System and method for extracting physiological data using ultra-wideband radar and improved signal processing techniques

• US 20040249258A1
• Filed: 06/04/2003
• Published: 12/09/2004
• Est. Priority Date: 06/04/2003
• Status: Active Grant

• Current Assignee: LifeWave Biomedical, Inc.
• Original Assignee: LifeWave, Inc.
• Inventors: Rollins, Richard Jensen, Van Rooyen, Robert Martinez, Tupin, Joe Paul Jr.
• Granted Patent: US 7,725,150 B2
• Time in Patent Office: Days
• US Class Current: 600/407
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/05 Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves impedance plethysmography A61B5/0295; measuring movement of the entire body or parts thereof A61B5/11; detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof A61B5/24
• CPC Class Codes: A61B 5/0507 using microwaves or terahertz waves
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: G01S 13/0209 Systems with very large relative bandwidth, i.e. larger than 10 %, e.g. baseband, pulse, carrier-free, ultrawideband

Abstract

Disclosed is a variant of ultra-wide band (UWB) radar known as micropower impulse radar (MIR) combined with advanced signal processing techniques to provide a new type of medical imaging technology including frequency spectrum analysis and modern statistical filtering techniques to search for, acquire, track, or interrogate physiological data. Range gate settings are controlled to depths of interest within a patient and those settings are dynamically adjusted to optimize the physiological signals desired.

Claims

1. In a device employing ultra wideband radar return signals for extracting physiological data from one or more bodily organs or physiological processes of a patient, said device having an electronically controlled range gate, the process of extracting accurate physiological data including the steps of employing frequency spectrum analysis and statistical filtering techniques in extracting said data.

2. The process of claim 1 further including the steps of searching for, acquiring, tracking, and interrogating one or more of said bodily organs or physiological processes by electronically controlling range gate settings of said device to depths of interest within the patient and dynamically adjusting those settings to optimize desired ones of said physiological data.

3. The process of claim 2 including the range gate mode step of holding the range gate at one or more of said depths while extracting said physiological data.

4. The process of claim 2 including the range finder mode step of sweeping the range gate across a range of said depths while extracting said physiological data.

5. The process of claim 2 including the range finder mode step of electronically stepping the range gate through a series of said depths while extracting said physiological data.

6. The process of claims 3, 4 or 5 including the steps of selecting the method of operation of the range gate and controlling said gate by one or more control algorithms employing as a reference a statistical model of one or more of said bodily organs or physiological processes, and comparing said reference to incoming radar return signals to determine the optimal range gate selection for desired physiological data extraction.

7. In a device employing ultra wideband radar return signals for extracting physiological data from one or more bodily organs or physiological processes of a patient, the process of electronically fixing a sample depth within the body from which physiological data are to be extracted and collecting a number of samples at said sample depth, said process including the steps of employing frequency spectrum analysis and statistical filtering techniques in extracting said data.

8. The process of claim 7 including the further steps of, after said collecting, electronically changing said sample depth and collecting a number of samples at said changed sample depth.

9. In a device employing ultra wideband radar return signals for extracting physiological data from one or more bodily organs or physiological processes of a patient, the process of electronically selecting a plurality of sample depths within the body corresponding to the area for which physiological data are to be extracted and collecting data samples at a plurality of said plurality of sample depths.

10. The process of claim 9 including the steps of collecting said data samples at discrete ones of said plurality of sample depths.

11. The process of claim 9 including the steps of collecting said data samples at continuously varying ones of said plurality of sample depths.

110. The device of claim 10 further including a static reflection suppression device and an anti-aliasing filter.

12. A device employing ultra wideband radar return signals for extracting physiological data from a patient, the device including a pulse generator and a range delay circuit coupled to said generator for receiving a pulse train therefrom, said range delay circuit including a digital to analog converter coupled to (a) at least one operational amplifier and (b) at least one single-shot multivibrator, said at least one single-shot multivibrator receiving at least (i) a trigger signal for causing the single-shot multivibrator to initiate a single pulse cycle for substantially every pulse in the pulse train and (ii) a hold-off signal to determine the period of the pulses in the pulse cycle.

