Patent Description:
This invention was made with government support under Contract Number <NUM> awarded by the National Science Foundation. The government has certain rights in the invention.

Some methods of determining blood volume status or related metrics of patient health involve invasive measurement of central venous pressure (CVP) or central arterial pressure (CAP) via insertion of a catheter. Unfortunately, CVP/CAP measurements can be slow to change in response to certain acute conditions and can lead to inadequate fluid administration. Fluid overload detection is difficult, whether caused by excessive fluid administration or pathological conditions. Fluid overload can lead to increased morbidity and mortality. Conventional vital sign monitoring fails to detect euvolemia or hypervolemia during resuscitation, often resulting in unguided and/or excessive fluid administration. <CIT> discloses methods for decomposing a physiological signal of a patient using empirical mode decomposition (EMD). In one embodiment, the EMD algorithm may involve identifying a frequency component, referred to as an intrinsic mode function, in the physiological signal. The physiological signal may be decomposed into one or more intrinsic mode functions through multiple iterations of the EMD algorithm. Each subsequent mode function may have a different frequency component of the original physiological signal input into the EMD algorithm. In some embodiments, each mode function may be further analyzed and/or processed to determine various physiological data corresponding to blood flow in the patient.

A first aspect of the disclosure is a method that includes generating, via a sensor of a computing device, a signal representing vibrations originating from a blood vessel of a subject and decomposing the signal into one or more first intrinsic oscillatory modes and one or more second intrinsic oscillatory modes. The one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes. The method includes obtaining an intensity spectrum of the one or more first intrinsic oscillatory modes over a range of frequencies and using the obtained intensity spectrum to determine a blood volume status of the subject.

A second aspect of the disclosure is a computing device that includes one or more processors, a sensor, a user interface, and a computer readable medium storing instructions that, when executed by the one or more processors, cause the computing device to perform functions. The functions include generating, via the sensor, a signal representing vibrations originating from a blood vessel of a subject and decomposing the signal into one or more first intrinsic oscillatory modes and one or more second intrinsic oscillatory modes. The one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes. The functions include obtaining an intensity spectrum of the one or more first intrinsic oscillatory modes over a range of frequencies and using the obtained intensity spectrum to determine a blood volume status of the subject.

A third aspect of the disclosure is a non-transitory computer readable medium storing instructions that, when executed by a computing device, cause the computing device to perform functions. The functions include generating, via a sensor of the computing device, a signal representing vibrations originating from a blood vessel of a subject and decomposing the signal into one or more first intrinsic oscillatory modes and one or more second intrinsic oscillatory modes. The one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes. The functions include obtaining an intensity spectrum of the one or more first intrinsic oscillatory modes over a range of frequencies and using the obtained intensity spectrum to determine a blood volume status of the subject.

A fourth aspect of the disclosure is a method that includes generating, via a sensor of a computing device, a signal representing vibrations originating from a blood vessel of a subject and decomposing the signal into one or more first intrinsic oscillatory modes and one or more second intrinsic oscillatory modes. The one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes. The method includes using the one or more second intrinsic oscillatory modes to determine one or more mechanical properties of the blood vessel or tissue adjacent to the blood vessel.

A fifth aspect of the disclosure is a computing device that includes one or more processors, a sensor, a user interface, and a computer readable medium storing instructions that, when executed by the one or more processors, cause the computing device to perform functions. The functions include generating, via the sensor, a signal representing vibrations originating from a blood vessel of a subject and decomposing the signal into one or more first intrinsic oscillatory modes and one or more second intrinsic oscillatory modes. The one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes. The functions include using the one or more second intrinsic oscillatory modes to determine one or more mechanical properties of the blood vessel or tissue adjacent to the blood vessel.

