Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
  • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
  • A61B5/0535—Impedance plethysmography
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
  • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
  • A61B5/0538—Measuring electrical impedance or conductance of a portion of the body invasively, e.g. using a catheter
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/20—Measuring for diagnostic purposes; Identification of persons for measuring urological functions restricted to the evaluation of the urinary system
  • A61B5/202—Assessing bladder functions, e.g. incontinence assessment
  • A61B5/204—Determining bladder volume
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
  • A61B5/6813—Specially adapted to be attached to a specific body part
  • A61B5/6823—Trunk, e.g., chest, back, abdomen, hip
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
  • A61B5/683—Means for maintaining contact with the body
  • A61B5/6831—Straps, bands or harnesses
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
  • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
  • A61B5/6867—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive specially adapted to be attached or implanted in a specific body part
  • A61B5/6868—Brain
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2576/00—Medical imaging apparatus involving image processing or analysis
  • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
  • A61B2576/023—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part for the heart
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N1/00—Electrotherapy; Circuits therefor
  • A61N1/18—Applying electric currents by contact electrodes
  • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
  • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
  • A61N1/362—Heart stimulators
  • A61N1/365—Heart stimulators controlled by a physiological parameter, e.g. heart potential
  • A61N1/36514—Heart stimulators controlled by a physiological parameter, e.g. heart potential controlled by a physiological quantity other than heart potential, e.g. blood pressure
  • A61N1/36521—Heart stimulators controlled by a physiological parameter, e.g. heart potential controlled by a physiological quantity other than heart potential, e.g. blood pressure the parameter being derived from measurement of an electrical impedance
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H30/00—ICT specially adapted for the handling or processing of medical images
  • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Definitions

• the invention relates to the field of medical diagnostic devices and more specifically to a system and method for measuring anatomical parameters of region of interests of internal organs other than blood vessels.
• congestive heart failure is the most common admitting diagnosis among the Medicare patient population, placing a large social and financial burden upon the health care system (>$40B/yr, lOpts/ 1000 over age 65).
• a particularly vexing problem is the high rate of readmission following a hospitalization for heart failure (-25% to 30% 30-day readmission rate). Since heart failure admissions account for 80% of heart failure- related costs, there is a great need for effective means of disease management that effectively reduce heart failure-related hospitalizations.
• Impedance cardiography or ICG and electrical cardiometry (ECG) typically involve placement of four dual disposable sensors on the neck and chest.
• ICG electrical and impedance changes in the thorax, a region of interest in the heart, which are used to measure and calculate hemodynamic parameters.
• ICG measures the baseline impedance (resistance) to this input current.
• ICG measures the corresponding change in impedance and uses this to calculate hemodynamic parameters.
• ECG uses electrical velocimetry (EV) for its calculations of parameters. EV is based on the fact that the conductivity of the blood in the aorta changes during the cardiac cycle.
• bladder fluid or urine volume is an important measurement.
• ultrasound imaging is used where sound waves are used to visualize the kidneys, ureters, bladder, and urethra.
• cystoscopy is a technique where a thin tube with a tiny camera is inserted in the urethra and used to see the inside of the urethra and bladder.
• a diagnostic device comprises a diagnostic element that includes a first set of electrodes to receive a pre-determined electrical input signal from an excitation and measurement device and to transmit it to a region of interest, a second set of electrodes to receive a plurality of voltage signals from the region of interest and transmit it to a measurement device.
• the measurement device receives the voltage signals and employs a processor that uses data represented by the voltage signals to measure parameters such as fluid index for a thoracic fluid, an instantaneous volume for chambers of a heart over a cardiac cycle, urine volume in a bladder, or level of cerebrospinal fluid in a brain.
• the first electrode comprises a first pair of electrodes and the second electrode comprises a second pair of electrodes.
• FIG. 1 is a graphical representation showing the magnitude of specific impedance for various tissue types over a range of frequencies
• FIG. 2 is a graphical representation showing phase of specific impedance for various tissue types over a range of frequencies
• FIG. 3 is a diagrammatic representation of an exemplary embodiment of a diagnostic device
• FIG. 4 is a diagrammatic representation of a diagnostic element of the diagnostic device having spaced apart electrodes at pre-determined positions according to one aspect of an exemplary embodiment
• FIG. 5 is a diagrammatic representation of an exemplary embodiment of a diagnostic device
• FIG. 6 is a diagrammatic representation of another exemplary embodiment of a diagnostic device
• FIG. 7 is a diagrammatic representation of an exemplary embodiment of the diagnostic device showing associated electronics
• FIG. 8 is a diagrammatic representation of an exemplary embodiment of the diagnostic device used in conjunction with other medical devices.
• FIG. 9 is a flowchart representation of exemplary method steps for using the diagnostic device.
• FIG. 10 is a diagrammatic representation of a 2-port network with port voltages and port currents
• FIG. 11 is a diagrammatic representation of an exemplary embodiment with a multi port network at a distal end and the excitation and measurement entity at a proximal end;
• FIG. 12 is a diagrammatic representation of another exemplary embodiment with a multi port network at a distal end and the excitation and measurement entity at a proximal end;
• FIG. 13 is a diagrammatic representation of an exemplary embodiment for use in measuring electrical response from a region of interest
• FIG. 14 is a diagrammatic representation for another exemplary embodiment with a different configuration for obtaining the measurements from a region of interest
• FIG. 15 is a diagrammatic representation of a multi terminal embodiment used for modeling the system of FIG. 13 and FIG. 14;
• FIG. 16 is a diagrammatic representation of a multi port network that can use the assumptions of the embodiment of FIG. 15;
• FIG. 17 is a diagrammatic representation of a multi port network that can uses the method of the invention where 6 degrees of freedom are presented in the load network;
• FIG. 18 is a diagrammatic representation of an embodiment with an exemplary 3-port passive network 6 complex impedances;
• FIG. 19 is a diagrammatic representation of another embodiment with an exemplary
• FIG. 20 is a flowchart for the exemplary method steps of for calibration and de- embedding
• FIG. 21 is a flowchart representation of exemplary steps in the method for multiple frequency excitation according to an exemplary method
• FIG. 22 is a graphical representation that shows examples of current values that may be provided to a heart over a range of frequencies
• FIG. 23 is a block diagrammatic representation of a system for multiple frequency excitation
• FIG. 24 shows an exemplary implementation of a pseudo random binary sequence
• FIG. 25 a shows the exemplary pseudo random binary sequence in time domain
• FIG. 25b shows a zoomed in portion of the exemplary pseudo random binary sequence in time domain
• FIG. 26 shows the power spectral density of the exemplary pseudo random binary sequence
• FIG. 27 shows the phase plot of the exemplary pseudo random binary sequence
• FIG. 28 shows an exemplary implementation for orthogonal frequency division multiplexed (OFDM) sequence using IFFT
• FIG. 29 shows a time domain signal for the OFDM sequence of FIG. 28;
• FIG. 30 shows the OFDM Frequency Response for the implementation of FIG. 28; and [0045]
• FIG. 31 shows another exemplary implementation for generating a customized multi- frequency sequence involving additions of multiple sinusoids with appropriate phases.
• the devices, systems, and methods described herein combine precise physical measurement and tissue characterization at a smaller footprint and at lower cost compared to other standard diagnostic techniques such as, without limitation, impedance cardiography (ICG) for parameters related to the health of a heart.
• ICG impedance cardiography
• the techniques described herein can further uncover more anatomical details than some other diagnostic approaches and provide several advantages in a variety of uses.
• Parameter or “Dimension” or “ Anatomical Parameter” are used herein interchangeable and include, without limitation, parameters or dimensions related to an internal organ characteristic other than blood vessels or vascular bodily lumen that involve the circulatory system. These include cross sectional area, diameter, radius, major/minor axis for the region of interest, flow of fluid, level of fluid, pressure of fluid in an organ and any derivatives thereof. It includes for heart, hemodynamic parameters, such as cardiac output, stroke volume, systemic vascular resistance, thoracic fluid content, acceleration index, and systolic time ratio, fluid index etc.
