Respiration monitoring system based on sensed physiological parameters

Abstract

A system and method obtains a respiration parameter of an animal or human, such as respiratory rate, from a blood pressure, volumetric blood flow, blood velocity or stroke volume signal from a sensor for the animal or human. Specifically, the signal is processed to develop an amplitude versus time waveform. A sequence of selected features derived from individual cardiac cycles of the amplitude versus time waveform over a selected time interval are extracted from the developed amplitude versus time waveform. A mathematical model is fitted to the extracted sequence of selected features to yield a fitted mathematical model. The physiologic parameter information is computed from the fitted mathematical model.

Description

FIELD OF THE INVENTION

[0002] The present invention relates generally to obtaining physiological parameters from animals or humans, and more particularly to a system for deriving respiratory related information from another sensed physiological parameter.

BACKGROUND OF THE INVENTION

[0003] In animals or humans, it is often desirable to be able to monitor a variety of physiological parameters for purposes of research, therapeutics, and diagnosis. One such parameter that is of value is respiration. Simple measurement of the animal's or human's respiration rate have included the use of plethysmographs, strain gauges, chest impedance measurements, diaphragmatic EMG measurements, and other known measurement devices. For example in animal research, the plethysmograph approach requires the use of a non-compliant closed box in which the animal is placed with a means for measuring either the air flow in and out of the box or pressure changes inside the box or air flow at the animal's mouth when breathing air from outside or inside the closed container. While plethysmographs work reasonably well with human subjects who can cooperate with test personnel, it is unreliable when dealing with laboratory animals, such as rats, dogs, monkeys, and etc.

[0004] The use of strain gauges and apparatus for measuring chest impedance changes generally require the animal to be tethered to the test equipment via electrical leads and the like. This does not lend itself to chronic testing and, moreover, strain gauges are quite sensitive to movement artifacts that can mask the desired signal output. Diaphragmatic EMG measurements can be used to determine respiratory rate, but electrode placement requires a higher skilled surgeon, and electrical noise from ECG and other sources can make accurate detection of respiration difficult.

[0005] To allow for animal mobility, it has proven advantageous to surgically implant sensors within the animal along with a telemetry transmitter so that the sensed signals can be electronically transmitted to an external receiver without the need for exteriorized conductive leads or catheters. U.S. Pat. No. 4,846,191 to Brockway, et al., owned by applicant's assignee, describes an implantable blood pressure sensing and telemetering device suitable for long-term use in a variety of laboratory animals. A solid-state pressure sensor is fluid coupled to a selected blood vessel and the signal produced by the sensor is amplified, digitized and transmitted, transcutaneously, to an external receiver by means of a battery-powered transmitter. Once the blood pressure data are received, they are signal processed to recover features thereof, such as mean systolic pressure, mean diastolic pressure, mean arterial pressure, heart rate, etc. The Brockway et al. '191 patent also recognizes that the pressure sensing system used therein can be adapted to monitor intrathoracic pressure from which respiratory rate and other respiratory parameters can be derived. However, if it is desired to chronically monitor both blood pressure and respiratory activity following the teachings of the Brockway et al. patent, plural sensors and at least one telemetry transmitter with a multiplexing capability is required.

[0006] The Kahn et al. U.S. Pat. No. 4,860,759 demonstrates the combined use of transthoracic impedance and strain gauge sensors to monitor respiration rate. The approach disclosed in the Kahn et al. patent suffers from many shortcomings, not the least of which is the quality of the resulting data.

[0007] An example of the use of multiple, chronically implanted sensors in laboratory animals is described in R. Rubini et al., Power Spectrum Analysis of Cardiovascular Variability Monitored By Telemetry in Conscious, Unrestrained Rats,Journal of the Autonomic Nervous System, vol. 45, at 181-190 (1993). In the experiments described in the Rubini et al. paper, telemetry equipment manufactured by Data Sciences International (applicant's assignee) was utilized. However, the experimenters involved, while recognizing that a respiratory artifact was present in the sensed blood pressure data, discarded this component in favor of lower frequency components relating to sympathetic modulation of the cardiovascular system.

[0008] It is also known in the art that blood pressure, blood flow and stroke volume are influenced by respiratory activity. In this regard, reference is made to H. Barthelmes and J. Eichmeier, A Device with Digital Display for the Determination of Respiratory Frequency from the Respiratory Fluctuations of Blood Pressure, Biomedizinische Technik, vol. 18, No. 4 (August 1973) and to D. Laude et. al., Effect of Breathing Pattern on Blood Pressure and Heart Rate Oscillations in Humans, Clinical and Experimental Pharmacology and Physiology, vol. 20, at 619-626 (1993). A system described in the Barthelmes paper employs an analog electronic filter to extract respiration rate from blood pressure signals. In the Laude article, human subjects were told to breath in rhythm with a metronome at several discrete frequencies while blood pressure was continuously measured. This study led to the conclusion that the relationship between systolic blood pressure and respiration differs from that between respiration and respiration sinus arrhythmia. Medical Physiology, Vol 1, at 217 (Vernon B. Mountcastle, M.D. ed., C. V. Mosby Company, 12th ed., 1968) similarly recognizes the effect of respiration on blood flow and stroke volume.