13. In a device employing ultra wideband radar return signals for extracting physiological data from a patient, the combination of (a) a pulse repetition frequency generator for providing a pulse train coupled to a transmitter and a receiver, said pulse repetition generator including a voltage controlled oscillator having at least one control port as the source of the pulse train, and (b) a source of dither signals coupled to the output of the pulse repetition frequency generator to inject randomized noise signal into said at least one control port to cause spectral spreading.

14. The device of claim 13 wherein said dither signals provide a unique transmit coding pattern for said device.

15. A device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient, the device including a pulse repetition frequency generator for providing a pulse train coupled to a transmitter and a receiver, each of said transmitter and receiver coupled to one or more antennas, said transmitter transmitting pulse signals to said patient and said receiver receiving return signals from said patient, said receiver connected to an analog to digital converter and further including dual channels and a sample and hold circuit coupled to each channel, each said sample and hold circuits triggered by the pulse repetition frequency generator, said circuits coupled to integrator elements and including a range compensated gain circuit including blanking to eliminate undesired ones of said return signals.

16. The device of claim 15 further including feed-forward circuitry including an amplifier coupled to a low-pass filter producing a filtered signal for removing from the output of said amplifier frequency components related to movement of said one or more organs, said low-pass filter coupled as an input to a subtractor circuit having as one input the output of said amplifier, said subtractor element subtracting said filtered signal from said output.

17. The device of claim 15 including anti-aliasing circuitry and amplification circuitry prior to said analog to digital converter.

18. The device of claim 17 wherein said anti-aliasing circuitry includes circuitry including circuit elements exhibiting electrical characteristics of a Butterworth filter, a Bessel filter, a Chebychev filter or an elliptic filter.

19. The device of claim 15 wherein said analog to digital converter is coupled to a signal processor component and operates on the analog signal from said receiver to provide a digitized signal, said digitized signal being processed by said signal processor component to extract pertinent physiological data.

20. The device of claim 19 wherein said signal processor component converts said digitized signal to a format which is (a) tracings of amplitude versus time for at least some of said one or more depths of interest;

(b) power spectral density for at least some of said one or more depths of interest;

(c) time domain histograms for at least some of said one or more depths of interest;

(d) frequency domain histograms for at least some of said one or more depths of interest;

(e) a numerical value for at least one physiological function;

or (f) confidence factors for one or more of the said formats.

21. The device of claim 20 wherein said converted digitized signal is displayed on a display device.

151. The device of claim 15 further including a feedback filter producing a filtered signal for removing from the output of said amplifier frequency components related to movement of said one or more organs, said feedback filter coupled as an input to a subtractor circuit having as one input the output of said amplifier, said subtractor element subtracting said filtered signal from said output.

152. The device of claim 20 where said at least one physiological function is heart rate or lung rate.

22. In a device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient, said device capable of initiating radar sweep cycles, said device including

a. a pulse repetition frequency generator coupled to a transmitter and receiver for providing a pulse train thereto, each of said transmitter and receiver coupled to one or more antennas, said transmitter transmitting pulse signals to said patient, and

b. a receiver receiving return signals in analog form from said patient,

said device including a signal processor component and an analog to digital converter for converting said return signals to digital form for processing said return signals for presentation to a user, the processing including the steps of

i. preprocessing and digitizing said analog return signals to provide enhanced, digitized return signals, and

ii. extracting from said ditigized signals one or more signals representing desired physiological data including a sequence of values and confidence measures.

23. The process of claim 22 further including the step of said receiver operating on said analog return signals by suppressing certain high-strength return signals and amplifying signals representing motion within said patient.

24. The process of claim 23 wherein the preprocessing step includes low-pass filtering said suppressed and amplified analog return signals, said low pass filtering passing frequencies up to 5 Hz, and minimizing the potential of aliasing prior to said digitizing step.

25. The process of claim 23 wherein the preprocessing step includes low-pass filtering said suppressed and amplified analog return signals, said low pass filtering passing frequencies up to 200 Hz, and minimizing the potential of aliasing prior to said digitizing step.

26. The process of claim 25 wherein said low-pass filtering is accomplished by passing said suppressed and amplified return signals through a filter circuit including circuit elements exhibiting electrical characteristics of a Butterworth filter, a Bessel filter, a Chebychev filter and an elliptic filter.

27. The process of claim 24 wherein the step of low pass filtering includes the steps of filtering said return signals to produce filtered signals and sampling said filtered signals, said filter having filter parameters and said sampling being at a sampling rate such that the amplitude of said return signals at frequencies equal to or greater than half the sample rate are less than the minimum amplitude detectable by said analog to digital converter.