• the region of interest referred herein includes internal organ regions other than blood vessels, methods described herein may be applied for any specific type of treatment or diagnostic applications for various anatomical features of a subject. Aspects of the disclosure can be applied as stand-alone systems or methods, or as part of a greater diagnostic or therapeutic device or procedure. It shall be understood that aspects of the disclosure can be appreciated individually, collectively, or in combination with each other. Features described in one or more embodiments can be incorporated into other embodiments unless the disclosure specifically says otherwise.
• An electrical network as referred herein is an interconnection of electrical elements such as resistors, inductors, capacitors, conductor wires, voltage sources, current sources and switches.
• a terminal is the point at which a conductor from an electrical component, device or network comes to an end and provides a point of connection to external circuits.
• a terminal may simply be the end of a wire or it may be fitted with a connector or fastener.
• terminal means a point at which connections can be made to a network in theory and does not necessarily refer to any real physical object.
• An electrical connector is an electro-mechanical device for joining electrical circuits as an interface using a mechanical assembly.
• the connection may be temporary, as for portable equipment, or may require a tool for assembly and removal, or may be a permanent electrical joint between two wires or devices.
• electrical measurements include measurable independent, semi- independent, and dependent electrical quantities including for example voltage by the means of voltmeter (or using oscilloscope, including pulse forms), electric current by the means of ammeter, electrical resistance, conductance, susceptance and electrical conductance by the means of ohmmeter, magnetic flux and magnetic field by means of a Halls sensor, electrical charge by the means of electrometer, electrical power by the means of electricity meter, electrical power spectrum by the means of spectrum analyzer.
• Electrical impedance as referred herein is defined as vector sum of electrical resistance and electrical reactance.
• Inductance is defined as frequency proportionality coefficient for reactance, and capacitance defined as reciprocal frequency proportional coefficient for reactance.
• Voltage between any two points as generally referred herein is the electrical potential difference between the two points and is also referred herein as voltage difference or voltage drop.
• peak to rms ratio means the value obtained for a waveform by the division of peak amplitude of the waveform by the root mean square value for the waveform. It is a dimensionless number generally expressed as a ratio of a positive rational number to one. It is also known in the art as "crest factor,” peak to average ratio, or by other similar terms, known to those of ordinary skill in the art. PAR values for a variety of standard waveforms are generally known. PAR values may be obtained from theoretical calculations, or it may be measured using some PAR meters for specific situations.
• SNR Signal to noise ratio
• the invention provides systems and methods for measurements of anatomical parameters in region of interests of heart, bladder, brain, stomach and other organs.
• Various aspects of the invention described herein may be applied to any of the particular applications set forth below or for any other types of treatment or diagnostic applications for various anatomical features of a subject.
• the invention may be applied as a stand alone system or method, or as part of a diagnostic or therapeutic device or procedure. It shall be understood that different aspects of the invention can be appreciated individually, collectively, or in combination with each other.
• FIG. 1 is a graphical representation 10 of impedance magnitude 12 for various tissue types over a range of frequencies 14. Impedance magnitude (absolute value of Vin/Iin measured in dB) versus frequency (Hz) is provided for aorta 16, blood 18, and fat (average infiltrated) 20. Vin represents voltage and lin represents current.
• the plots of impedance magnitude (absolute value of Vin/Iin measured in dB) for blood, tissue (aortic vessel) and fat shown indicate that when an excitation (e.g., a sinusoidal current (AC), or any other waveform) at different frequencies is applied in series across the region of interest (1 cubic millimeter, for example) shown by area 21, the impedance magnitude varies depending on the type of bodily material that occupies that volume.
• an excitation e.g., a sinusoidal current (AC), or any other waveform
• FIG. 2 is a graphical representation 24 of an example of impedance phase 26 (in degrees) for various tissue types over a range of frequencies 28.
• Line 30 represents the impedance phase (angle of Vin/Iin measured in degrees) of tissue (e.g. aortic vessel) across a frequency range of 100 Hz to 100 MHz;
• line 32 represents impedance phase (angle of Vin/Iin measured in degrees) of blood across a frequency range;
• line 34 represents impedance phase (angle of Vin/Iin measured in degrees) of fat across a frequency range.
• Vin represents voltage and Iin represents current.
• the plots of impedance phase (angle of Vin/Iin measured in degrees) for blood, tissue and fat shown indicate that when an excitation (e.g., a sinusoidal current (AC), or any other waveform as described elsewhere) at different frequencies is applied in series across the volume of interest (1 cubic millimeter, for example), the impedance phase depends on the type of bodily material that occupies that volume.
• an excitation e.g., a sinusoidal current (AC), or any other waveform as described elsewhere
• the impedance magnitude and/or the impedance phase may also be additive as would be known to one skilled in the art.
• the impedance measurements taken over the range of frequencies can yield the dimensions of the various tissue types.
• the unit electrical properties may be converted into volumetric data of the environment, utilizing the uniqueness of the combination.
• FIG. 3 is a diagrammatic representation of an exemplary embodiment of a diagnostic device 36 that includes a diagnostic element 38 according to an aspect of the invention.
• the diagnostic element 38 includes at least two spaced apart set of electrodes 40 and 42 at a distal end 44 configured to be placed proximal to the region of interest 46, wherein at least one of the set of electrodes is configured to receive an input current from a excitation and measuring device (also referred as excitation and measurement device) 48, and another pair (or the same pair) of electrodes is configured to receive a plurality of voltage signals from the region of interest 46 and transmit the voltage signal to the excitation and measuring device 48 at the proximal end 50.
• excitation and measuring device also referred as excitation and measurement device
• the excitation and measurement device 48 receives and measures the plurality of voltage signals to calculate a voltage difference between the spaced apart electrodes. These voltage differences are a function of bodily parameters and can be distinguished as explained in FIG. 1 and FIG. 2. In the exemplary embodiment a pair of electrodes has been referred to for measuring the signals from the region of interest, however any number of electrodes may be employed for this purpose.
• FIG. 4 is a diagrammatic representation of the diagnostic element 38 that includes the spaced apart electrodes 40 and 42 arranged at pre-determined positions that may be symmetric in one example and non-symmetric in another example, indicated by the reference numerals 60 through 64 as shown in FIG. 3. It may be noted here that the asymmetric positioning i.e. when the electrodes are not spaced apart equidistantly, may yield better sensitivity of measurements according to the methods described herein.
• the size of the electrodes may be same or different as indicated by reference numerals 52-58. It would be understood by those skilled in the art that the size and spacing of electrodes are designed for optimal performance.
• electrodes may be formed of a conductive material.
• electrodes may include a metal, such as copper, silver, aluminum, gold, or any alloys, plating, or combinations thereof. Electrodes may include exposed portions of wires. Electrodes may include any electrically conductive material in electrical communication with electronics for providing and/or receiving an electrical signal and/or current. It may be noted that in some configurations, the electrodes to send the input current and the electrodes to transmit the attenuated signals may be pre-determined. Further it is possible to select more than one pair of electrodes to send the input current and similarly more than one pair may be selected to sense the voltage signals from the region of interest. In specific embodiments electrodes may be provided in close proximity to an anatomical feature. For example, electrodes may be provided in close proximity to heart or other organ, where the electrodes may contact the outside surface and/or inside surface of the organ.
• the technology and methods described in the disclosure that employ the diagnostic device of FIG. 4 can be used to monitor important trends of a few parameters of interest such as thoracic fluid volumes, systolic and end diastolic volumes of cardiac chambers etc. and use this to provide an early detection and warning system of impending clinical deterioration
• the diagnostic device may be placed proximal to the heart region and is arrayed to send and receive low-amplitude, multi-frequency electrical current signals in multiple transthoracic vectors.
• different tissues blood, fat, cardiac tissue, etc.
• the information on thoracic fluid may be derived (e.g. a fluid "index").
• a clinical alert may be triggered.