A sixth aspect of the disclosure is a non-transitory computer readable medium storing instructions that, when executed by a computing device, cause the computing device to perform functions. The functions include generating, via a sensor of the computing device, a signal representing vibrations originating from a blood vessel of a subject and decomposing the signal into one or more first intrinsic oscillatory modes and one or more second intrinsic oscillatory modes. The one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes. The functions include using the one or more second intrinsic oscillatory modes to determine one or more mechanical properties of the blood vessel or tissue adjacent to the blood vessel.

These, as well as other aspects, advantages, and alternatives will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying drawings. Further, it should be understood that this summary and other descriptions and figures provided herein are intended to illustrate the invention by way of example only and, as such, that numerous variations are possible.

As discussed above, determination of blood volume status via catheter insertion and measurement of central venous pressure (CVP) or central arterial pressure (CAP) has diagnostic value, but is inherently invasive and can be costly. Disclosed herein are methods and systems for using non-invasive venous waveform analysis (NIVA) to indirectly determine or detect blood volume status, CVP/CAP, mechanical in vivo properties of a subject's blood vessels, the presence of edema in the subject, and other subject metrics such as mean pulmonary arterial pressure, pulmonary artery diastolic pressure, left ventricular end diastolic pressure, left ventricular end diastolic volume, cardiac output, total blood volume, volume overload, dehydration, hemorrhage, and volume responsiveness. One or more of these metrics may be used to diagnose or treat various disorders that may afflict a subject or be used for real time assessment and resuscitation of a subject.

Methods disclosed herein generally involve non-invasively measuring a peripheral arterial waveform (PAW) or a peripheral vein waveform (PVW) using a (e.g., piezoelectric) sensor positioned over a subject's artery or vein (e.g., in contact with the subject's skin). The waveforms represent vibrations originating from the blood vessel of the subject and are generally caused by blood flowing though the vessel and/or the physiological reaction of the vessel or surrounding tissue to the blood flow. The sensor generates a signal representing the vibrations and a computing device can process the signal to decompose the signal into intrinsic oscillatory modes, using empirical mode decomposition (EMD) (e.g., a Hilbert-Huang transform) or ensemble EMD (EEMD). This technique allows for non-linear analysis of the signal, which is useful because the signal representing the blood vessel vibrations will generally take the form of a soliton. By decomposing the waveform, a pulse pressure waveform mode can be isolated from components of the signal representing motion effects and high frequency dissipative shear waves that are generated as a conical wake by the propagating vessel pressure pulse. As such, blood volume status identification can be performed with increased accuracy. In addition, these techniques enable the quantification of blood vessel mechanical properties from the higher order intrinsic oscillatory modes.

In a particular embodiment, the amplitude spectral density of the non-invasive indirect pulse waveform mode is generated by the computing device. The indirect pulse waveform mode generally consists of the full signal minus three to five of the higher order intrinsic oscillatory modes. A ratio of the amplitude of the heart rate and weighted amplitudes of the harmonics of the heart rate divided by the sum of the heart rate and the heart rate harmonics can be normalized to create an "estimated pulmonary capillary wedge pressure which is directly related to the subject's blood volume status. Pulmonary capillary wedge pressure is a well described measure of volume status. The mechanical attenuation properties of the blood vessels can be quantified from the high frequency dissipative shear wave mode. The edema state of the patient can be determined from the decomposed modes of the waveforms.

<FIG> is a simplified block diagram of an example computing device <NUM> that can perform various acts and/or functions, such as any of those described in this disclosure. The computing device <NUM> may be a mobile phone, a tablet computer, a laptop computer, a desktop computer, a wearable computing device (e.g., in the form of a wrist band), among other possibilities.

The computing device <NUM> includes one or more processors <NUM>, a data storage unit <NUM>, a communication interface <NUM>, a user interface <NUM>, a display <NUM>, and sensor(s) <NUM>. These components as well as other possible components can connect to each other (or to another device or system) via a connection mechanism <NUM>, which represents a mechanism that facilitates communication between two or more devices or systems. As such, the connection mechanism <NUM> can be a simple mechanism, such as a cable or system bus, or a relatively complex mechanism, such as a packet-based communication network (e.g., the Internet). In some instances, a connection mechanism can include a non-tangible medium (e.g., where the connection is wireless).