28. The process of claim 25 wherein said step of low-pass filtering includes the steps of filtering said return signals to produce filtered signals, and sampling said filtered signals, said filter having filter parameters and said sampling being at a sampling rate such that the amplitude of said return signals at frequencies equal to or greater than half the sample rate is less than the minimum amplitude detectable by said analog to digital converter.

29. The process of claim 24 wherein the digitizing step includes the step of converting a return signal from analog to digital in said analog-to-digital converter, said converter accepting analog input signals and producing digital output signals, to translate said return signals into a series of discrete numerical values representing the amplitudes of said return signals to provide an input signal for the extracting step.

30. The process of claim 29 wherein said analog-to-digital converter is synchronized with (a) changes in depth in both said range gate mode and said range finder mode and (b) initiation of a new one of said sweep cycles in the case of said range finder mode.

31. The process of claim 30 wherein some or all of the output of said analog-to-digital converter is stored in storage in a two-dimensional matrix of time-sampled return signal values.

32. The process of claim 31 including the step of reducing data from said output prior to said output being stored in said matrix.

33. The process of claim 31 including the step of reducing data stored in said two-dimensional matrix after said data is stored in said matrix.

34. The process of claim 31 wherein the two-dimensional matrix stores (a) ones of said output signals representing return signals collected at a particular time sample interval with respect to the synchronization signal and (b) ones of said output signals representing return signals collected from a depth within the patient.

35. The process of claim 31 wherein the depth of said one or more depths within the patient from which said return signals are collected is fixed and a number of samples of said return signals are collected at said fixed depth over a period of time.

36. The process of claim 35 wherein said two-dimensional matrix is filled by row ordering.

37. The process of claim 31 wherein the depth of said one or more depths within the patient from which return signals are collected is varied over a finite range of said depths of interest and samples of said return signals are collected at a plurality of said depths.

38. The process of claim 37 including the step of collecting said data samples at one of said one or more depths of interest.

39. The process of claim 37 including the step of collecting said data samples at continuously varying ones of said one or more depths of interest.

40. The process of claim 37 wherein said two-dimensional matrix is filled by column ordering.

41. The process of claim 29 wherein said converting step includes the step of collecting sub-samples of said return signals from said samples to reduce the volume of time-sampled data.

42. The process of claim 41 including the step of collecting said sub-samples and subsequently loading them into a two-dimensional matrix in computer readable storage.

43. The process of claim 41 including the step of loading said samples into a two-dimensional matrix in storage and subsequently collecting said sub-samples from said samples.

44. The process of claim 29 wherein the converting step includes the step of coarse quantizing to increase contrast and reduce computational complexity of said return signals.

45. The process of claim 29 wherein the converting step includes the step of normalizing to maximize the dynamic range of said return signals.

46. The process of claim 22 wherein said extracting step includes the step of converting the digitized return signals into signals representing useful physiological data.

47. The process of claim 46 wherein said useful physiological data represent cardiopulmonary data.

48. The process of claim 47 wherein said useful cardiopulmonary data includes one or more information signals selected from the group of information signals representing rate, rhythm, cardiac chamber volume, cardiac stroke volume, cardiac ejection fraction, and cardiac output.

49. The process of claim 22 wherein the extracting step includes operating on files of said return signals collected at a time earlier than the time of said step of operating.

50. The process of claim 22 wherein the extracting step includes operating on continuous ones of said return signals in real time.

51. The process of claim 22 wherein the extracting step includes collecting return signals from a particular one of said one or more depths of interest within a patient.

52. The process of claim 22 wherein the extracting step includes the step of transforming time sampled return signals to the frequency domain for spectral processing.

53. The process of claim 52 wherein said transforming step includes employing a Fast Fourier Transform.

54. The process of claim 52 wherein said transforming step includes employing a discrete cosine transform.

55. The process of claim 52 wherein the transforming step includes employing a discrete filter bank.

56. The process of claim 54 or 55 wherein the result of said transforming step is a two dimensional frequency domain reflection signal matrix stored in computer readable storage, one dimension of said matrix including cells containing coefficients of frequency signals the real value of which corresponds to the amplitude of the frequency coefficient of one of said return signals at one of said one or more of said depths of interest within said patient.