[0009] Although there are many studies in the published literature documenting variations of blood pressure, blood flow and stroke volume with respiration, there is a need for an improved method and apparatus to derive respiratory parameters from blood pressure, volumetric blood flow, blood velocity and stroke volume data.

SUMMARY OF THE INVENTION

[0010] The present invention provides a method and apparatus for obtaining respiratory parameter information of an animal or human from sensed variations in another physiological parameter, e.g., blood pressure, volumetric blood flow, blood velocity, or stroke volume. In one embodiment, the blood pressure signal is externally signal processed to develop an amplitude versus time waveform. A sequence of selected blood pressure features derived from individual cardiac cycles of the amplitude versus time waveform over a selected time interval are extracted from the developed amplitude versus time waveform. A mathematical model is fitted to the extracted sequence of selected blood pressure features to yield a fitted mathematical model. The physiologic parameter information is computed from the fitted mathematical model.

[0011] In a preferred embodiment of the present invention, the physiologic parameter information obtained is respiratory rate. In accordance with this preferred embodiment, a catheter tip of a blood pressure sensor is surgically implanted in an animal's vascular system where the sensor provides an electrical signal related to variations in the animal's blood pressure. A telemetry transmitter, also implanted in the animal, is connected to the blood pressure sensor for selectively transmitting the electrical signal to an external receiver. The external receiver receives the the electrical signal and provides the blood pressure signal. The blood pressure signal is then signal processed in a digital computer to develop the amplitude versus time waveform. The signal processing algorithm extracts from the amplitude versus time waveform the sequence of selected blood pressure features such as beat-to-beat systolic data points, beat-to-beat diastolic data points, or beat-to-beat mean values of blood pressure over a predetermined time interval.

[0012] The mathematical model in this preferred embodiment is an nth order polynomial curve, which is preferably fitted to n+1 sequential points of the selected blood pressure features to obtain a fitted nth order polynomial curve. The fitted nth order polynomial curve preferably includes curve fit data values substantially equi-spaced in time. The curve fit data values are tested for critical points in accordance with a predetermined criteria such as a criteria relating peak values to zero-crossings of the fitted curve. Next, the computer determines from the critical points whether they are a maximum or a minimum whereby the respiratory rate can be determined from successive maximum and minimum critical points.

[0013] In one alternative embodiment of the present invention, spectral analysis is performed on the fitted mathematical model. In another alternative embodiment, several distinct blood pressure features for each cardiac cycle are combined to yield a sequence of averaged blood pressure features, and the mathematical model is then fitted to the resulting sequence of averaged blood pressure features.

[0014] The implantable pressure sensor/transmitter described in the Brockway et al. '191 patent referenced in the background section provides an excellent means for monitoring blood pressure By appropriately signal processing the telemetered blood pressure waveforms, respiration rate and other physiologic parameters can be derived therefrom according to the present invention.

[0015] Accordingly, the present invention provides an improved system for monitoring a plurality of physiologic parameters in animals or humans using a single implanted telemetric sensor. Moreover, the present invention provides a system for measuring blood pressure and respiratory parameters in such animals or humans using a single telemetric sensor. The present invention also provides a system for extracting respiratory related physiologic data from animals or humans using an implanted blood pressure sensor and telemetry transmitter where the transmitted blood pressure signal is externally signal processed to recover the respiratory information contained in the blood pressure signal. It is not, however, necessary that the blood pressure data be telemetered. A blood pressure sensor could be connected via wires to an amplifier and analog-to-digital converter and then input into the computer-based system according to the present invention.

[0016] In another embodiment, a method of obtaining respiratory parameter information of an animal or human from a signal indicative of sensed variations in blood flow data of the animal or human. The method generates a signal that represents the blood flow data. A sequence of selected features of the blood flow data are extracted from the signal over a selected time interval. A mathematical model is fitted to the extracted sequence of selected features to yield a fitted mathematical model. The respiratory parameter information is computed from the fitted mathematical model.