The processor <NUM> may include a general-purpose processor (e.g., a microprocessor) and/or a special-purpose processor (e.g., a digital signal processor (DSP)). In some instances, the computing device <NUM> may include more than one processor to perform functionality described herein.

The data storage unit <NUM> may include one or more volatile, non-volatile, removable, and/or non-removable storage components, such as magnetic, optical, or flash storage, and/or can be integrated in whole or in part with the processor <NUM>. As such, the data storage unit <NUM> may take the form of a non-transitory computer-readable storage medium, having stored thereon program instructions (e.g., compiled or non-compiled program logic and/or machine code) that, when executed by the processor <NUM>, cause the computing device <NUM> to perform one or more acts and/or functions, such as those described in this disclosure. Such program instructions can define and/or be part of a discrete software application. In some instances, the computing device <NUM> can execute program instructions in response to receiving an input, such as from the communication interface <NUM> and/or the user interface <NUM>. The data storage unit <NUM> may also store other types of data, such as those types described in this disclosure.

The communication interface <NUM> can allow the computing device <NUM> to connect to and/or communicate with another other device or system according to one or more communication protocols. The communication interface <NUM> can be a wired interface, such as an Ethernet interface or a high-definition serial-digital-interface (HD-SDI). The communication interface <NUM> can additionally or alternatively include a wireless interface, such as a cellular or WI-FI interface. A connection provided by the communication interface <NUM> can be a direct connection or an indirect connection, the latter being a connection that passes through and/or traverses one or more entities, such as such as a router, switcher, or other network device. Likewise, a transmission to or from the communication interface <NUM> can be a direct transmission or an indirect transmission.

The user interface <NUM> can facilitate interaction between the computing device <NUM> and a user of the computing device <NUM>, if applicable. As such, the user interface <NUM> can include input components such as a keyboard, a keypad, a mouse, a touch sensitive and/or presence sensitive pad or display, a microphone, a camera, and/or output components such as a display device (which, for example, can be combined with a touch sensitive and/or presence sensitive panel), a speaker, and/or a haptic feedback system. More generally, the user interface <NUM> can include any hardware and/or software components that facilitate interaction between the computing device <NUM> and the user of the computing device <NUM>.

In a further aspect, the computing device <NUM> includes the display <NUM>. The display <NUM> may be any type of graphic display. As such, the display <NUM> may vary in size, shape, and/or resolution. Further, the display <NUM> may be a color display or a monochrome display.

The sensor(s) <NUM> may take the form of a piezoelectric sensor, a pressure sensor, a force sensor, an optical wavelength selective reflectance or absorbance measurement system, a tonometer, an ultrasound probe, a plethysmograph, or a pressure transducer. Other examples are possible. The sensor(s) <NUM> may be configured to detect vibrations originating from a blood vessel of a subject as further described herein.

As indicated above, the connection mechanism <NUM> may connect components of the computing device <NUM>. The connection mechanism <NUM> is illustrated as a wired connection, but wireless connections may also be used in some implementations. For example, the communication mechanism <NUM> may be a wired serial bus such as a universal serial bus or a parallel bus. A wired connection may be a proprietary connection as well. Likewise, the communication mechanism <NUM> may also be a wireless connection using, e.g., Bluetooth® radio technology, communication protocols described in IEEE <NUM> (including any IEEE <NUM> revisions), cellular technology (such as GSM, CDMA, UMTS, EV-DO, WiMAX, or LTE), or Zigbee® technology, among other possibilities.