57. The process of claim 53 wherein the result of said transforming step is a two dimensional frequency domain reflection signal matrix in computer readable storage, one dimension of said matrix including cells containing coefficients of frequency signals the complex value of which corresponds to the real and imaginary components of the frequency coefficient of one of said return signals at one or more of said depths of interest within said patient.

58. The process of claim 53, 54 or 55 wherein said transforming is controlled by a focuser to prohibit transforming ones of said return signals received from predetermined depths within said patient.

59. The process of claim 22 wherein the extracting step includes operating on a frequency domain reflection signal matrix and estimating an approximate value for said physiological data.

60. The process of claim 59 wherein said estimating an approximate value includes obtaining maximum data value in each row of said frequency domain reflection signal matrix by determining the highest amplitude frequency components of return signals for a plurality of depths of said one or more depths of interest within said patient.

61. The process of claim 60 wherein said data peaks are in the form of a two-dimensional matrix of signal entries stored in computer readable storage, said matrix containing a pair of values for return signals from at least some of said one or more depths of interest within said patient.

62. The process of claim 61 including the further step of filtering the signal entries in said two-dimensional matrix to remove those entries where the frequency value lies outside an expected range of frequencies.

63. The process of claim 62 wherein said filtering step includes applying a bandpass filter with a pass band from 0.5 Hz to 5 Hz to the frequency values in said two-dimensional matrix for applications related to cardiac functions.

64. The process of claim 61 including the steps of filtering the signal entries in said two-dimensional matrix by applying multiple filters with different filtering characteristics to said signal entries to estimate more than one type of physiological data.

65. The process of claim 22 wherein said extracting step includes the step of employing a modeler component to adapt one or more models from a data base containing one or more filter models representing a single cycle of physiological data, into a matched filter for subsequent cross-correlation with certain of said return signals.

66. The process of claim 65 wherein said return signals for correlation are time-based return signals.

67. The process of claim 65 wherein said return signals for correlation are frequency-based return signals.

68. The process of claim 65 wherein one of said one or more models is obtained by self-convolving a single cycle input pattern that models an expected extracted physiological data signal to produce a matched filter for said physiological data signal.

69. The process of claim 68 wherein said input patterns are sinusoids, patterns developed through theoretical studies of expected return signals, or actual patterns from individual patients.

70. The process of claim 69 wherein said input patterns include patterns developed from signals representing normal physiological data and patterns developed from signals representing abnormal physiological data.

71. The process of claim 70 wherein said abnormal physiological data include data corresponding to bradycardia, tachycardia and fibrillation.

72. The process of claim 22 wherein said extracting step includes using matched filters and correlation to calculate an array of numerical rate values and associated confidence factors for said physiological data.

73. The process of claim 65 including the additional step of cross correlating one of said one or more filter models from said modeler component with an original return signal matrix to produce a series of correlation coefficient vectors with one vector for each of said one or more depths per cross-correlated filter model.

74. The process of claim 73 wherein said return signal matrix is a time-based return signal matrix.

75. The process of claim 73 wherein said return signal matrix is a frequency-based return signal matrix.

76. The process of claim 73 wherein each of said vectors is DC-filtered to remove common bias.

77. The process of claim 76 wherein each said DC-filtered vector is operated on by a peak detector component, the maximum data value obtained by said circuit for each said vector representing the degree of fit of the cross correlated filter model to the time-based physiological data, said degree of fit representing the accuracy of the frequency estimate to the actual frequency of said return signals, the output of said peak detector component being a vector representing a set of confidence metrics, the members of said set being the confidence factors for each return signal from said one or more depths within said patient at which said return signals are collected.

78. The process of claim 73 wherein said additional step is repeated for a plurality of said one or more models.

79. The process of claims 59 and 72 including the additional step of operating on said sequence of values and said confidence factors to produce a single best fit measure and corresponding confidence factor for each of said return signals from said one or more organs or physiological processes.

80. The process of claim 79 wherein said step of producing a single best fit measure includes (i) choosing the approximate value with the single largest amplitude confidence factor, or (ii) choosing return signals from one of said one or more depths within said patient, said one depth generally providing return signals with generally high amplitude confidence factors.

81. The process of claim 72 further including an analyzing step including the step of processing said time-ordered sequences of said numerical rate values and associated confidence factors to determine problem trends in said numeral rate values.