• physiologic data such as left ventricle (LV) end-diastolic volume estimates, heart rate, etc. may be used to further enhance the system accuracy and sensitivity of the measurements.
• Another exemplary embodiment involves the use of the diagnostic device for patients suffering from incontinence.
• Incontinence affects several lO's of millions of people around the world. They have reduced control of a few specific abdominal muscles and it causes them to lose control when the bladder is semi full.
• the diagnostic device for such use may be positioned across the lower abdomen, over the bladder, and is arrayed to send and receive low-amplitude, multi- frequency electrical current signals in multiple transabdominal vectors. Different tissues (skin, blood, fat, etc.) and urine inside the bladder will have different frequency responses as described earlier and therefore a unique signature when traversed by electrical signals.
• the information on amount of urine in the bladder can be obtained and a alarm cam be set to go off (vibratory) when the bladder is full enough but before the patient loses control and has an accident.
• the exemplary embodiment of the diagnostic device may also be used to estimate the level of cerebrospinal (CS) fluid (and hence the cranial pressure) noninvasively. Similar to the application that measures the amount of urine in the bladder, it is possible to trend the amount (increase or decrease) of CS fluid in the cranial cavity. Since the cranial cavity is inelastic, an increase in the CS fluid volume directly leads to an increase in intracranial pressure, which is a parameter of critical clinical importance. By using the diagnostic device the voltages across different cranial vectors are measured yielding the change in the volume occupied by the CS fluid over time that can be monitored for increase or decrease of the cranial pressure.
• CS cerebrospinal
• the diagnostic device where it can be placed externally on a body part close to the non- vasculature regions of the organs whose parameters need to be observed or measured.
• FIG. 5 is a diagrammatic representation of an exemplary embodiment of the diagnostic device in the form of a band 66 that includes a strap 68 having the electrodes 40 and 42 integrated into it with known techniques, and a clasp 70 for placing it in position in proximity to the organ for the region of interest.
• the band 66 may be placed around a chest of a patient being diagnosed or treated for heart failure or related disease, and may be used for continuous monitoring. It may be placed in proximity of the abdomen for inconsistency related treatments as explained herein above, or around a head for a patient for brain related disease and treatment. Such a band may be placed on any other bodily part where the measurements are needed for specific parameters.
• the exemplary band may be sized according to the bodily part where it is to be placed and may include other features for holding the band in contact and in place, and for ease of patient wear.
• Other exemplary configurations include a separate transmitter and receiver adapted to be worn on the wrists.
• a bathroom scale-like device may be used that is connected to electrodes mounted on handles which will be grasped by the patient.
• the diagnostic device may be provided as an adjunct to the patient's footwear.
• FIG. 6 is a diagrammatic representation 72 of an implantable defibrillator 74 where the diagnostic element 38 that is the electrodes are embedded in the leads of the defibrillator (residing in left ventricale and atrium and the coronary sinus or a subset of these locations).
• the various electrodes can be used to either excite or measure voltages within the heart. Using the methods described herein, it is possible to obtain instantaneous volumes of the four chambers of the heart over the entire cardiac cycle. This advantageously allows a clinician to obtain many critical cardiac parameters.
• the excitation and measurements of electrical voltages and currents can be initiated and maintained by the implanted defibrillator and the information can be telemetrically transmitted to points outside the body.
• the diagnostic element can also be a stand alone implant or be placed inside a cardiac pacemaker or an implantable cardioverter defibrillator (ICD) and be used for home and hospital monitoring. This arrangement advantageously would not require any new leads or work flow implementation.
• FIG. 7 is a diagrammatic representation of the diagnostic device 76 having the diagnostic element 38 coupled to the excitation and measurement device that is shown with associated circuitry.
• the excitation and measurement device is incorporated in a dongle 78 and a host computer like a personal computer (PC) 80.
• the dongle 78 includes an electronics board that comprises a signal conditioning modules 82 which send and receive signal to and from the electrodes (or at least few of them.
• Each signal conditioner may be coupled to a high precision circuit shown general by 84 (for non-limiting example: a 16 bit data acquisition [DAQ] digital/analog circuit, or an 18bit DAQ), which converts a digital signal to an analog signal and is coupled to a level 1 signal processing unit 86.
• DAQ 16 bit data acquisition
• 18bit DAQ 18bit DAQ
• the signal may comprise any waveform known in the art.
• the signal may comprise a sinusoidal waveform, square waveform, triangular waveform, saw tooth waveform, pulse waveform, or any combinations thereof.
• These data acquisition circuits digitize the output voltage measured by the excitation and measurement device, and the digitized signal may be processed first by a level 1 signal processing unit 86.
• a computer or host computer or any specific type of network device may include, but is not limited to, a personal computer, server computer, or laptop computer; personal digital assistants (PDAs).
• PDAs personal digital assistants
• multiple devices or processors may be used.
• various computers or processors may be specially programmed to perform one or more step or calculation or perform any algorithm, as described herein
• This signal processing unit 86 can be split into multiple sections, some residing in hardware in the dongle and the rest on a host computer as shown in FIG. 7 by a level 2 signal processing unit 88. This splitting is not mandatory and in some embodiments, the signal processing units 86 and 88 may be incorporated entirely on the host computer, or the signal processing units 86 and 88 may be provided entirely on a dongle. In one exemplary embodiment, a first level of the signal processor (level 1 signal processing unit) may reduce the sheer volume of data making it amenable to be transferred into a PC where the rest of the processing is done.
• a level 1 or a first level signal processing unit may compress the output signal such that essential information is not lost, but noise is reduced in the data, thus reducing the size of the data packet (or processed digital signals) passed to a level 2 or second level signal processing unit.
• the level 1 signal processing unit may remove the effects of device resistance and coupling.
• the level 2 signal processor may be part of a computer or part of the electronics board itself. This level 2 processor may execute an algorithm or a technique or a method to determine the dimensional aspects of interest (measurements, tissue characterizations, displays of the same for non-limiting example).
• the level 1 and level 2 processors may be contained in a single processor which carries out both functions of the separate level 1 and level 2 processors described. Also, at least one of the processors and/or conditioner is configured and/or programmed to remove the effects (at least in part, if not entirely) of device resistance and coupling.
• FIG. 8 is a diagrammatic representation of the exemplary embodiment 98 showing the integration of the diagnostic device with other medical devices for enhanced measurements.
• the diagnostic device 38 that includes the excitation and measurement device 92 receives the voltage signals from at least one set of electrodes of the diagnostic device 38 and converts (and/or transform) them to measurements and/or other anatomical information for non-limiting example using a processing unit 94.
• the processing unit 94 may also be coupled to an ECG capture unit 98 and another imaging modality such as X Ray or ultrasound 100 for further processing.
• the results from the processing unit 94 can be overlaid on the outputs from the ECG capture unit and the imaging modality to provide more insights for diagnosis and treatment.
• the processing unit 94 outputs the measurements (may include only the measurements from the diagnostic device or enhanced measurements or results after combining data from other medical devices such as ECG or imaging modality data) that are displayed on a display device 96.
• the display device 96 shows the results in different forms, dimension values, graphical representation or visual representations overlaid on angiograms.
• the display device and the processor or part of the processor may be incorporated in a host computer.
• the display is a video display.
• Video displays may include devices upon which information may be displayed in a manner perceptible to a user, such as, for example, a computer monitor, cathode ray tube, liquid crystal display, light emitting diode display, touchpad or touch screen display, and/or other means known in the art for emitting a visually perceptible output.
• the visual representation may be monochromatic, or may include color. In some embodiments, colors or shading may be indicative of the dimensions.
• the dimensions or parameters may be automatically displayed by the processing unit onto the display unit.
• the dimensions may be displayed in response to a user input.
• user input may include, but are not limited to, a cursor over a portion of the display (which may be controlled by a pointing device such as a mouse, trackball, joystick, touchscreen, arrow keys, remote control), or a keyboard entry.
• the dimensions are provided in proximity to a cursor, or other user input. For example, as a user positions a mouse cursor over a portion of the visual representation, the dimension at that portion may be revealed. In other embodiments, all dimensions may be displayed.