[0017] In another embodiment, an apparatus that obtains respiratory parameter information of an animal or human from a signal indicative of sensed variations in blood flow data of the animal or human is provided. The apparatus includes a sensor that generates a signal that represents the blood flow data. The apparatus further includes a data processing apparatus. The data processing apparatus is communicatively coupled to the sensor. The data processing apparatus extracts from the signal a sequence of selected features of the blood flow data over a selected time interval. The data processing apparatus further fits a mathematical model to the extracted sequence of selected features to yield a fitted mathematical model. And, the data processing apparatus computes the respiratory parameter information from the fitted mathematical model.

[0018] In another embodiment, a method of obtaining respiratory parameter information of an animal or human from a blood pressure signal indicative of sensed variations in blood pressure of the animal or human. The method comprises extracting from the blood pressure signal a sequence of selected blood pressure features derived from individual cardiac cycles of the amplitude versus time waveform over a selected time interval. Further, the method fits a mathematical model to the extracted sequence of selected blood pressure features to yield a fitted mathematical model. The method computes the respiratory parameter information from the fitted mathematical model.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG. 1 is a block diagram of an embodiment of a system according to the present invention for collecting and processing signals that represent a physiological parameter from which respiration information is derived.

[0020] FIG. 2 is a software flow diagram illustrating an embodiment of a process for execution on a computer that extracts the length of a respiratory cycle and therefore a respiratory rate from blood pressure data.

[0021] FIG. 3 is a graph illustrating a plot of blood pressure versus time showing a low frequency respiratory component of the blood pressure.

[0022] FIG. 4 is a block diagram of an embodiment of a system for gathering blood flow, blood velocity or stroke volume data according to the present invention.

[0023] FIG. 5 is a software flow diagram illustrating an embodiment of a process for execution on a computer that extracts the length of a respiratory cycle and therefore a respiratory rate from blood flow, blood velocity or stroke volume data.

[0024] FIG. 6 is a graph illustrating a plot of blood velocity versus time showing a low frequency respiratory component of the blood velocity.

[0025] FIG. 7 is a graph illustrating a plot of stroke volume versus time showing a low frequency respiratory component of the stroke volume.

DETAILED DESCRIPTION

[0026] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present invention. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.

I. Obtaining a Respiration Parameter From Blood Pressure

[0027] Referring to the block diagram of FIG. 1, there is indicated a plurality of individual laboratory animals 10, 12 and 14 each having a blood pressure sensor and telemetry transmitter of the type described in the Brockway Patent U.S. Pat. No. 4,846,191 implanted therein. These implanted modules are identified by numeral 11 in FIG. 1.

[0028] Disposed with or in proximity of each of the cages where the animals are kept is a telemetry receiver 15 which is tuned to pick up a signal transmitted from an individual animal. The individual receivers, in turn, are connected to multiplexers, as at 16, capable of servicing the number of individual receivers connected thereto, whereby data from a particular selected animal may be fed into the computer-based system 18 for collecting, storing, displaying, and analyzing data from a large plurality of animals.

[0029] In accordance with the present invention, the sensor/transmitters 11 are surgically implanted within the animals so as to monitor blood pressure in a predetermined blood vessel. The sensor/transmitter transmits bursts of pulses, the timing of which codes the magnitude of the analog blood pressure signal picked up by the sensor. Because the implanted units measure pressure relative to a vacuum, a pressure reference 20 is used to provide an input of the ambient barometric pressure to the computer-based system 18. By subtracting the ambient room pressure from the transmitter pressure readings, changes in barometric pressure can be accommodated.

[0030] Turning now to FIG. 2, there is illustrated by means of a software flow diagram the algorithm executed by the computer-based system 18 for deriving both blood pressure information and respiratory information from the blood pressure measurements taken using the equipment illustrated in FIG. 1. This flow chart is in sufficient detail that persons skilled in programming an IBM PC or clone thereof can write the source code for such a computer.

[0031] The first step in the algorithm identified by numeral 20 is to bring in the raw blood pressure data from an animal via the associated receiver 15 and consolidation matrix 16. This data is temporarily held in a DMA buffer (block 22) where it is maintained until called for by the program.

[0032] Had more than one sensor been implanted in the animal to provide measurements of more than one physiologic parameter, such as, for example, blood pressure and EKG, the data stream provided by the associated telemetry transmitter would include both channels of data. Hence, the software includes a routine for separating the multichannel data (block 24). In such an arrangement, a circular buffer is provided for each of the parameters being measured so that data from the individual channels can be separately stored (block 26). The data from a circular buffer, when selected, is processed in an interpolation algorithm (block 28), which converts the data to points substantially equi-spaced in time. The interval between successive points is set by the sample rate chosen in the software. It does so by fitting a third order polynomial of the form y=ax3+bx2+cx +d to a series of four points comprising blood pressure readings at four different times. Continuing on with the assumption that it is the blood pressure parameter that has been sensed and is being processed, at block 30, the data are converted to appropriate units, e.g., mmHg. If noise is present in the interpolated data, it may be necessary to low pass filter the data to remove high frequency noise (block 32).