82. The process of claim 81 wherein said analyzing step includes (i) detecting excursions in said rate values beyond appropriate ranges, (ii) detecting deviations of said rate values from expected patterns, (iii) performing a time series analysis of said sequence of said rate values in which the next incremental value in said sequence is predicted from one or more past values thereof and calculating the difference between said next incremental value and said predicted value and using said difference as the first order error term, and detecting excursions in said error terms beyond appropriate ranges, (iv) performing a time series analysis based on higher-order error terms by extending step (iii) to the derivation of second, third, fourth, and higher error terms, or (v) matching said sequence of rate values with a known problem pattern by cross correlating said sequence against a known problem pattern.

83. The process of claim 82 wherein said sequence of rate values is cross-correlated against a pattern for bradycardia.

84. The process of claim 82 wherein said sequence of rate values is cross-correlated against a pattern for tachycardia.

85. The process of claim 82 wherein said sequence of rate values is cross-correlated against a pattern for fibrillation.

86. The process of claim 79 further including a step of determining whether multiple groups of pairs of said values and confidence factors are being selected because more than one set of physiological data are being analyzed simultaneously and, as a result of said determining step, processing each of said pairs of values and confidence factors independently and using the values in one of said group of said pairs to analyze another of said group of said pairs.

87. The process of claim 22 wherein including the further step of applying a control process that includes changing the depths of said one or more depths at which return signals are collected by using a depth range of interest indicator within a matrix of said return signals and processing only those entries in said matrix within the depth range of interest.

88. The process of claim 22 including the further step of applying a control process that includes the step of varying the amount of processing performed on said return signals from each of said one or more depths on each operation cycle of said device by (a) correlating, for subsequent cross-correlation with certain of said return signals, only one of a plurality of models from a data base of said models to conserve computation resources, or (b) correlating many of said plurality of models to seek broadly for a best-fit model.

134. The process of claim 53 wherein the result of said transforming step is a two dimensional frequency domain reflection signal matrix in computer readable storage, one dimension of said matrix including cells containing coefficients of frequency signals the complex value of which corresponds to the amplitude and phase of the frequency coefficient of one of said return signals at one or more of said depths of interest within said patient.

135. The process of claim 61 wherein said pair of values comprises (1) the frequency of the signal having the maximum frequency coefficient and (2) the value of said frequency coefficient.

140. The process of claim 22 wherein said sequence of values is time-ordered.

145. The process of claim 22 wherein said extracting step includes the steps of employing a modeler component to adapt a plurality of models from a data base containing a plurality of filter models representing physiological data into a plurality of matched filters and cross-correlating said plurality of matched filters from said modeler component with a matrix of said return signals to produce a series of correlation coefficient vectors with one vector for each of said one or more depths per cross-correlated filter model.

146. The process of claim 22 wherein said extracting step includes the step of storing a plurality of digitized ones of said return signals from said analog to digital converter in a time domain reflection matrix comprising a plurality of rows of storage, and subtracting each of a plurality of said digitized ones of said return signals from a representative signal of said plurality of digitized ones of said return signals to obtain specific optimized data signals.

147. The process of claim 146 wherein said representative signal is stored in the first row of said time domain reflection matrix.

148. The process of claim 146 wherein said representative signal is the average value of each amplitude from each of said rows of said time domain reflection matrix.

149. The process of claim 146 wherein said plurality of rows comprises at least eight rows and said representative signal is the average of each amplitude from each of the first eight rows of said matrix.

150. The process of claim 146 wherein said plurality of rows comprises at least eight rows and said representative signal is the average of each amplitude from each of the immediately preceding eight rows of said matrix.

89. Electronic radar return signals embodied in processor readable storage, said signals representing a physiological process and presented as a two-dimensional graph of time-domain return signals acquired by a device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient, said device capable of performing the process steps of using statistical models of expected return signals from said one or more areas for collecting signal samples at various of said depths.

90. The electronic radar return signals of claim 89 wherein a first of said dimensions represents said depths on a vertical axis, and a second of said dimensions represents a number of samples taken at said depths on a horizontal axis, and strength of said return signals is represented as a color intensity ranging across a color spectrum.

91. The electronic radar return signals of claim 90 wherein said color spectrum ranges from a first color to one or more second colors to provide sufficient color discrimination, said color discrimination being hue, saturation or luminance.