• the signal processing may be based on different techniques including but not limited to calculating the voltage difference based on spatial diversity of the at least two electrodes.
• the voltage difference may be calculated based on frequency diversity of the input current and the measured voltage signals.
• the voltage difference is based on tissue diversity or the characteristic of the bodily fluid of the region of interest.
• the voltage distribution obtained from the plurality of electrodes created by the injection of current across a pair of electrodes is unique as mentioned herein above and is describable by a set of equations that govern the electrical transport through the media surrounding the electrodes. These equations are solvable due to the diversity of frequencies of measurement (frequency diversity), diversity in the dimensions over which electrical transport is observed (spatial diversity). Any electrical characteristic may be measured and/or determined from the measurements, including but not limited to voltage distribution, impedance distribution across the region of interest.
• electrical measurements may be performed over a range of frequencies.
• the magnitude and the phase of the response may be captured.
• Electrical measurements may be taken over any range of frequency values. Electrical measurements taken may include voltage and current measurements.
• the magnitude of the voltage for a given magnitude of input current and also the phase of the voltage with respect to the phase of the input current may be measured.
• the magnitude and phase may be used in one technique to determine the parameters. These techniques are described in more details in reference to FIG. 21-31.
• the methods described herein exploit the frequency diversity of the input signal, frequency diversity of the measured voltage signals and also in some embodiments the spatial diversity of the electrodes to uniquely determine anatomical parameters from the response observed by the electrodes.
• One method is to create an electrically equivalent 3-dimensional model of the near-field using lumped finite-elements and mathematically invert the unit elements to allow the model to recreate the observed set of voltages, and thereby calculating the required parameters.
• a network may be provided with finite element unit characteristics of the subject's tissue embedded. This may accommodate different types of tissues.
• the tissue may include blood and fat.
• An electrical network with a first element (e.g., blood) multiplied by a first dimension may be provided.
• An electrical network with a second element (e.g., fat) multiplied by a second dimension may be provided.
• the networks may provide simulations, and the various dimensions (e.g., first and second dimension) may be altered until the desired response is achieved. This may be provided for any number of dimensions corresponding to various tissue types (e.g., one, two, three, four, or more dimensions). When the desired response is achieved, the dimension values used to achieve the desired response may be the dimensions of the tissue.
• Another method is to embed the electromagnetic simulation within an inverting algorithm, again yielding the dimensions of interest. This may utilize similar steps as described above.
• Yet another method is to pre-calculate the responses using an off-line set of simulations using a 3-D EM simulator and the algorithm may search this pre-calculated data-set to arrive at the most appropriate set of responses which matches the observed responses.
• various tissues e.g., blood, fat, aorta
• the various dimension values may be varied, and calculations may be performed, to yield a result.
• the result may be stored in memory.
• the results may be stored within a look-up table.
• the look-up table may be searched for results that match one or more measurement, and the set of dimensions used to arrive at that results may be adopted as the dimensions of the tissue. Points outside the finite instances in the look-up table may be accommodated by an interpolation.
• FIG. 10-FIG. 20 describe some more novel techniques for calibration and de- embedding of the measured voltage signals.
• FIG. 9 is a flowchart representation 102 of exemplary steps for a method for measuring the anatomical parameters for a region of interest.
• the method includes a step 104 for providing at least two sets of spaced apart electrodes configured to be placed proximal to a region of interest, a step 106 for receiving an input current from an electrical excitation source across at least one pair of the spaced apart electrodes and transmitting the input current to the a region of interest, a step 108 for receiving a plurality of voltage signals from the at least one set of spaced apart electrodes, a step 110 for measuring the plurality of voltage signals as a function of voltage difference between at least one set of the spaced apart electrodes; and a step 112 for converting the voltage differences to one or more parameter measurements through the various techniques that have been described herein.
• the description relates generally to estimating a remotely located multi port electrical network and more specifically calibration and de-embedding of the electrical signals to and from the multi port network. These techniques are useful in determining the measurements from the voltage signals received by the diagnostic element as described earlier.
• the measurement and the excitation apparatus is at a physical distance from the sensors or the load across which these measurements are desired.
• conductors connect the electrical source and measurement apparatus and the load.
• the electrical source, measurement apparatus, conductors, and load form an electrical network.
• the electrical network has been modeled using electrical parameters. There are many types of parameters used in the art for modeling:
• Z parameters also called the impedance parameters of a network, relate the voltage and currents of a multi-port network.
• Z parameters also called the impedance parameters of a network, relate the voltage and currents of a multi-port network.
• the two voltages and two currents are related by through Z parameters as follows:
• n, m, k 1, ... N
• Y parameters also called as Admittance parameters of a network, relate the voltage and currents of a multi-port electrical network.
• Admittance parameters of a network relate the voltage and currents of a multi-port electrical network.
• the 2 voltages and 2 currents are related by through Y parameters as follows
• the S parameters also called the Scattering parameters of a network, relate the incident and reflected power waves.
• the relationship between the reflected, incident power waves and the S-parameter matrix is given by: where a n and b n are the incident and reflected waves, and are related to the port voltages and currents.
• H parameters also called the Hybrid parameters relate the port voltages and currents in a different way.
• Hybrid parameters relate the port voltages and currents in a different way.
• G parameter also called the inverse Hybrid parameters of a network, relate the voltages and current as follows: where .'i ...f ! 1 T
• All the above formulations are related, and one set of parameters can be derived from another. This is well known and established in the art.
• the Z and Y parameter matrices are inverses of each other.
• the H and G parameter matrices are inverses of each other.
• the Y and S parameters are also related, and can be derived from each other. All the mentioned types of models are electrically equivalent. The choice of implementation depends on convenience and specific needs of a problem.
• Z-parameters are the impedance parameters for an electrical network.
• a generic multi-port network referred herein includes ports 1 to N, where N is an integer depicting the total number of ports. For port n, where n is ranging from 1 to N, the associated input current through that port to the network is defined as In and the voltage across that port is defined as Vn.
• Z is an N x N matrix the elements of which can be indexed using conventional matrix notation.
• the elements of the Z-parameter matrix are complex numbers and functions of frequency.
• the Z- matrix reduces to a single element, that is the ordinary impedance measured between the two terminals.
• Y is an N x N matrix.
• Y is related to Z.
• Y is the matrix inverse of Z. In some special circumstances, either Z or Y becomes non-invertible.
• FIG.11 is a diagrammatic representation of an exemplary embodiment 212.
• the problem being solved by the instant disclosure specifically deals with estimation of an electrical network 218 of a distant zone (herein referred to as a load network) when it is excited by an electrical stimulus from the near end.
• the load network 218 situated on the distal end is connected to a set of stimulating and measuring devices 214 on the proximal end through a bunch of conductors 216 whose combined electrical property is fixed but unknown.
• the stimulus can be either an arbitrary current or, voltage from the stimulating device located at the proximal end while the measurements are in form of voltage measurements again at the proximal end.
• the voltage measurement is in general non-ideal (i.e.
• the voltage measurement devices draw non-zero finite currents from the network and hence loads the network).
• the solution to this problem described subsequently can be extended and applied to any area of operation, where the electrical network to be estimated is situated at a remote location where in-situ excitation and measurements are not feasible.
• FIG. 12 An exemplary embodiment 220 is shown in FIG. 12 that embodies the electrical network of FIG. 11, with the excitation and measurement entity 48 having an excitation entity 233, a reference resistor RRef 234, and voltage measurement entities VM1 231 for measuring voltage drop across the resistance RRef and voltage measurement entities 236, 238, 240 shown as VM2, VM3, and VM4 for measuring proximally the distal voltages of the multiport load network 218 through a multiport inter connecting network 216.
• the working of such an embodiment is described in more detail in reference to FIG. 13 herein below.
• FIG. 13 A specific practical scenario is illustrated in FIG. 13, through the exemplary embodiment 220 which is used for measuring proximal voltages corresponding to distal voltages across four conductors connected to the distal end electrodes 40 and 42 at distal end 44 placed proximal to the region of interest.