<FIG> depicts one embodiment of the computing device <NUM> and the sensor(s) <NUM>. In <FIG>, the sensor(s) <NUM> takes the form of a wearable wristband that is worn by a human subject and the computing device <NUM> takes the form of a mobile phone. The sensor(s) <NUM> may detect vibrations originating from a blood vessel at the subject's wrist and wirelessly transmit, via the connection mechanism <NUM> (e.g., via Bluetooth®), a signal representing the detected vibrations to the computing device <NUM>. The computing device <NUM> may receive the signal for further processing as described further herein.

<FIG> depicts the sensor(s) <NUM> as being incorporated into a wrist band <NUM> that is worn on a human wrist. The sensor(s) <NUM> (e.g., piezoelectric sensors) are positioned respectively over the dorsal vein <NUM>, the radial artery <NUM>, and the palmar vein <NUM>, and held in place by the tensioned wrist band <NUM>.

<FIG> is a block diagram of a method <NUM> for determining a blood volume status of a subject.

At block <NUM>, the method <NUM> includes generating, via a sensor of a computing device, a signal representing vibrations originating from a blood vessel of a subject. For example, the computing device <NUM>, via the sensor(s) <NUM>, may detect vibrations originating from a blood vessel (e.g., a vein wall or an artery wall) of a subject. The sensor(s) <NUM> can be positioned proximately to a peripheral vein or a peripheral artery of the subject to detect vibrations that originate from the peripheral vein or the peripheral artery.

The vibrations can be produced by fluid flowing through the blood vessel, can be produced by wall tension of the blood vessel, or can be produced by contraction or relaxation of the blood vessel in (e.g., physiological) response to the fluid flowing through the blood vessel. In a specific example, the sensor(s) <NUM> may be secured (e.g., via a Velcro strap) to the subject's skin above or near the blood vessel (see <FIG>). The sensor(s) <NUM> may detect the vibrations caused by blood flow through the blood vessel as the vibrations are conducted through tissues such as the subject's skin.

The subject may be human, but other animals are possible. As the sensor(s) <NUM> detects the vibrations, the subject may be breathing spontaneously, e.g., without the aid of a mechanical ventilator, or with the aid of a mechanical ventilator.

At block <NUM>, the method <NUM> includes decomposing the signal into one or more first intrinsic oscillatory modes and one or more second intrinsic oscillatory modes. In this context, the one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes.

Typically, the one or more first intrinsic oscillatory modes are useful for determining blood volume status of the subject or other subject related metrics, and the one or more second intrinsic oscillatory modes are useful for evaluating mechanical properties of the blood vessel or tissue adjacent to the blood vessel, as discussed below in the context of the method <NUM>.

An intrinsic oscillatory mode of the signal can be defined as a mode (e.g., a component of the signal) having a number of extrema and a number of zero-crossings that are equal or that differ by no more than one. At any point in time, a mean value of an envelope defined by the local maxima of the mode and an envelope defined by the local minima of the mode will generally be zero. The envelopes will typically be defined by a cubic spline line that connects the local maxima and a cubic spline line that connects the local minima.

In some embodiments, decomposing the signal includes performing an empirical mode decomposition (e.g., a Hilbert-Huang transform (HHT)) or an ensemble empirical mode decomposition upon the signal to identify the one or more first intrinsic oscillatory modes and the one or more (e.g., three, four, or five) second intrinsic oscillatory modes. The one or more second intrinsic oscillatory modes are generally the highest order (e.g., highest frequency) intrinsic oscillatory modes of the signal.

The HHT is an iterative (e.g., sifting) process for identifying intrinsic oscillatory modes of the signal. First, all local minima and local maxima are identified in the time-domain signal generated at block <NUM>. An upper envelope taking the form of a cubic spline line is generated to connect all of the local maxima of the signal, and a lower envelope taking the form of a cubic spline line is generated to connect all of the local minima of the signal. The time-dependent mean of the upper envelope and the lower envelope is then calculated and subtracted from the signal and the result is evaluated with respect to predetermined stoppage criteria (discussed in more detail below). If the result satisfies the stoppage criteria, the result is identified as the highest order intrinsic oscillatory mode (e.g., a first mode of the one or more second intrinsic oscillatory modes).