92. The electronic radar return signals of claim 90, said samples being collected over a time period that is greater than half of the shortest expected cycle period of said physiological process.

136. The electronic radar return signals of claim 89 wherein said process steps collect said signal samples from various depths of interest within said patient from front to back in the patient'"'"'s body.

137. The electronic radar return signals of claim 89 wherein said process steps collect said signal samples from various depths of interest within said patient from back to front in the patient'"'"'s body.

138. The electronic radar return signals of claim 89 wherein said process steps collect said signal samples from various depths of interest within said patient by randomly varying said depths across a predetermined range of depths.

93. Electronic radar return signals embodied in processor readable storage representing time-domain representations of radar return signals acquired by a device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient by using statistical models of expected ones of said return signals from at least some of said one or more depths, said radar return signals in a time domain operated on by a Fast Fourier Transform to transform said signals into a frequency domain, said electronic signals represented in two-dimensional graphical form wherein depth is represented on a vertical axis, frequency on a horizontal axis, and amplitude is mapped as a color intensity.

94. The electronic radar return signals of claim 93 wherein said signals include a representation of color luminance and said color luminance ranges from black to white across a spectrum of a color other than black and white.

95. The electronic radar return signals of claim 94 wherein amplitude refers to the strength of the corresponding frequency component in the time-domain signal.

96. Electronic radar return signals embodied in processor readable storage, said signals representing a physiological process and presented as a two-dimensional graph of time-domain return signals acquired by a device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient by using statistical models of expected ones of said return signals from at least some of said one or more depths, wherein said two dimensional graph represents frequency in a first color trace and amplitude in a second color trace of the primary tone per depth for each of a plurality of said one or more depths.

97. The electronic radar return signals of claim 96 wherein said primary tone is the frequency with the greatest amplitude at each said depths.

98. The electronic radar return signals of claim 97 wherein depth is represented on a horizontal axis and the frequency and amplitude on a vertical axis.

99. The electronic radar return signals of claim 98 further including a signal indicating the depth of said one or more depths having the highest correlation between the frequency data of said return signals and a statistical model of one or more of said bodily organs or physiological processes.

100. The electronic radar return signals of claim 99 wherein said statistical model is the model of a cardiac waveform.

101. The electronic radar return signals of claim 100 further including an electronic signal indicating the range in which said physiological data are valid.

102. The electronic radar return signals of claim 101 wherein said physiological data represent cardiac data and said range is 30 to 300 beats per minute.

103. The electronic radar return signals of claim 102 further including an electronic signal indicating the current data file name along with its attributes.

104. The electronic radar return signals of claim 103 wherein said attributes include the number of discrete depths at which data was collected and stored in the frequency domain reflection matrix, the number of samples taken at each of said discrete depths, and the sample rate in Hertz.

105. The electronic radar return signals of claim 96 further including an electronic signal indicating the distribution of primary tones for cardiac data for a plurality of said depths.

106. A graphical user interface embodied in processor readable storage, said graphical user interface capable of being displayed on a display device and including electronic radar return signals embodied in processor readable storage, said return signals representing a physiological process and presented as a two-dimensional graph of time-domain signals acquired by a device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient, by using statistical models of expected ones of said return signals in collecting signal samples at various ones of said one or more depths.

107. A graphical user interface embodied in processor readable storage, said graphical user interface capable of being displayed on a monitor, and including electronic radar return signals embodied in processor readable storage representing time-domain representations of radar return signals acquired by a device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient, by using statistical models of expected ones of said return signals in collecting signal samples at various ones of said one or more depths, said radar return signals in a time domain being operated on by a Fast Fourier Transform to transform said signals into a frequency domain, said electronic signals represented in two-dimensional graphical form wherein depth is represented on a vertical axis, frequency is represented on a horizontal axis, and amplitude is represented as a color intensity.

108. A graphical user interface embodied in processor readable storage, said graphical user interface capable of being displayed on a display device, and including electronic radar return signals embodied in processor readable storage, said return signals representing a physiological process and presented as a two-dimensional graph of time-domain return signals acquired by a device employing ultra wideband radar return signals for extracting physiological data from one or more organs or other physiological processes of a patient at one or more depths of interest within said patient, by using statistical models of expected ones of said return signals in collecting signal samples at various ones of said one or more depths, wherein said two dimensional graph represents frequency in a first color trace and amplitude in a second color trace of the primary tone per depth, for each of a plurality of said one or more depths.