[0033] The resulting waveform data may again be held in a buffer (block 34) until called for. If desired, and as represented by block 36, the waveform can be saved on the computer's hard drive, a floppy-disk and/or presented on a computer display screen. The blood pressure waveform can be processed by a feature detection algorithm (block 38) in which the peaks, valleys, slopes, zero-crossings and the like are examined to extract information of value to the researcher, such as systole, diastole, heart rate and dP/dt. Referring to the waveform of FIG. 3, there is illustrated a plot of blood pressure measured in mmHg versus time for a five-second interval. The systolic points obtained as a result of the feature detection operation 38 are identified in FIG. 3 by numeral 40 and the diastolic valleys by numeral 42. The systolic points and diastolic points shown in FIG. 3 are illustrative of sequences of selected blood pressure features such as beat-to-beat systolic data points and beat-to-beat diastolic data points, but which can also include other blood pressure features such as beat-to-beat mean values of blood pressure over a predetermined time interval.

[0034] To obtain readings of the systolic and diastolic blood pressure, the points 40 and 42 may be averaged over a predetermined time interval such as, for example, 10 seconds which, in FIG. 2, is referred to as parameter extraction (block 44). Control loops back, via path 46, to the input of block 20 where further raw data telemetered from the implanted sensor or sensors is inputted for processing.

[0035] The algorithm represented by blocks 20 through 38 and 44 have been used in the past for deriving blood pressure parameters on an on-line and off-line basis. The present invention is concerned with the improvement whereby respiratory parameters may also be derived from the measured blood pressure signals obtained from the laboratory animals or alternatively from humans.

[0036] Referring again to FIG. 2, the series of systole points 40 obtained as a result of the feature detection operation 38 are supplied as inputs to another curve-fitting algorithm (block 48) which functions to add additional points between the peaks 40, thereby creating a fitted curve between adjacent systolic peaks in the blood pressure versus time waveform. The thus curve-fitted systolic waveform is labeled as such in FIG. 3. Once a third order curve has been fit to the data, the resulting waveform is tested for critical points based on a predetermined criteria (block 50). For example, the predetermined criteria in the preferred embodiment defines critical points at maximum excursions above a zero-crossing and/or peaks and valleys in the curve-fitted waveform. A test is made at decision block 52 whether a critical point has been reached and, if not, control exits via path 54 to block 59 and further systole points are subjected to the curve-fitting algorithm at block 48.

[0037] When the test at decision block 52 indicates that a critical point has been reached, the operation performed at block 56 determines whether the critical point is a maximum or a minimum. By computing the interval between two adjacent maximums or two adjacent minimums, the length of a respiratory cycle is obtained. The inverse of this cycle length is respiratory rate, measured in breaths per second. The interval is continuously computed as represented by block 58 to provide a current, real-time indication of respiration rate. Although respiratory rate is a parameter of value and is the parameter derived in the preferred embodiment, the present invention is not limited to deriving respiratory rate from the curve-fit beat-beat data as the present invention can be applied to derive other physiologic parameters from the curve-fit beat-beat data.

II. Obtaining a Respiration Parameter from Volumetric Blood Flow, Blood Velocity or Stroke Volume

[0038] FIG. 4 is a block diagram of an embodiment of a system for deriving a respiration parameter from volumetric blood flow, blood velocity or stroke volume data according to the present invention. For purposes of this specification, the term “blood flow” or “blood flow data” refers to volumetric blood flow, blood velocity or stroke volume, collectively and individually. In the art, volumetric blood flow and blood flow velocity are understood as interchangeable concepts, since estimates of volumetric blood flow are obtained from estimates of blood flow velocity by multiplying blood flow velocity with a known constant cross-sectional area of a blood vessel.

[0039] The system includes probe 400 that is placed in proximity of a selected blood vessel 402 of an animal or human under test. Probe 400 comprises, for example, a probe that uses Doppler ultrasound (pulsed or continuous), laser Doppler, transit time ultrasound, thermal dilution flow measurement, electromagnetic flow measurement, or other suitable flow measurement technique to measure blood flow data, such as volumetric blood flow, blood velocity and/or stroke volume. Probe 400 is held in place with, for example, a cuff that surrounds blood vessel 402. Alternatively, probe 400 is sutured to vessel 402, held in place by hand, or fixed in place using another appropriate technique. Probes for gathering data on volumetric blood flow and blood velocity are commercially available from Crystal Biotech Incorporated, of Northborough, Mass. Other appropriate sensors can be used to gather the volumetric blood flow, blood velocity or stroke volume data.