• the four electrodes are connected to four long conductors 232 which gets terminated on a connector on the proximal end 50. Though four electrodes are shown for the exemplary embodiment, three or more electrodes can be used in different configurations needed for measurements and these are included in the scope of the method described herein.
• the connector is connected to a hardware which provides the stimulus across the two conductors connected to the extreme electrodes and also measures the three voltages across the three pair of conductors.
• the hardware includes an electrical source and a measurement device 48 having the excitation entity 233 and measurement entities 236, 238, 240.
• a fourth measurement via the measurement entity 231 is done across a reference resistor 234 which is in series with this network.
• the entire network is in variant across various load configurations at the distal end 44 but not known to start with and needs to be estimated through carefully chosen load configurations.
• the calibration method as described herein estimates this network in order to correctly determine and de-embed the measurements for any arbitrary load network connected distally to it.
• FIG. 14 is another exemplary embodiment 242 with a different configuration for obtaining the measurements.
• the fourth measurement via the measurement entity 231 (VM1) is done, where VM1 is placed in parallel with the excitation entity 233 to obtain the reference voltage across the excitation entity, while the other three measurements are obtained as mentioned in reference to FIG. 12.
• the other components in FIG. 14 are same as in embodiment of FIG. 13. It would be appreciated by those skilled in the art that there may be other alternate configurations for obtaining the measurements and the embodiments described in reference to FIG. 12, FIG. 13 and FIG. 14 are to treated as non-limiting examples. In general any four independent measurements would suffice for estimation of distal load network.
• FIG. 12, FIG. 13 and FIG. 14 respectively are typically but not limited to, a set of front end buffers and amplifiers for signal conditioning and noise filtering followed by an analog to digital converter.
• the measurement entity may provide frequency dependent gain to the incident signal across it.
• a voltage measurement unit should not draw any current from the network it is connected to, but in practice it is impossible to implement the same.
• the voltage measurement entity can be equivalently modeled, as a cascade of an equivalent parasitic network that accounts for the loading, filtering and other non-idealities followed by an ideal buffer and gain unit that does not draw any input current and only amplify the incident voltage by a fixed amount.
• the parasitic network can be merged as a part of the in between catheter network and estimated jointly, as is described in more detail herein below.
• FIG. 15 is a terminal representation 244 for the specific scenario of FIG. 12. It will be understood by those skilled in the art that a terminal, generally referred as Tk (Vk, Ik) represents a terminal k whose voltage with respect to an arbitrary ground, represented as GND 254 in FIG. 15 is Vk while the current entering the network through that terminal is Ik.
• Tk terminal k
• GND 254 in FIG. 15
• the terminals are defined in the following manner.
• Terminal-0 TO
• 246 is the terminal across which a voltage source or, a current source 48 is connected.
• the voltage measured on Terminal-0 with respect to an arbitrary GND is defined as V0, while the current entering the network through TO is defined as 10.
• Terminal-1A represented by 248 is one of the differential terminals across which the first measurement is done. This terminal does not source or, sink any current to the network as these terminals are modeled as ideal measurement points.
• Terminal -IB represented by 250 pairs with Terminal -1A and behaves similarly to Terminal- 1 A.
• Terminal-2A, Terminal-2B are the set of differential terminals for the second measurement.
• Terminal-3A, Terminal-3B are the terminals for the third measurements, while Terminal-4A, Terminal-4B are the set of differential terminals for the fourth measurement.
• the terminals 2 A, 2B, 3 A, 3B, 4A, 4B are shown by referral numeral 252 and represent the terminals for proximal voltages in FIG. 15. Each of these terminals don't source or, sink any current. The voltages on these terminals are all measured with reference to the same GND 254 referenced in the previous paragraph.
• Terminal-5 On the distal side, Terminal-5, Terminal-6, Terminal-7 and Terminal-8, referred by
• V5, V6, V7 and V8 corresponds to the four electrodes forming the multi port load network 218 that is connected to the measurement entities and excitation source via the multi port interconnecting network 216 as explained herein above.
• the voltages on these terminals are referred to as V5, V6, V7 and V8 referred also as distal voltages respectively, where all these measurements are done with respect to the same GND 254.
• the currents entering the network through these terminals are referred to as 15, 16, 17 and 18 respectively.
• V1 Z1* I1 (1)
• VI [Vo VIA V 1B V 2A V 2B V 3A V 3B V 4A V 4B V 5 V 6 V 7 V 8 ] T
• Zl is the impedance matrix of the network relating the current vector II to the voltage vector VI.
• node 1 , node 2, node 3 and node 4 representing the distal end electrodes are represented differentially as:
• VI VIA - VIB V 2 - V 2 A 2 B
• V 4 V4A - V 4 B (3)
• Equation (1) can be now re-written as:
• Z2 is the impedance matrix of the network relating the current vector 12 to the voltage vector V2.
• a port representation on the distal end is shown instead of the terminal representation of FIG. 15 as described herein above.
• the port voltages PI, P2, P3, P4 and PLl, PL2, PL3 in this exemplary embodiment are defined as differences between two neighboring terminal voltages, the voltage difference being depicted by reference numerals 260-272 respectively, while the port currents are defined as the current that goes in through one arm of the port and comes out of the network through another arm of the port.
• V ZI (6) where, V and I are given by,
• V [Vo Vi V 2 V 3 V 4 V L1 V L2 V L3 ] T
• Z is the impedance matrix of the network relating the current vector I to the voltage vector V .
• VO is the voltage applied to the network
• 10 is the current getting into the network. If the excitation is a perfect voltage source, VO is fixed to the value of the voltage source. Similarly, for a perfect current source excitation, 10 is fixed to the value of the current for the current source. However in practice, an ideal voltage source or, a current source do not exist.
• Vi Ziolo + Zulu + I 2 IL2 + ZI 3 IL 3
• V 2 Z 2 oIo + Z 2 IILI + Z 22 IL 2 + Z 23 IL 3
• V 3 Z 3 oIo + Z 3 IILI + Z 32 IL 2 + Z 33 IL 3
• V 4 Z 4 oIo + Z 4 IILI + Z 42 IL 2 + Z 43 IL 3
• VLI Z50I0 + Z51ILI + Z5 2 IL 2 + Z5 3 IL 3
• VL 2 Z 6 oIo + Z 6 IILI + Z 62 IL 2 + Z 63 IL 3
• VL 3 Z70I0 + Z 7 IILI + Z 72 IL 2 + Z 73 IL 3 (8)
• the four measured voltages are grouped in a vector VM and similarly the load side voltages are grouped in the vector VL.
• the load side currents are similarly grouped in vector IL, as shown in the equations below:
• V M [Vi V 2 V 3 V 4 ] T
• V L [VLI V L2 V L3 ] T
• V L Z LO Io + Z LL I L (10)
• ZMO, ZML, ZLO and ZLL are sub-matrices of the impedance matrix (Z) formed by the grouping of the Z-terms in Eqn (16).
• the distal side is also terminated by an arbitrary network which can be modeled as a 3x3 admittance matrix Y related to the load side voltage vector VL and current vector IL.
• the admittance matrix, Y would have 6 independent variables, whereas for a general active network the number of variables would be 9.
• the load network may have other constraints and the degrees of freedom is lower than 6.
• the anatomical constraints while measuring the non vasculature or anatomical parameters may drive the degrees of freedom of the Y parameters to 3 or, less.
• Equation (11) in Equation (10) the following is derived:
• V L ZLOIO + ZLLIL
• VL Z LO IO Z LL YVL
• VL (I + Z LL Y)- 1 Z LO IO
• V M /Io ZMO - Z ML Y(I + ZLLY) -1 Z lo (12)
• the exemplary method as described herein uses the above system model for determining the actual voltage difference measurements for an arbitrary load network connected at the distal end through proximal measurements.
• the next step for the method is to identify the Z parameters of the connecting network along with measurement parasitics, herein referred to as the calibration step.