109. In a device employing ultra wideband radar return signals for extracting physiological data from one or more areas of a patient, the process of extracting accurate physiological data including the step of using statistical models of expected return signals from said one or more areas.

111. In a device employing ultra wideband radar return signals for extracting physiological data from one or more depths in a patient as reflected signals, by using statistical models of expected ones of said return signals in collecting signal samples at various ones of said one or more depths, the process of operating on digitized reflected signals in a time domain reflection matrix by a reducer, said process including one or more of the steps of (a) sub-sampling said signals to reduce the volume of said signals, (b) coarsely quantizing said reduced volume to increase contrast and reduce the computational complexity thereof, and (c) normalizing said reduced volume to maximize dynamic range to produce a second two-dimensional matrix containing enhanced time-sampled reflected signals.

112. The process of claim 111 including the further step of converting said enhanced time-sampled reflected signals to signals in the frequency domain for spectral processing.

113. The process of claim 112 wherein said converting step includes the steps of employing a Fast Fourier transformation algorithm.

114. The process of claim 112 wherein said converting step includes the step of employing a Discrete Cosine Transform.

115. The process of claim 112 wherein said converting step includes the step of employing a Discrete Filter Bank.

116. The process of claims 113, 114 or 115 wherein said signals in the frequency domain are in a matrix wherein each cell of said matrix corresponds to the frequency coefficient at a given one of said one or more depths.

117. The process of claim 116 wherein the converting step includes employing a Fast Fourier Transformer and the value of each cell is a complex coefficient having a real and imaginary components.

118. The process of claim 117 including the steps of operating on said real and imaginary components by employing trigonometric identities to convert said components to signals representing magnitude and phase.

119. A plurality of signals stored in two dimensional storage said stored signals representing signals reflected from a depth of a patient by a device employing ultra wideband radar return signals for extracting physiological data from one or more depths in a patient as reflected signals, by using statistical models of expected ones of said return signals in collecting signal samples at various ones of said one or more depths, by using statistical models of expected ones of said return signals in collecting signal samples at various ones of said one or more depths, said stored signals being in the frequency domain wherein the stored signals in one dimension of said storage represents at least one of said one or more depths and the stored signals in another dimension of said storage represents the frequency of said reflected signal at said at least one other of said one or more depths.

120. In a device employing ultra wideband radar return signals for extracting data from one or more physiological processes being measured, from one or more depths in a patient as reflected signals, by using statistical models of expected ones of said return signals in collecting signal samples at various ones of said one or more depths, the process of operating on signals stored in two-dimensional storage in a frequency domain matrix by an estimator to derive a set of signals representing approximate values for said physiological data for a plurality of said one or more depths.

121. The process of claim 120 wherein said set of signals is operated on by a modeler algorithm to adapt one or more matched filters from a database including a plurality of matched filters, each of said one or more matched filters developed by self-convolving one of a plurality of single cycle patterns of data representing extracted physiological data to produce a matched filter for said one of said plurality of single cycle patterns.

122. The process of claim 121 wherein the step of convolving is the step of generating a matched filter MF(n) from a discrete one of said plurality of patterns of length N, said pattern designated by P(n), by the discrete convolution algorithm:

123. The process of claims 121 and 122 wherein said database includes filters representing normal ones of said patterns and abnormal ones of said plurality of patterns resulting from a plurality of ailments.

124. The process of claim 121 including the steps of:

(a) receiving from said estimator the estimates for the i-th physiological process being measured where i ranges from 1 to said number of physiological processes being measured;

(b) selecting matched filters from said database;

(c) receiving feedback information indicating which of said filters of said database have been providing the best results;

(d) analyzing said feedback information and, based on said analyzing step, eliminating one or more of said matched filters in said database from consideration to select a set of matched filters; and

(e) customizing said set of matched filters.

125. The process of claim 124 including the further step of forwarding said set for correlation.

126. The process of claim 124 including the further steps of repeating steps (a) through (e) for each physiological process being measured.

127. The process of claim 124 wherein said customizing includes the steps of (a) operating on said set of filters generate a first-order estimate of a cardiac rate for each depth in the vector and (b) adapting each of said filters of said database to the estimated rate through expansion or contraction of the period of each of said plurality of single cycle patterns.