[0040] Probe 400 is coupled to flow measurement apparatus 404. Flow measurement apparatus 404 provides a stimulus for probe 400 to use in gathering data that represents the volumetric blood flow, blood velocity and/or stroke volume of the animal or human under test. Flow measurement apparatus 404 can further generate an appropriate waveform representation from the data gathered by probe 400.

[0041] In one embodiment, data processing circuit 406 is coupled to flow measurement apparatus 404. Flow measurement apparatus 404 or data processing apparatus 406 calculates parameters such as mean velocity, peak velocity, mean flow, peak flow, mean Doppler frequency, and peak Doppler frequency. Data processing circuit 406 uses, for example, the technique described below to calculate a respiration parameter, e.g., respiration rate, from the data output of flow measurement apparatus 404.

[0042] Turning now to FIG. 5, there is illustrated by means of a software flow diagram an embodiment of a process executed by flow measurement apparatus 404 and data processing apparatus 406. It is noted that although flow measurement apparatus 404 and data processing apparatus 406 are shown as separate blocks in FIG. 4, their functionality can be implemented by a single microprocessor based computer or circuit running a software application that uses the process shown and described with respect to FIG. 5. Further, it is noted that FIG. 5 is described in terms of a system that processes data external to the animal or human subject. It is understood that the system of FIG. 4 can be implemented in an implantable device with no external processing. In this case, the system can provide a control signal for some other device that is controlled at least in part by a respiration parameter, e.g., a stimulator, an artificial organ, or a drug pump.

[0043] At block 520, raw blood flow data from an animal is conveyed to flow measurement apparatus 404 via, for example, an associated receiver and consolidation matrix. This data is temporarily held in a DMA buffer (block 522) where it is maintained until called for by the program.

[0044] Had more than one sensor been implanted in the animal to provide measurements of more than one physiologic parameter, such as, for example, blood flow and blood pressure, the data stream provided by the associated telemetry transmitter would include both channels of data. Hence, the software includes a routine for separating the multichannel data (block 524). In such an arrangement, a circular buffer is provided for each of the parameters being measured so that data from the individual channels can be separately stored (block 526). The data from a circular buffer, when selected, is processed in an interpolation algorithm (block 528), which converts the data to points substantially equi-spaced in time. The interval between successive points is set by the sample rate chosen in the software, e.g., 10 milliseconds per data point. It does so by fitting a third order polynomial of the form y=ax3+bx2+cx +d to a series of four points comprising blood flow readings at four different times. Continuing on with the assumption that it is the blood flow parameter that has been sensed and is being processed, at block 530, the data are converted to appropriate units, e.g., centimeters/second, milliliters/minute. If noise is present in the interpolated data, it may be necessary to low pass filter the data to remove high frequency noise (block 532).

[0045] The resulting waveform data may again be held in a buffer (block 534) until called for. If desired, and as represented by block 536, the waveform can be saved and/or displayed, e.g., on a monitor. The blood flow waveform can be processed by a feature detection algorithm (block 538) in which the peaks, valleys, slopes, zero-crossings and the like are examined to extract information of value to the researcher. Referring to the waveform of FIG. 6, there is illustrated a plot of blood velocity measured in cm/sec versus time. The peak points obtained as a result of the feature detection operation 38 are identified in FIG. 6 by numeral 640 and the valleys by numeral 642.

[0046] To obtain readings of the peaks and valleys in the blood flow, the points 640 and 642 may be averaged over a predetermined time interval such as, for example, 10 seconds which, in FIG. 5, is referred to as parameter extraction (block 544). Control loops back, via path 546, to the input of block 520 where further raw data telemetered from the implanted sensor or sensors is inputted for processing.

[0047] Referring again to FIG. 5, the series of peaks 640 obtained as a result of the feature detection operation 538 are supplied as inputs to another curve-fitting algorithm (block 548) which functions to add additional points between the peaks 640, thereby creating a fitted curve between adjacent peaks in the blood flow versus time waveform. The thus curve-fitted peak waveform is labeled as such in FIG. 6. Once a third order curve has been fit to the data, the resulting waveform is tested for critical points based on a predetermined criteria (block 550). For example, the predetermined criteria in one embodiment defines critical points at maximum excursions above a zero-crossing and/or peaks and valleys in the curve-fitted waveform. A test is made at decision block 552 whether a critical point has been reached and, if not, control exits via path 554 to block 559 and further points are subjected to the curve-fitting algorithm at block 548.

[0048] When the test at decision block 552 indicates that a critical point has been reached, the operation performed at block 556 determines whether the critical point is a maximum or a minimum. By computing the interval between two adjacent maxima or two adjacent minima, the length of a respiratory cycle is obtained. The inverse of this cycle length is respiratory rate, measured in breaths per second. The interval is continuously computed as represented by block 558 to provide a current, real-time indication of respiration rate. Although respiratory rate is a parameter of value and is the parameter derived in this embodiment, other physiologic parameters can be derived from the curve-fitted waveform.

