) and O 1 (t 2 ) from the sensor device located at the first location A relative to the system being sensed. Operation then continues to step S 240 .  
     [0182] In step S 240 , mean values {overscore (I)} and {overscore (O)} of the input signal I 1 (t) and the output signal O 1 (t), respectively, are determined based at least in part on the input values I 1 (t) and the output signals O 1 (t) determined in steps S 210  and  220 . Next, in step S 250 , the calibration constant O 1  is determined based on the determined calibration coefficient C 1  and the determined mean input signal and output signal values {overscore (I)} and {overscore (O)}. Then, in step S 260 , the output signal values are scaled to the input values based on the determined calibration parameters. Operation then continues to step S 270 , where operation of the method ends.  
     [0183]FIG. 17 is a flowchart outlining one exemplary embodiment of applying the methods outlined above in FIGS. 15 and 16 to calibrating a radial artery blood pressure sensor, such as a radial tonometer, using an occlusion cuff blood pressure sensor placed on a living being to sense the brachial artery blood pressure. In particular, as shown in FIG. 17, operation of the method begins in step S 300  and continues to step S 310 , where the occlusion cuff is itself calibrated to the living being to which the radial tonometer will be calibrated using the systems and methods according to this invention. Operation then continues to step S 320 .  
     [0184] It should be appreciated that any known or later-developed method or technique for calibrating the occlusion cuff to the living being can be used in step S 310 . Additionally, it should be appreciated that step S 310  can be omitted either if the occlusion cuff has already been calibrated to this living being, or if the occlusion cuff has been generally calibrated by determining generalized calibration parameters that are usable relative to this living being. For example, if most human beings have substantially similar calibration parameters for the occlusion cuff, generalized calibration parameters usable with any human being can be determined to calibrate the occlusion cuff and used in the systems and methods according to this invention to calibrate the uncalibrated radial tonometer.  
     [0185] In step S 320 , the occlusion cuff is pressurized, by inflating or deflating the occlusion cuff, around the upper limb of the living being to a pressure that is at most just below the diastolic blood pressure in the brachial artery of that living being. It should be appreciated that the pressure in the occlusion cuff need not be close to the diastolic pressure. Then, in step S 330 , signals P 1 (t) and E 2 (t) are obtained from the occlusion cuff and the uncalibrated radial artery blood pressure sensor, respectively, for at least one full cycle of the blood pressure pulse wave. Next, in step  340 , the mean values {overscore (P)} 1  and {overscore (E)} 2  for the signals P 1 (t) and E 2 (t), respectively, are determined. Operation then continues to step S 350 .  
     [0186] In step S 350 , the blood pressure pulse wave-to-radial artery sensor output signal transfer function Ĥ pv (f) is estimated using any known or later developed parametric or non-parametric technique usable to estimate a transfer function in the frequency domain. Next, in step S 360 , the value of K is determined based on the zero-frequency value of the estimated blood pressure pulse wave-to-radial artery sensor output signal transfer function Ĥ pv (0). Then, in step S 370 , the sign of K is determined as outlined above with respect to step S 160 . Operation then continues to step S 380 .  
     [0187] In step S 380 , the calibration coefficient Cc for the uncalibrated radial artery sensor is determined as the reciprocal of K. Then, in step S 390 , the calibration constant P 0  for the uncalibrated radial artery sensor is determined based on the determined calibration coefficient C C  and the determined mean brachial artery blood pressure {overscore (P)} 1  and the mean radial artery sensor output signal value Ê 2 . Operation then continues to step S 400 , where the operation of the method ends.  
     [0188]FIG. 18 is a block diagram outlining one exemplary embodiment of a sensor calibration system according to this invention. As shown in FIG. 18, the sensor calibration system  100  is connected to a calibrated input signal sensor  200  by a link  210  and to an uncalibrated input signal sensor  300  by a link  310 . Each of the calibrated and uncalibrated input signal sensors  200  and  300  are attached to a system having a physical phenomenon to be sensed. In particular, the calibrated input signal sensor  200  is connected to the system at a first location A, while the uncalibrated input signal sensor  300  is attached to the system at a second location B that is spaced apart from the first location A.  
     [0189] The sensor calibration system  100  includes an input output interface  110  that inputs the signals over the links  210  and  310 , a controller  120 , a memory  130 , a mean value determining circuit or routine  140 , an estimated transfer function determining circuit or routine  150 , and a calibration parameters determining circuit or routine  160 , each interconnected by a control and/or data bus  170 . The memory  130  includes a signal portion  132 , a mean value portion  134 , an estimated transfer function portion  136  and a calibration parameters portion  138 .  
     [0190] The signal portion  132  stores the one or more full cycles of the periodic physical phenomenon being sensed output by each of the calibrated and uncalibrated input signal sensors  200  and  300 . The mean value portion  134  stores the mean values determined by the mean value determining circuit or routine from the signals received from the calibrated and/or uncalibrated input signal sensors  200  and/or  300 . The estimated transfer function portion  136  stores the estimated transfer function generated by the estimated transfer function determining circuit or routine  150 . The calibration parameters portion  138  stores the calibration coefficient and the calibration constant determined by the calibration and parameters determining circuit or routine for the uncalibrated input signal sensor  300 . The calibration parameters portion  138  can also store the calibration parameters determined by the sensor calibration system for the calibrated input signal sensor  200 . Alternatively, the calibration parameters portion  138  can store predetermined calibration parameters for the calibrated input signal sensor  200 .  