• a step of de-embedding is done wherein, the proximal measurements are mapped to (or, fitted to) the distal load network after due consideration for the Z parameters of the connecting network and measurement parasitics.
• the three voltage ratios with respect to the first voltage is measured for different combination of precisely known load networks connected on the distal end.
• the number of unknown Z-parameters to be estimated is 23.
• the Z parameters are obtained using a suitable fitting utility that runs on the set of measured data. Since every configuration provides three voltages, it is necessary to have at least measurements from 8 independent configurations to obtain all the Z parameters. More number of configurations provides better noise immunity to the fitted values.
• the fitter routine starts with an arbitrary starting point and computes the estimated ratios of voltages across different known load configurations for Equation (13).
• the method then computes an error metric which is the Euclidian distance between the measured ratios and the estimated ratios.
• the fitter tries to minimize this error by adjusting the Z parameter values. It is possible for the solution to converge to alternate solutions. However, skilled persons in this art would recognize these challenges and come up with suitable techniques to circumvent them. This can be done by employing suitable optimization techniques. It may be noted that the fitted Z parameters are not the true Z parameters of the network but are a mathematical representation that fits the observation under the constraints of one pre-determined Z-parameter (any one of ZLO). Further, a few Z- parameters are normalized to Z10 and Z10 is fixed to unity, as was mentioned earlier.
• the connecting network can be used to identify any arbitrary load network at the distal end.
• the degrees of freedom for the network is 3.
• the three voltage distributions across the three consecutive pairs of electrodes completely define the Z-parameters of the equivalent electrical network formed by the electrodes inside the region of interest. Similar applications such as measurement of a cross section of a pipe electrically through similar means would also have similar degrees of freedom.
• a similar fitter routine can be used to find out the load network.
• the fitter routine is initialized by a starting value of Y, which is the best case estimate given by the user.
• the ratios are accordingly estimated (according to Equation 13) and an error metric is computed as the difference between the measured ratios and the estimated ratios.
• the error metric is then minimized by adjusting the Y parameters of the load network.
• the Y parameters representing the lowest error represent the optimal Y parameters for the load network.
• the Y parameter can have 9 degrees of freedom.
• the degrees of freedom are typically 6. Identification of such networks can also be done using extension of the exemplary method. To identify a passive arbitrary load network (with 6 degrees of freedom), the calibration and de-embedding processes needs to be done for two independent interconnecting networks. In practice, it can also be achieved by taking two measurements, one with the actual interconnecting network and the other with a tweaked version of the same.
• the connecting network is tweaked using a reversible mechanism (such as a relay 276 shorting the two centre ports 2 and 3 at the proximal end of the embodiment 274 of FIG. 17) and the new proximal voltages are measured.
• a reversible mechanism such as a relay 276 shorting the two centre ports 2 and 3 at the proximal end of the embodiment 274 of FIG. 17
• an n-port load network is represented by L independent
• FIG. 18 represents an embodiment 278 with an exemplary 3-port passive network 280 with 6 complex impedances shown generally by reference numeral 282. Any other passive 3-port network topology can be reduced to an equivalent network 280 with the topology shown in the embodiment 284 of FIG. 19 as well.
• Other components related to the excitation and measurement entity remain the same as described in earlier figures.
• the voltage across any two points (u, v) in the network can be represented as a product of the excitation voltage or, excitation current ( ⁇ ) and a ratio of sum of polynomials formed by all the impedances present in the network.
• the denominator polynomial is referred to as the characteristic polynomial of the network consisting of all the impedances in the network.
• the characteristic polynomial is independent of the points of measurements.
• the voltage can still be represented as a product of ⁇ and the ratio of sum of polynomials formed by all the discrete impedances present in the network, wherein the coefficients of the polynomial would capture the effects of the distributed elements.
• the polynomials can be regrouped into a polynomial of just the discrete impedances of interest.
• the coefficients of the regrouped polynomial would contain the effects of the other discrete impedances as well as the distributed elements of the network.
• the voltage between any two points (u, v) in the network can be written as:
• each of the L number of load impedances contributes to the voltage distribution within the network.
• the contribution of fixed elements within the network is absorbed in the polynomial coefficients.
• the denominator is equivalent to the characteristic polynomial for the combined network (48, 216 and 218), and its coefficients (a's) are fixed for the given network and depends on network 48 and 216.
• the entire n-port load network can be represented by n complex impedances.
• the Z- parameter for the network would be a diagonal matrix with n diagonal terms.
• FIG. 18 describes an exemplary embodiment where the number of ports (ri) is 3.
• the voltage measurements in the proximal side e.g. Vi, V 2 , V 3 , V 4 ) is given by:
• the properties of the measurement and the connecting networks are represented by the polynomial coefficients.
• the number of independent polynomial coefficients would be (n+l)*2n-l. It may be noted that all the polynomial coefficients in Equation (16) can be scaled by the first term in the denominator, thereby reducing one unknown.
• the act of calibrating these networks would involve making proximal measurements with known impedances connected to the distal ports. The number of such independent measurements required would depend on the number of unknowns that need to be solved and the number of information per measurement. A fitter routine would then run on all of these measurement ratios, for known set of loads and estimate the polynomial coefficients.
• any arbitrary load connected across the distal ports in a similar configuration can be estimated.
• the proximal measurements are made and the ratios are computed with respect to the reference measurement.
• a fitter routine is invoked with the pre-determined polynomial coefficients and the ratios corresponding to the arbitrary load.
• the fitter routine may be initialized by the user with a starting value of the load impedances based on best guess. The fitter shall converge to a minimal residue on finding the optimal values for impedances which would match the ratio of measurements. Convergence to alternate solutions are possible, however skilled persons in this art would be adept in avoiding such situations.
• Equation (14) To estimate a generalized three port passive load network which can be modeled by six independent impedances, one would need to write the polynomial equations in Equation (14) with all six impedance present. Since the numbers of ratios measured are only three, the method needs to be extended for measurement of six impedances as discussed before.
• the method of calibration would involve making measurements with various combinations of load networks (comprised of all six impedances) for two independent interconnecting networks. The polynomial coefficients for both these networks would then be estimated using the individual sets of measured voltages and the knowledge of load impedances. Next, measurements would be made with arbitrary six impedance load networks, again with the same two independent interconnecting networks. The two sets of measurements along with the polynomial coefficients for both the networks would jointly be fitted by a fitter routine for estimating the six impedances. The method can similarly be extended to active networks where a nine impedance model needs to be estimated.
• the methods as described herein above are also depicted in the form of flowchart 286 of FIG. 20.
• the calibration technique for use in measurements from a remotely located multi port network is shown by steps 288 to 300 of the flowchart, and includes a step 288 of providing an excitation and measurement entity for exciting the remotely located multi port network and for measuring a plurality of proximal voltages corresponding to the remotely located multi port network; a step 290 of providing a connecting network for connecting the excitation and measurement entity and the remotely located multi port network; a step 292 providing a plurality of known load networks coupled to the connecting network.
• the calibration technique further includes a step 294 for measuring a set of voltages corresponding to each load of the known load networks; and a step
• the method further includes a step 298 for providing a plurality of unknown load networks coupled to the connecting network and a step 300 for using the electrical parameters to de- embed the measurements from the remotely located multi port network.
• the method as described herein above maybe incorporated as a tool that is used to determine the voltages or any other electrical response from a remotely located multi port network.
• a system for de-embedding measured proximal voltages across conductors connected to at least three electrodes placed proximal to the region of interest may include the embodiments of FIG. 11- FIG. 14 having an excitation and measurement entity for exciting the at least three electrodes and for measuring a plurality of proximal voltages corresponding to the at least three electrodes.
• the system also includes a connecting network in the form of two or more conductors for connecting the excitation and measurement entity and the at least three electrodes, where the at least three electrodes are at a distal end of the two or more conductors.
• a processor is added in the embodiments of FIG.11- FIG.
• the multi-port interconnection network 216 may be include multiple parts or components. In this case, and each part would be calibrated separately and the parameters can be combined together at the time of de-embedding. It is to be understood that this divided approach for calibration and de-embedding is also within the scope of the invention as described herein.