[0049] In another embodiment, at block 548, the stroke volume is calculated from the waveform of FIG. 6. Essentially, the stroke volume is a measure of the volume of blood output by the heart on a cardiac cycle. Thus, the stroke volume can be calculated by integrating the area under the blood velocity or volumetric blood flow curve over a cardiac cycle. Each of these integration operations results in a data point. FIG. 7 is a graph that illustrates a series of stroke volume data points that are derived from blood flow data. The respiration rate is derived from these data points using the technique shown in blocks 548 to 559 of FIG. 5. It is noted that the stroke volume can be calculated by other appropriate means, and, can be measured directly with a stroke volume probe.

Conclusion

[0050] It is seen that there is provided a method and apparatus to derive respiration rate and other physiologic parameters, either on-line or off-line from a continuous blood pressure, volumetric blood flow, blood velocity or stroke volume signals. In one embodiment, this is accomplished by way of a feature detection software algorithm. In addition to on-line processing, the same data can be post-processed to derive respiration rate, other respiratory parameters, or other information related to other physiologic parameters which may be contained in this data. Advantageously, data on more than one physiologic parameter, e.g., blood pressure and respiratory rate, may be obtained using only a single sensor implanted in the animal or human.

[0051] This invention has been described herein in considerable detail in order to comply with the Patent Statutes and to provide those skilled in the art with the information needed to apply the novel principles and to construct and use such specialized components as are required. However, it is to be understood that the invention can be carried out by specifically different equipment and devices, and that various modifications, both as to the equipment details and operating procedures, can be accomplished without departing from the scope of the invention itself. For example, the present invention is not limited to third order polynomials curves, as other nth order polynomials and other suitable mathematical models can be similarly fitted to data points according to the present invention. Likewise, it is not necessary that the blood pressure, volumetric blood flow, blood velocity, or stroke volume data be telemetered. A sensor could, for example, transmit a signal indicative of blood pressure via LEDs or wires to an amplifier and analog-to-digital converter, which provides a signal into a computer-based system.

[0052] When considering modifications to the disclosed embodiments, it is to be understood that the respiratory-related information is obtainable by performing peak detection directly on the feature data without curve fitting, but since the data may not always be ideally or equally spaced, it is difficult to resolve maximum and minimum points accurately as resolved by the present invention. Respiratory-related information is also obtainable by performing spectral analysis on the raw pressure or flow waveform, but since the pressure or flow data varies in frequency and amplitude, this approach is less than ideal in that it works best on truly periodic data. One could also signal process the data in accordance with the Barthelmes paper cited in the background section in which the raw blood pressure date is first high-pass filtered and then low-pass filtered. The Barthelmes approach, however, unlike the present invention, requires an optimal design of the filter and amplifier characteristics, be they digital or analog, to deal with a wide spectrum of data characteristics.

[0053] As to alternatives to the above described embodiments, one embodiment of the present invention performs spectral analysis on the curve fit data values, such as the third order fitted curve, instead of performing the peak detection described above. Another embodiment of the present invention may achieve a more accurate indication of the respiratory effect on blood pressure by curve fitting the averaged systolic and diastolic values or combining features or signals to yield a more robust estimate for each cardiac cycle before applying a peak detection to determine respiration rate. This approach is advantageous only when the systolic and diastolic curves remain completely in phase.

[0054] It is possible to combine selected features or signals in a number of ways to yield a more robust estimate of the respiratory parameter. For example, a confidence factor can be obtained by calculating respiratory information from two different signals, waveforms or data, e.g., blood pressure signal and volumetric blood flow signal, curve-fitted systolic and diastolic waveforms, etc. Further, in one embodiment, an averaging operation can be applied to the features extracted from two different signals to provide a set of features for use in calculating the respiratory parameter information.

[0055] Although specific embodiments have been illustrated and described herein for purposes of description of the preferred embodiment, it will be appreciated by those of ordinary skill in the art that a wide variety of alternate and/or equivalent implementations calculated to achieve the same purposes may be substituted for the specific embodiments shown and described without departing from the scope of the present invention. Those with skill in the mechanical, electro-mechanical, electrical, and computer arts will readily appreciate that the present invention may be implemented in a very wide variety of embodiments. This application is intended to cover any adaptations or variations of the preferred embodiments discussed herein.