If the result of the first iteration of the process does not satisfy the stoppage criteria, another iteration of the process is performed. Another upper envelope taking the form of a cubic spline line can be generated to connect all of the local maxima of the result of the first iteration of the process, and a lower envelope taking the form of a cubic spline line can be generated to connect all of the local minima of the result of the first stage of the process. The time-dependent mean of the upper envelope and the lower envelope can then be subtracted from the result of the first iteration of the process and the result of the second iteration of the process can be evaluated with respect to the predetermined stoppage criteria. This process is repeated until the iterative result satisfies the stoppage criteria at which point a highest order intrinsic oscillatory mode has been identified.

Next, the identified intrinsic oscillatory mode can be subtracted from the signal generated at block <NUM> and the remaining portion of the signal is processed as described above to identify one or more additional intrinsic oscillatory modes.

In various embodiments, the computing device <NUM> determines a standard deviation of two consecutive iterative results of the sifting process and identifies the most recent result of the sifting process as an intrinsic oscillatory mode if the standard deviation is less than a threshold amount.

In other embodiments, the computing device <NUM> will continue the sifting process until the computing device <NUM> determines that for a threshold number of consecutive sifting processes the consecutive results have numbers of zero-crossings, local maxima, and local minima that are equal or at most differ by one. When these criteria are satisfied, the computing device <NUM> identifies the most recent result of the sifting process as an intrinsic oscillatory mode.

In other embodiments, the sifting process is continued until the most recent result is a monotonic function, in which case the result of the sifting process preceding the monotonic function is identified as an intrinsic oscillatory mode.

Due to the nature of the sifting process, the one or more second intrinsic oscillatory modes (e.g., higher frequency modes) are identified prior to the identification of the one or more first intrinsic oscillatory modes (e.g., lower frequency modes). In fact, the one or more second intrinsic oscillatory modes are generally used by the computing device <NUM> to further identify the one or more first intrinsic oscillatory modes.

At block <NUM>, the method <NUM> includes obtaining an intensity spectrum of the one or more first intrinsic oscillatory modes over a range of frequencies (e.g., <NUM>-<NUM>). In the invention, the computing device <NUM> performs a fast Fourier transform (FFT) upon the one or more first intrinsic oscillatory modes of the signal representing the lower frequency vibrations originating from the blood vessel. Frequencies of interest such as a subject's respiratory rate, a pulse rate, and harmonics or multiples of the pulse rate may take the form of "peaks" within the obtained intensity spectrum. Such peaks may take the form of local (or global) maxima of signal intensity with respect to signal frequency. The Fourier transform may be non-linear or linear and may additionally involve the performance of an autocorrelation function upon the one or more first intrinsic oscillatory modes.

At block <NUM>, the method <NUM> includes using the obtained intensity spectrum to determine a blood volume status of the subject. In some embodiments, the computing device <NUM> can additionally or alternatively use the obtained intensity spectrum to determine subject metrics such as a pulmonary capillary wedge pressure (PCWP), a mean pulmonary arterial pressure, a pulmonary artery diastolic pressure, a left ventricular end diastolic pressure, a left ventricular end diastolic volume, a cardiac output, total blood volume, and a volume responsiveness of the subject.

In particular, the ratio of a peak corresponding to the subject's heart rate and a peak corresponding to a frequency that is double the subject's heart rate can be useful in determining blood volume status. For example, the computing device <NUM> can use the obtained intensity spectrum to generate a numerical score that represents the blood volume status or any of the subject metrics discussed above.