• a method for measuring a plurality of actual voltage distributions across the ports of a remotely located multi port network, a tool and system incorporating the method are disclosed.
• the method includes a calibration and de-embedding step that involves providing an excitation and measurement entity for exciting the remotely located multi port network and for measuring a plurality of proximal voltages corresponding to the remotely located multi port network and providing a connecting network for connecting the excitation and measurement entity and the remotely located multi port network.
• a calibration method is used to provide a plurality of electrical parameters as calibration parameters corresponding to the measurement entity and the connecting network.
• the method includes exciting the remotely located multi port network with a known voltage and a known current; measuring proximal voltages across at least two pair of ports for the remotely located multiport network; and estimating actual voltage distribution across the at least two pair of ports using the electrical parameters to de-embed the proximal voltages.
• the following description relates to generating multiple frequency excitation for the diagnostic element as mentioned earlier.
• This medium may be composed of different types of substances that have different electrical properties. Analysis of these electrical properties may lead to inferences of other useful properties of the medium such as physical features, dimensions and composition.
• One such electrically conducting medium is biological tissue. In particular, this could be a region that consists of bodily fluid and other surrounding tissue. Different types of tissue have different frequency dependent properties. In such an analysis, the region of interest is treated as an electrical network through which electrical current can traverse.
• an electrical excitation (stimulus) is sent to the specific regions of interest and the responses are picked up at different pick up points and analyzed.
• excitation of the specific regions of interest at multiple frequencies are provided and the responses are analyzed at each frequency.
• Prior arts in this area mostly focus on exciting the network with one frequency at a time and scanning through multiple frequencies within a small interval of time. Such techniques are common in Electrical Impedance Tomography. The responses at each frequency belonging to slightly different time instances are stitched together and analyzed.
• the peak value of the excitation is limited by the admissible peak currents in the region of interest where the excitation is applied, and the linearity and dynamic range of the electrical hardware associated with the excitation (transmitter) and the measurements (receiver).
• an increased PAR would limit the average energy (rms) of the excitation signal. This in turn would degrade the signal to noise ratio (SNR) of the received responses.
• SNR signal to noise ratio
• the techniques described hereinbelow involve broad band excitation of a slow time varying electrical network for example the network involving a cardiac region where the excitation is done using a signal that simultaneously presents multiple frequencies. More specifically, two exemplary non-limiting efficient ways of multi-frequency excitations are described, the first one using a pseudo random sequence and the second using a method using orthogonal frequency division multiplexed (OFDM) sequence.
• OFDM orthogonal frequency division multiplexed
• the invention provides a method for exciting at least a set of electrodes configured to be placed in vivo proximal to a volume of interest in a vasculature.
• the description herein below relates to an estimation method of a broad band frequency response of any slow time varying electrical network by exciting it with a broad band electrical excitation and analysing its electrical response.
• the broad band excitation is done using a signal that simultaneously presents multiple frequencies.
• FIG. 21 shows the method of the invention, depicted by numeral 310, in the form of a flowchart having exemplary steps.
• the method comprises generating a multiple frequency sequence pulse having a predetermined peak to root-to-mean-square (rms) ratio that is close to unity, as shown in FIG. 21 and represented by numeral 312.
• an excitation with multiple frequencies and a good PAR i.e. PAR close to unity is constructed by generating a pseudo random sequence.
• a pseudo random sequence of length L and generated at a sampling of fs would contain discrete un-aliased tones of frequency from 0 (which corresponds to a DC frequency) to fs/2, in steps of fs/L.
• the power at each frequency (except DC) is equi-distributed while the phase of the individual tones is uniformly spread over - ⁇ to + ⁇ .
• D/A digital-to- analog converter
• DAC digital-to- analog converter
• the D/A sampling rate needs to be at least double the required maximum frequency of excitation.
• the basic shape of the D/A converter output is a rectangular pulse of width equal to the time difference between two consecutive samples.
• the D/A converter that outputs a pseudo random sequence is sampled at a frequency (fs) that is at least twice the desired maximum frequency of measurement (fH), it would create a frequency shape that is the product of the frequency shape of the basic pseudo random sequence and the frequency shape of the rectangular pulse (i.e. a Sine function with the first null at fs).
• a big advantage of an excitation based on pseudo random sequence with a basic rectangular shape is that its PAR is unity. This leads to maximising the rms signal power for a given peak amplitude of the signal.
• the output of the D/A converter in this implementation has only two levels (-A and A), where A is the amplitude of excitation.
• the linearity of the transmit chain is irrelevant since non-linearity only produces a gain error and offset error to the signal.
• the receive chain design is also simplified with a lower PAR since dynamic range and linearity requirements are less demanding.
• the D/A converter output needs to be filtered effectively to prevent out of band emissions outside the band of interest.
• the filtering may be accomplished using a passive or, an active analog filter with pass band at the region of interest. It would be understood by those skilled in the art that filtering would result in a small increase in PAR which is however not significant and PAR would still remain close to unity.
• the excitation sequence is constructed as a repetitive orthogonal frequency division multiplexed (OFDM) sequence.
• the OFDM sequence consists of equal amplitude of all frequencies starting from a low frequency of interest to a high frequency of interest.
• the number of frequencies excited is proportional to the ratio of the high frequency (fH) to the low frequency (fL), while the spacing between frequencies is same as the lowest frequency (fL) of interest that is chosen.
• the duration of the basic OFDM sequence is inversely related to its lowest frequency.
• the PAR of the OFDM sequence can be made to a low value close to unity by suitable choice of phase for each frequency.
• An OFDM based sequence is a sum of several discrete tones whose number is a power of 2, and provides distinct advantage of implementing the processing circuitry in an efficient manner based on Fast Fourier Transform (FFT).
• FFT Fast Fourier Transform
• the excitation sequence can be constructed as additions of multiple coherent sinusoids with a method that would minimize the overall PAR of the sequence. PAR minimization can be achieved by suitably adjusting the phase of each sinusoid. Such sequences can also be constructed by appropriately dropping out one or, more tones from the OFDM sequence. These sequences are particularly useful over a full-fledged OFDM sequence where the electrical hardware may not handle a large set of frequency information due to its limited capacity or, the non-linearity of the electrical hardware is too high and dictates the use of tones that have non- multiplicative relationship with each other, so that the non-linear effect of one or, more tones do not impact another tone.
• FIG. 22 shows a graphical representation 316 of exemplary current values 318 that may be provided to a heart over a range of frequencies 320. For example, maximum permissible current through a heart (in ⁇ ) may vary over the range of frequencies.
• the maximum permissible current through a heart may also vary depending on whether the current is applied in an abnormal non-continuous manner, abnormal continuous manner, or normal continuous manner as shown at reference numerals 322, 324, 326. In some embodiments, it may be preferable to apply a current in a normal continuous manner in a range around a 100 KHz frequency. In some embodiments, the current may be applied between about 40 KHz (fL) and 10 MHz (fH).A safe zone may be used as indicated by reference numeral 328.
• One possible way of determining the value of rms current for an excitation based on pseudo-random sequence can be by matching the rms current of the composite signal to the corresponding admissible rms current for the lowest frequency.
• the exemplary method then comprises applying the multiple frequency sequence pulse to excite across the set of electrodes of the diagnostic device.
• the step of applying the multiple frequency pulse is depicted by numeral 314.
• the excited set of electrodes then sends a pulse of electric current across the region of interest.
• a voltage is developed across the electrodes. There will be one voltage corresponding to each excitation frequency from the multiple frequency pulse.
• the set of voltages at multiple frequencies developed across the electrodes may then be detected using an appropriate measurement device that is capable of handling all of these signals simultaneously.
• Different tissues or non-tissues found in the body
• the measurement device may be configured to process the multiple sets of information sequentially, in parallel, or in groups to provide results.