Claims

1. A method of obtaining respiratory parameter information of an animal or human from a blood pressure signal indicative of sensed variations in blood pressure of the animal or human, the method comprising the steps of: signal processing, external from the animal or human, the blood pressure signal to develop an amplitude versus time waveform; extracting from the developed amplitude versus time waveform a sequence of selected blood pressure features derived from individual cardiac cycles of the amplitude versus time waveform over a selected time interval; fitting a mathematical model to the extracted sequence of selected blood pressure features to yield a fitted mathematical model; and computing the respiratory parameter information from the fitted mathematical model.

2. The method of claim 1 wherein the mathematical model is an nth order polynomial curve.

3. The method of claim 2 wherein n is at least equal to 3.

4. The method of claim 1 wherein the selected blood pressure features are selected from a group of blood pressure features including systolic data points, diastolic data points, and mean values of blood pressure for each cardiac cycle.

5. The method of claim 1 wherein the fitted mathematical model comprises curve fit data values substantially equal-spaced in time.

6. The method of claim 1 wherein the computing step includes the steps of: testing the fitted mathematical model for critical points in accordance with a predetermined criteria; determining from the critical points whether the critical points are a maximum or a minimum; and computing the respiratory parameter information from successive maximum and minimum critical points.

7. The method of claim 6 wherein the step of testing the fitted mathematical model for critical points includes locating peak values and zero-crossings of the fitted mathematical model.

8. The method of claim 1 wherein the respiratory parameter information is respiratory rate.

9. The method of claim 1 further comprising the steps of: sensing variations in blood pressure from the animal or human and providing an electrical signal indicative of the sensed variations in blood pressure; transmitting the electrical signal; receiving, external to the animal or human, the transmitted signal and providing the blood pressure signal which is proportional to the transmitted signal.

10. The method of claim 9 further comprising the step of surgically implanting a blood pressure sensor and a transmitter in the animals's or human's vascular system, the sensor sensing the variations in blood pressure from the animal or human and providing the electrical signal indicative of the sensed variations in blood pressure to the transmitter which transmits the electrical signal.

11. The method of claim 1 wherein the signal processing step is performed by digitally signal processing the blood pressure signal.

12. The method of claim 1 wherein the computing step comprises the step of performing spectral analysis on the fitted mathematical model.

13. An apparatus for obtaining respiratory parameter information of an animal or human from a blood pressure signal indicative of sensed variations in blood pressure of the animal or human, the apparatus comprising: an external signal processor for signal processing the blood pressure signal to develop an amplitude versus time waveform; extracting means for extracting from the developed amplitude versus time waveform a sequence of selected blood pressure features derived from individual cardiac cycles of the amplitude versus time waveform over a selected time interval; fitting means for fitting a mathematical model to the extracted sequence of selected blood pressure features to yield a fitted mathematical model; and computing means for computing the respiratory parameter information from the fitted mathematical model.

14. The apparatus of claim 13 wherein the mathematical model is an nth order polynomial curve.

15. The apparatus of claim 14 wherein n is at least equal to 3.

16. The apparatus of claim 13 wherein the selected blood pressure features are selected from a group of blood pressure features including systolic data points, diastolic data points, and mean values of blood pressure for each cardiac cycle.

17. The apparatus of claim 13 wherein the fitted mathematical model comprises curve fit data values substantially equal-spaced in time.

18. The apparatus of claim 13 wherein the computing means includes: means for testing the mathematical model for critical points in accordance with a predetermined criteria; means for determining from the critical points whether the critical points are a maximum or a minimum; and means for computing the respiratory parameter information from successive maximum and minimum critical points.

19. The apparatus of claim 18 wherein the means for testing includes means for locating peak values and zero-crossings of the fitted mathematical model.

20. The apparatus of claim 13 wherein the respiratory parameter information is respiratory rate.

21. The apparatus of claim 13 further comprising: a blood pressure sensor implantable in the animal or human to sense variations in blood pressure from the animal or human and provide an electrical signal indicative of the sensed variations in blood pressure; a transmitter implantable in the animal or human to receive the electrical signal and to transmit the electrical signal from the animal or human; and an external receiver for receiving the transmitted signal indicative of sensed variations in blood pressure of the animal or human and for providing the blood pressure signal proportional to the transmitted signal.

22. The apparatus of claim 13 wherein the external signal processor digitally signal processes the blood pressure signal.

23. The apparatus of claim 13 wherein the computing means includes means for performing spectral analysis on the fitted mathematical model.

24. A method of obtaining respiratory parameter information of an animal or human from a signal indicative of sensed variations in blood flow data of the animal or human, the method comprising: generating a signal that represents the blood flow data; extracting from the signal a sequence of selected features of the blood flow data over a selected time interval; fitting a mathematical model to the extracted sequence of selected features to yield a fitted mathematical model; and computing the respiratory parameter information from the fitted mathematical model.

25. The method of claim 24, and further comprising processing the signal to develop an amplitude versus time waveform prior to extracting the sequence of selected features.