In some examples, the above methods can be performed both before and after treatment of the subject to determine the effectiveness of the treatment (e.g., to determine if fluid administration has altered the subject's blood volume to a more desirable level). For instance, the subject may be suffering from increased or decreased cardiac output compared to control, or increased or decreased intravascular volume status compared to control. Additionally or alternatively, the subject could be scheduled to undergo cardiac catheterization or have undergone cardiac catheterization to determine cardiac output or volume status. By further example, the subject could have or be under the effect of one or more of pneumonia, cardiac disorders, sepsis, asthma, obstructive sleep apnea, hypopnea, anesthesia, abnormal pain, or narcotic use.

In some examples, the computing device <NUM> can use the determined blood volume status or other determined metrics to determine an effect that administering a fluid to the subject would have on the subject (e.g., an increase, a decrease, or no change in cardiac output or blood volume status).

In some examples, the computing device <NUM> can use the determined blood volume status or other determined metrics to diagnose respiratory distress or hypoventilation in the subject.

In some embodiments, the computing device <NUM> can use the determined blood volume status or other determined metrics to provide, via the user interface <NUM>, an indication of the determined blood volume status or other determined metrics. For example, the computing device <NUM> can determine that the determined blood volume status indicates hypovolemia or hypervolemia, and provide, via the user interface <NUM>, an indication that the determined blood volume status indicates hypovolemia or hypervolemia in the subject.

In particular embodiments, the computing device <NUM> can adjust (e.g., in real time) a flow rate of fluid that is provided intravenously to the subject based on the determined blood volume status or other subject metrics.

In some embodiments, the computing device <NUM> uses the obtained intensity spectrum to determine a heart rate of the subject and provides, via the user interface <NUM>, an indication of the determined heart rate.

In particular embodiments, the computing device <NUM> makes a determination, via an accelerometer (e.g., part of the sensors <NUM>) of the computing device <NUM>, that a current rate of movement of the subject is less than a threshold rate of movement. In response, the computing device <NUM> can perform the method <NUM> or the method <NUM> and/or related actions. This can help prevent the computing device <NUM> from performing processing operations during subject movement (e.g., exercise) that might erroneously alter determinations of various subject metrics.

Block <NUM> may involve using known statistical correlations between previously collected intensity spectra of subject blood vessel vibrations and the aforementioned subject metrics. For example, blood vessel vibration data may be collected for a number of subjects while one or more of the aforementioned metrics are directly measured for each of the subjects. This data may then be used to determine statistical correlations between the collected blood vessel vibration data and the aforementioned subject metric data. More specifically, such correlations between the blood vessel vibration data and the subject metric data can be approximated as mathematical functions using various statistical analysis or "curve fitting" techniques (e.g., least squares analysis). As such, future subject metrics may be determined indirectly (e.g., without direct measurement) and non-invasively with the sensor(s) <NUM> by performing the identified mathematical functions upon subsequently collected blood vessel vibration intensity data.

Any of the aforementioned subject metrics that are determined using the above methods may be used to diagnose or treat one or more of the following disorders: hypervolemia, hypovolemia, euvolemia, dehydration, heart failure, tissue hypoperfusion, myocardial infarction, hypotension, valvular heart disease, congenital heart disease, cardiomyopathy, pulmonary disease, arrhythmia, drug effects, hemorrhage, systemic inflammatory response syndrome, infectious disease, sepsis, electrolyte imbalance, acidosis, renal failure, hepatic failure, cerebral injury, thermal injury, cardiac tamponade, preeclampsia/eclampsia, or toxicity. The determined subject metrics may also be used to diagnose respiratory distress or hypoventilation due to one or more of the following conditions: pneumonia, cardiac disorders, sepsis, asthma, obstructive sleep apnea, hypopnea, anesthesia, pain, or narcotic use.

The method <NUM> and related functionality is described in more detail below with reference to <FIG>.

<FIG> is a block diagram of a method <NUM> for determining one or more mechanical properties of a subject's blood vessel or tissue adjacent to the blood vessel.