     [0191] In operation, under control of the controller  120  of the sensor calibration system  100 , one or more of the calibrated input signal sensor  200  and the uncalibrated input signal sensor  300  generate output signals from the sensed physical phenomenon of the system being sensed. These output signals are provided by the one or more of the calibrated and/or uncalibrated input signal sensors  200  and/or  300  over the links  210  and/or  310 , respectively, to the input output interface  110 . The input output interface  110 , under control of the controller  120 , stores the signals in the signal portion  132  of the memory  130 . Then, under control of the controller  120 , the signals stored in the signal portion  132  are output to the mean value determining circuit or routine  140 . The mean value determining circuit or routine  140  determines the mean values for the signals from each of the calibrated and uncalibrated input signal sensors  200  and  300 . Then, under control of the controller  120 , the mean values determined by the mean value determining circuit or routine are stored in the mean value portion  134 .  
     [0192] Also under control of the controller  120 , the signals stored in the signal portion  132  are provided to the estimated transfer function determining circuit or routine, which determines an estimated transfer function Ĥ IO (f) from the input signal portions using any known or later developed parametric or non-parametric transfer function estimating technique or algorithm. Then, under control of the controller  120 , the estimated transfer function is stored into the estimated transfer function portion  136 . It should be appreciated that the estimated transfer function and determining circuit  150  can operate independently or concurrently with the mean value determining circuit  140 .  
     [0193] Next, under control of the controller  120 , the calibration parameters determining circuit or routine  160  inputs the estimated transfer function stored in the estimated transfer function portion and extracts a desired frequency component, such as, for example, the zero frequency component, of the estimated transfer function as the value of K. Next, the calibration parameters determining circuit or routine  160  determines the value of the calibration coefficient C C  as the reciprocal of the value K. Then, the calibration parameters determining circuit or routine  160  determines the calibration constant O 0  based on the determined calibration coefficient C C , the mean value of the output signal output by the uncalibrated input signal sensor  300 , and the mean value of the physical phenomenon, which is obtained from the output signal output by the calibrated input signal sensor  200 . The calibration parameters for the uncalibrated sensor  300  determined by the calibration parameters determining circuit or routine  160  are stored in the calibration parameters portion  138 .  
     [0194] It should also be appreciated that, if the calibrated input signal sensor  200  needs to be calibrated for the particular location A of the system at which it is located, or for any other reason, the calibration parameters determining circuit or routine  160  can perform this operation. In particular, under control of the controller  120 , the calibrated input signal sensor  200  is operated to generate signal values usable to calibrate the calibrated input signal sensor, such as those outlined above with respect to FIGS. 17A and 17B. These signal portions are input through the input/output interface  110  and, under control of the controller  120 , are stored in the signal portion  132 . Then, the calibration parameters determining circuit or routine  160  generates the calibration coefficient C C1  for the calibrated input signal sensor  200  and the calibration constant I 1  for the calibrated input signal sensor  200 .  
     [0195] The sensor calibration system  100  shown in FIG. 18 is, in various exemplary embodiments, implemented on a programmed general purpose computer. However, the sensor calibration system  100  can also be implemented on a special purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmable logic device such as a PLD, PLA, FPGA or PAL, or the like. In general, any device, capable of implementing a finite state machine that is in turn capable of implementing the flowcharts shown in FIGS.  15 - 17 B, can be used to implement the sensor calibration system  100 .  
     [0196] It should be understood that each of the circuits shown in FIG. 18 can be implemented as portions of a suitably programmed general purpose computer. Alternatively, each of the circuits shown in FIG. 18 can be implemented as physically distinct hardware circuits within an ASIC, or using a FPGA, a PDL, a PLA or a PAL, or using discrete logic elements or discrete circuit elements. The particular form each of the circuits shown in FIG. 18 will take is a design choice and will be obvious and predicable to those skilled in the art.  
     [0197] Moreover, the sensor calibration system  100  can be implemented as software executing on a programmed general purpose computer, a special purpose computer, a microprocessor or the like. In this case, the sensor calibration system  100  can be implemented as a routine embedded in a sensor system, as a resource residing on a server, or the like. The sensor calibration system  100  can also be implemented by physically incorporating it into a software and/or hardware system.  
     [0198] The memory  130  shown in FIG. 18 can be implemented using any appropriate combination of alterable, volatile or non-volatile memory or non-alterable, or fixed, memory. The alterable memory, whether volatile or non-volatile, can be implemented using any one or more of static or dynamic RAM, a floppy disk and disk drive, a writable or re-writable optical disk and disk drive, a hard drive, flash memory or the like. Similarly, the non-alterable or fixed memory can be implemented using any one or more of ROM, PROM, EPROM, EEPROM, an optical ROM disk, such as a CD-ROM or DVD-ROM disk, and disk drive or the like.  
     [0199] The links  210  and  310  can each be any known or later developed device or system for connecting the sensors  200  and  300 , respectively, to the sensor calibration system  100 , including a connection through a public switched telephone network, a direct cable connection, a connection over a wide area network or a local area network, a connection over an intranet, a connection over the Internet, or a connection over any other distributed processing network or system. Further, it should be appreciated that, for each of the links  210  and  310  connecting the sensors  200  and  300 , respectively, to the sensor calibration system  100 , at least a portion of each such link can be a wired or wireless link In general, the links  210  and  310  can each any known or later developed connection system or structure usable to connect the scanner  100  to the scanned image registration system  200 .  
     [0200] While this invention has been described in conjunction with the specific embodiments outlined above, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, the preferred embodiments of the invention as set forth above are intended to be illustrative, not limiting. Various changes may be made without departing from the spirit and scope of the invention as defined in the following claims.