26. The method of claim 24, wherein generating a signal comprises generating a signal that represents blood velocity data of the animal or human.

27. The method of claim 24, wherein the selected blood flow features are selected from a group of blood flow features including peak flow, minimum flow, mean flow and stroke volume for each cardiac cycle.

28. The method of claim 24, and further comprising: extracting from a blood pressure signal a sequence of selected blood pressure features derived from individual cardiac cycles of an amplitude versus time waveform over a selected time interval; fitting a mathematical model to the extracted sequence of selected blood pressure features to yield a fitted mathematical model; computing the respiratory parameter information from the fitted mathematical model; and correlating the respiratory parameter information computed from the blood pressure features with the respiratory parameter information computed from the blood flow features.

29. The method of claim 24, wherein generating a signal comprises generating a signal that represents the volumetric blood flow of the animal or human.

30. The method of claim 24, wherein the fitted mathematical model comprises curve fit data values substantially equal-spaced in time.

31. The method of claim 24, wherein computing the respiratory parameter information comprises: testing the fitted mathematical model for critical points in accordance with a predetermined criteria; determining from the critical points whether the critical points are a maximum or a minimum; and computing the respiratory parameter information from successive maximum and minimum critical points.

32. The method of claim 31, wherein testing the fitted mathematical model for critical points includes locating peak values and zero-crossings of the fitted mathematical model.

33. The method of claim 24, and further comprising: sensing variations in the blood flow data from the animal or human and providing a signal indicative of the sensed variations in the blood flow data; transmitting the signal; and receiving, external to the animal or human, the transmitted signal.

34. The method of claim 24, wherein computing the respiratory parameter information comprises performing spectral analysis on the fitted mathematical model.

35. An apparatus that obtains respiratory parameter information of an animal or human from a signal indicative of sensed variations in blood flow data of the animal or human, the apparatus comprising: a sensor that generates a signal that represents the blood flow data; and a data processing apparatus, communicatively coupled to the sensor, that extracts from the signal a sequence of selected features of the blood flow data over a selected time interval, fits a mathematical model to the extracted sequence of selected features to yield a fitted mathematical model, and computes the respiratory parameter information from the fitted mathematical model.

36. The apparatus of claim 35, wherein the sensor comprises a blood flow sensor that uses Doppler ultrasound, transit time ultrasound or laser Doppler to generate the signal.

37. The apparatus of claim 35, wherein the data processing apparatus further generates a stroke volume waveform from the output of the sensor.

38. The apparatus of claim 35, and further comprising a transmitter coupled to the sensor and a receiver coupled to the data processing apparatus so as to communicatively couple the sensor to the data processing device.

39. A method of obtaining respiratory parameter information of an animal or human from a blood pressure signal indicative of sensed variations in blood pressure of the animal or human, the method comprising: extracting from the blood pressure signal a sequence of selected blood pressure features derived from individual cardiac cycles of the amplitude versus time waveform over a selected time interval; fitting a mathematical model to the extracted sequence of selected blood pressure features to yield a fitted mathematical model; and computing the respiratory parameter information from the fitted mathematical model.

40. The method of claim 39, wherein the mathematical model is an nth order polynomial curve.

41. The method of claim 39, wherein the selected blood pressure features are selected from a group of blood pressure features including systolic data points, diastolic data points, and mean values of blood pressure for each cardiac cycle.

42. The method of claim 39, wherein the fitted mathematical model comprises curve fit data values substantially equal-spaced in time.

43. The method of claim 39, wherein computing the respiratory parameter comprises: testing the fitted mathematical model for critical points in accordance with a predetermined criteria; determining from the critical points whether the critical points are a maximum or a minimum; and computing the respiratory parameter information from successive maximum and minimum critical points.

44. The method of claim 43, wherein testing the fitted mathematical model for critical points includes locating peak values and zero-crossings of the fitted mathematical model.

45. The method of claim 39, and further comprising: sensing variations in blood pressure from the animal or human and providing an electrical signal indicative of the sensed variations in blood pressure; transmitting the electrical signal; and receiving, external to the animal or human, the transmitted signal and providing the blood pressure signal which is proportional to the transmitted signal.

46. The method of claim 39, wherein computing the respiratory parameter information comprises performing spectral analysis on the fitted mathematical model.

47. The method of claim 39, and further including signal processing the blood pressure signal to develop an amplitude versus time waveform.

48. A method of obtaining respiratory parameter information of an animal or human, from a sequence of stroke volume features of the animal or human, the method comprising: fitting a mathematical model of the sequence of features to yield a fitted mathematical model; and computing the respiratory parameter information from the fitted mathematical model.

49. The method of claim 48, wherein the stroke volume features are measured directly with a stroke volume sensor.