At block <NUM>, the method <NUM> includes generating, via a sensor of a computing device, a signal representing vibrations originating from a blood vessel of a subject. The computing device <NUM> can perform block <NUM> in any manner similar to block <NUM> described above.

Typically, the one or more first intrinsic oscillatory modes are useful for determining blood volume status of the subject or other subject related metrics, and the one or more second intrinsic oscillatory modes are useful for evaluating mechanical properties of the blood vessel or tissue adjacent to the blood vessel.

The computing device <NUM> can perform block <NUM> in any manner similar to block <NUM> described above.

At block <NUM>, the method <NUM> includes using the one or more second intrinsic oscillatory modes (e.g., a dissipative shear waveform) to determine one or more mechanical properties of the blood vessel or tissue adjacent to the blood vessel.

In particular embodiments, the one or more second intrinsic oscillatory modes include one to three intrinsic oscillatory modes. In this context, the method <NUM> can further involve using the one to three (e.g., two) intrinsic oscillatory modes to determine whether the subject has edema. Additionally, the computing device <NUM> can display an indication of whether the subject has edema.

In some embodiments, using the one or more second intrinsic oscillatory modes to determine one or more mechanical properties of the blood vessel or tissue adjacent to the blood vessel includes generating and/or displaying a numerical score that represents the one or more mechanical properties.

In particular embodiments, using the one or more second intrinsic oscillatory modes to determine one or more mechanical properties of the blood vessel or tissue adjacent to the blood vessel includes determining a logarithmic decrement of the one or more second intrinsic oscillatory modes. The logarithmic decrement can be indicative of the mechanical properties as described below.

In some embodiments, using the one or more second intrinsic oscillatory modes to determine one or more mechanical properties of the blood vessel or tissue adjacent to the blood vessel includes determining a Q-factor of the one or more second intrinsic oscillatory modes. The Q-factor can be indicative of the mechanical properties as described below.

In particular embodiments, using the one or more second intrinsic oscillatory modes includes determining an anelastic coefficient of the one or more second intrinsic oscillatory modes.

In some examples, the method <NUM> is performed prior to carrying out a treatment of the subject and after carrying out the treatment to evaluate the treatment's effectiveness.

In particular embodiments, the user interface <NUM> provides an indication of the determined one or more mechanical properties of the blood vessel or adjacent tissue. As such, the one or more mechanical properties may indicate arteriosclerosis, edema, and/or elevated risk of aneurysm and the user interface <NUM> can provide an indication that the determined mechanical properties indicates arteriosclerosis, edema, and/or elevated risk of aneurysm.

In some examples, the method <NUM> involves determining a first amount of energy represented by the one or more first intrinsic oscillatory modes and a second amount of energy represented by the one or more second intrinsic oscillatory modes and using the determined first amount of energy and the determined second amount of energy to determine whether stiffening, plaque buildup, and/or other abnormal conditions are present in blood vessels of the subject.

<FIG> depicts an arm <NUM> of a human subject and waveforms <NUM>, <NUM>, <NUM>, and <NUM> associated with methods disclosed herein. The sensor <NUM> is shown positioned over a vein of the subject, with the sensor <NUM> held in place by the wrist band <NUM>. The waveforms <NUM>, <NUM>, and <NUM> can each represent an intrinsic oscillatory mode of the waveform <NUM> or a superposition of two or more intrinsic oscillatory modes of the waveform <NUM>. The waveform <NUM> represents a full (e.g., undecomposed) signal detected by the sensor <NUM>, also referred to herein as a peripheral venous waveform (PVW). The waveform <NUM> can be referred to herein as a high frequency dissipative shear waveform mode. The waveform <NUM> can be referred to herein as a venous pressure pulse waveform mode (e.g., the one or more first intrinsic oscillatory modes mentioned in the description of blocks <NUM> and <NUM> above). The waveform <NUM> can be referred to herein as a mean venous pressure waveform mode. Adding the three waveforms <NUM>, <NUM> and <NUM> yields the original PVW denoted as waveform <NUM>. Amplitude spectral density (ASD) analyses are conducted by the computing device <NUM> on the recomposed venous pressure pulse waveform mode waveform <NUM>, and the amplitudes of the respective frequencies from the ASD correlate to the blood volume status of the subject as described further below.