• One exemplary method comprises of extracting the frequency domain information from the set of voltages using either a multi-bank filter set at appropriate frequencies or, using a Fourier Transform, building a set of equations based on the voltages at each frequency across the electrodes when the current is passed from at least a first electrode set into the bodily fluid, tissue and/or non-tissue, and received at second electrode set, solving these equations to determine the unknowns in such equations which can be transformed into dimensions of the given anatomical feature or features.
• the equations are solved simultaneously.
• the method of the invention works on exploiting these diversities to solve out the dimensions of the region of interests that could uniquely yield the response observed the electrodes. There are several methods used to perform this decomposition or transformation.
• a network may be provided with finite element unit characteristics of the subject's tissue embedded. This may accommodate different types of tissues.
• the tissue may include blood and fat.
• An electrical network with a first element (e.g., blood) associated by a first dimension may be provided.
• An electrical network with a second element (e.g., fat) associated by a second dimension may be provided.
• the various dimensions e.g., first and second dimension) may be altered until the desired response is achieved.
• the dimension values used to achieve the desired response may be the dimensions of the tissue.
• various tissues e.g., blood, fat, aorta
• the dimension value may be 0 or greater.
• the various dimension values may be varied, and calculations may be performed, to yield a result.
• De-embedding may include taking into consideration material properties of the devices, or device components, such as the wires or electrodes.
• an electrode may be at a distal end of a wire at the region of interest, and electronics to receive and process the signals may be provided at a proximal end of a wire.
• An electrical measurement taken by the distal electrode(s) is received by the electronics.
• a signal provided at one end of the wire may be altered by the time it reaches the other end of the wire due to material properties of the wire. This variation may be taken into account by using appropriate models based on the material characteristics, length of the wire, and other variables relevant to this situation, or performing measurements with known electrical loads at distal end and calibrating the effect of the in between electrical conductors.
• the exemplary method provides for the capability of making multiple frequency measurements at the same time. Further, all the measurements are made in the same phase of heart beat, such as in the systolic phase or diastolic phase. This overcomes the difficulty associated with overlaying multiple measurements made at different times to account for the phases of the heart.
• Some exemplary measurements made using the method described herein include, for example, but not limited to, dimension, nature of a specific region of interest like fat, stenosis, block, artery, bodily fluid pressure, bodily fluid flow rate, tissue characteristics, and the like, and combinations thereof.
• the exemplary method may be administered effectively as a tool in the form of a software program product.
• the invention provides a tool that uses the method of the invention.
• the software may comprise algorithm steps to generate multiple frequency pulse as delineated herein.
• the software may then be configured to excite the set of electrodes with the multiple frequency pulse.
• the software may further be configured to receive the multiple signals from the region of interest to be processed. Accordingly, the computing requirements for the effective execution of the software will become obvious to one skilled in the art.
• the other components required for the functioning of the tool will also be obvious to one of ordinary skill in the art, and may include, for example, a display module such as a monitor having a suitable resolution, an input module like a keyboard and a mouse, and so on.
• the invention provides a system that comprises the tool of the invention, which in turn is based on the exemplary method.
• the system is shown in block diagrammatic representation in FIG. 23, and depicted generally by numeral 330.
• the system comprises at least a set of electrodes 332 configured to be placed proximal to the region of interest.
• the set of electrodes is capable of being excited by a multiple excitation pulse.
• the multiple excitation pulse is made possible using pseudo random generator that involves using a suitable number of flipflops 334.
• the number of flipflops required depends on the complexity of the pulse to be generated, among other factors.
• the exact sequence to be executed by the pseudo random generator may be obtained using an input module 336.
• the input module may be configured to take manual inputs, or may be configured to automatically generate a sequence for the pseudo random generator to execute.
• a pseudo random sequence instead of a pseudo random sequence, an OFDM sequence may also be used with the associated electronics for generation of the OFDM sequence as would be known to one skilled in this art.
• the multiple excitation pulse generated is then sent through a D/A converter 338.
• the system further comprises a filter 340.
• the filter may be a passive or an active filter, depending on various factors, such as, the necessity, the requirement of the situation, computing abilities, cost, and so on, and combinations thereof. In some specific applications, the requirement of the filter may not be necessary and the filter may not be a part of the system.
• the filter comprises a passive multi-stage LC ladder network.
• the system further comprises a processing device 342 to process the input for a pseudo random generator.
• the processing device may also be configured to send the multiple excitation pulse to the set of electrodes.
• the system may also comprise a communicating device (not shown in FIG. 23) to communicate the pseudo random generator to the set of electrodes.
• the communication between different components and modules may be achieved through any wired or wireless means known to those skilled in the art, and the exact requirement may be arrived at without undue experimentation.
• the system also comprises a detector module 344 to detect the voltages developed across the electrodes.
• the detected signals may then be fed into the processing device for further processing.
• the signals may give rise to a wealth of information related to the region of interest, which the processing device is configured to unravel based on inputs such as, but not limited to, the signal, the algorithm, the region of interest characteristics, and the like.
• the system of the invention may be used to make multiple simultaneous frequency measurements, without having to resort to stitching of data acquired at different time points which may introduce errors into the final measurement.
• the excitation frequency band is chosen between 40KHz
• the pseudo random generator resides on a back end entity and is comprised of a chain of 9 D-flipflops referred to as flops, to represent a 9-tap pseudo random sequence.
• the generator polynomial used to generate the sequence is
• X9 + X4 + 1 0 (17) which would mean that the input of the last tap is an xor-ed output of the first and the fifth flops, as shown in FIG. 24.
• the flop outputs are all initialized to l 's to begin with (Reset condition).
• the D/A converter produced an output with frequencies spaced at 39.14 KHz.
• the output is passed through a bandpass filter whose pass band starts at a value lower than 39.14 KHz and ends above 10 MHz ensuring decent flatness over the entire band.
• the filter is designed using a passive multi-stage LC ladder network. Since the minimum frequency of the final composite signal is at 39.14 KHz, the signal rms value is maintained to be lower than 391 ⁇ .
• the choice of the sampling frequency and the tap length depends on the minimum and maximum frequencies of operation. As described before, the sampling frequency is at least twice the maximum desired frequency in the excitation, while the tap-length (L) is the nearest integer satisfying the relationship
• FIG. 25a shows the time domain waveform of the 9-tap pseudo random binary sequence generated as described herein.
• the current waveform has amplitude and rms value of 391 ⁇ .
• FIG. 25b shows a zoomed in portion of the exemplary pseudo random binary sequence in time domain.
• FIG. 26 shows the power spectral density of the same 9-tap pseudo random binary sequence generated.
• FIG. 27 shows the plot between phase angle and frequency for the 9-tap pseudo random binary sequence.
• the phase angles for each tone are adjusted so as to obtain the minimum PAR close to unity.
• IFFT Inverse Fast Fourier Transform
• the time domain OFDM sequence can also be produced at higher sampling rates using appropriate size of IFFT inputs.
• FIG. 29 shows such an OFDM time domain sequence that has been sampled at 80 MHz where the IFFT is performed on 2048 inputs. The inputs corresponding to frequencies higher than 10 MHz is zeroed. A higher sampling rate eases the requirement on anti-aliased filtering while increasing the complexity of the hardware in the transmit side.
• FIG. 30 shows an exemplary OFDM frequency response for the implementation of FIG. 28.
• a customised sequence is created using multiple coherent sinusoids added with appropriate phase angles so as to minimize the PAR.
• the resultant sequence may bear the property where any given frequency is not harmonically related to any other frequency.
• the same can also be constructed in the OFDM framework described above, where one or, more IFFT inputs are nulled to remove a set of tones from the original sequence.
• an exemplary method for exciting at least a set of electrodes configured to be placed in vivo proximal to a region of interest.
• the method comprises generating a multiple frequency sequence pulse having a predetermined peak to root-to-mean-square (rms) ratio close to unity, using multiple methods; and applying the multiple frequency sequence pulse to excite the at least the set of electrodes.

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• 2011
  • 2011-06-13 WO PCT/IB2011/052553 patent/WO2011158166A1/en active Application Filing
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