128. The process of claim 127 wherein said one of said matched filters is based on a half cycle sinusoid with a nominal period of 1 second.

129. The process of claim 121 including the steps of calculating a matrix of correlation coefficients for measurements of a physiological process of said one or more physiological processes by cross-correlating a set of filter models with a matrix of signals representing return signals to produce a series of correlation coefficient matrices with one matrix for each matched filter model set.

130. The process of claim 129 wherein said matrix of signals is a time-based matrix.

131. The process of claim 129 wherein said matrix of signals is a frequency-based matrix.

132. The process of claim 129 wherein Di is the i-th depth of said one or more depths, said cross-correlation of a single one of said matched filters MF(n) of length N and the return signal from depth Di of length M is given by the following formula:

133. The process of claim 132 including the steps of DC filtering each row of each matrix in said series of correlation coefficient matrices to remove common bias;

and passing signals representing said filtered rows through a peak detector, the output of said peak detector being a matrix of signals wherein the peak value of said signal in each row of said matrix represents the degree of fit of said matched filter to said return signal.

139. In a device employing ultra wideband radar return signals for extracting physiological data from one or more depths in a patient as reflected signals, by using statistical models of expected ones of said return signals in collecting signal samples at various ones of said one or more depths, the process of operating on signals stored in two-dimensional storage in a frequency domain matrix by an estimator to derive a set of signals representing approximate values for said physiological data for a plurality of said one or more depths.

141. In a device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient, said device capable of initiating radar sweep cycles, said device including

a. a pulse repetition frequency generator coupled to a transmitter and receiver for providing a pulse train thereto, each of said transmitter and receiver coupled to one or more antennas, said transmitter transmitting pulse signals to said patient, and

b. a receiver receiving return signals in analog form from said patient,

said device including a signal processor component and an analog to digital converter for converting said return signals to digital form for processing said return signals for presentation to a user, the processing including the steps of

i. preprocessing and digitizing said analog return signals to provide enhanced, digitized return signals,

ii. extracting from said digitized signals one or more signals representing desired physiological data including a sequence of values and confidence measures,

iii. analyzing said values and confidence measures to detect signals representing problematic medical trends in said values, and

iv. applying a control process to one or more of the steps of preprocessing, digitizing and extracting to modify the amount and types of processing performed within the device.

142. The process of claim 141 wherein said extracting step includes using matched filters and correlation to calculate an array of numerical rate values and associated confidence factors for said physiological data and said analyzing step includes the step of processing said time-ordered sequences of said numerical rate values and associated confidence factors to determine problem trends in said numeral rate values.

143. The process of claim 142 wherein said analyzing step includes (i) detecting excursions in said rate values beyond appropriate ranges, (ii) detecting deviations of said rate values from expected patterns, (iii) performing a time series analysis of said sequence of said rate values in which the next incremental value in said sequences is predicted from one or more past values thereof and calculating the difference between said next incremental value and said predicted value and using said difference as the first order error term and detecting excursions in said error terms beyond appropriate ranges, (iv) performing a time series analysis based on higher-order error terms by extending step (iii) to the derivation of second, third, fourth, and higher error terms, or (v) matching said sequence of rate values with a known problem pattern by cross correlating said sequence against a known problem pattern.

144. The process of claim 141 wherein the step of applying a control process includes the step of varying the amount of processing performed on said return signals from each of said one or more depths on each operation cycle of said device by (a) correlating, for subsequent cross-correlation with certain of said return signals, only one of a plurality of models from a data base of said models to conserve computation resources, or (b) correlating many of said plurality models to seek broadly for a best-fit model.

153. A device employing ultra wideband radar return signals for extracting physiological data from one or more organs or physiological processes of a patient at one or more depths of interest within said patient, the device including a pulse repetition frequency generator for providing a pulse train coupled to a transmitter and a receiver, each of said transmitter and receiver coupled to one or more antennas, said transmitter transmitting pulse signals to said patient and said receiver receiving return signals from said patient, said receiver coupled to an analog to digital converter and further including dual channels and a sample and hold circuit coupled to each channel, each said sample and hold circuits triggered by the pulse repetition frequency generator, said circuits coupled to integrator elements and including a range compensated gain circuit including blanking to eliminate undesired ones of said return signals.