<FIG> depicts an arm <NUM> of a human subject and waveforms <NUM>, <NUM>, <NUM>, and <NUM> associated with methods disclosed herein. The sensor <NUM> is shown positioned over an artery of the subject, with the sensor <NUM> held in place by the wrist band <NUM>. The waveforms <NUM>, <NUM>, and <NUM> can each represent an intrinsic oscillatory mode of the waveform <NUM> or a superposition of two or more intrinsic oscillatory modes of the waveform <NUM>. The waveform <NUM> represents a full (e.g., undecomposed) signal detected by the sensor <NUM>, also referred to herein as a peripheral arterial waveform (PAW). The waveform <NUM> can be referred to herein as a high frequency dissipative shear waveform mode. The waveform <NUM> can be referred to herein as an arterial pressure pulse waveform mode (e.g., the one or more first intrinsic oscillatory modes mentioned in the description of blocks <NUM> and <NUM> above). The waveform <NUM> can be referred to herein as a mean arterial pressure waveform mode. Adding the three waveforms <NUM>, <NUM> and <NUM> yields the original PAW denoted as waveform <NUM>. Amplitude spectral density (ASD) analyses are conducted by the computing device <NUM> on the recomposed arterial pressure pulse waveform mode waveform <NUM>, and the amplitudes of the respective frequencies from the ASD correlate to the blood volume status of the subject as described further below.

<FIG> shows the time-dependent PVW waveform <NUM> which can be decomposed into its intrinsic oscillatory modes by the computing device <NUM>. Some of the intrinsic oscillatory modes are shown collectively as waveforms <NUM>. Typically, up to fourteen (<NUM>) intrinsic oscillatory modes can be isolated from the PVW waveform <NUM> using processes such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and/or a Hilbert-Huang transform (HHT). The decomposition of the PVW waveform <NUM> into its intrinsic oscillatory modes generally begins with the shortest period oscillatory mode first being identified, that mode then being subtracted from the original PVW waveform <NUM>, and the next shortest period oscillatory mode is found, and so on, until all the intrinsic oscillatory modes are determined as shown collectively (in part) as the waveforms <NUM>. The sum of all of the intrinsic oscillatory modes yields the original PVW waveform <NUM>. The intrinsic oscillatory modes are general in nature and can accommodate non-linear waveform analysis, and unlike constant amplitude and/or frequency in a simple harmonic component, the intrinsic oscillatory modes can have variable amplitude and frequency along the time axis.

<FIG> shows the time-dependent PAW waveform <NUM> which can be decomposed into its intrinsic oscillatory modes by the computing device <NUM>. Some of the intrinsic oscillatory modes are shown collectively as waveforms <NUM>. Typically, up to fourteen (<NUM>) intrinsic oscillatory modes can be isolated from the PAW waveform <NUM> using processes such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and/or a Hilbert-Huang transform (HHT). The decomposition of the PAW waveform <NUM> into its intrinsic oscillatory modes generally begins with the shortest period oscillatory mode first being identified, that mode then being subtracted from the original PAW waveform <NUM>, and the next shortest period oscillatory mode is found, and so on, until all the intrinsic oscillatory modes are determined as shown collectively (in part) as the waveforms <NUM>. The sum of all of the intrinsic oscillatory modes yields the original PAW waveform <NUM>. The intrinsic oscillatory modes are general in nature and can accommodate non-linear waveform analysis, and unlike constant amplitude and/or frequency in a simple harmonic component, the intrinsic oscillatory modes can have variable amplitude and frequency along the time axis.

<FIG> shows the PVW waveform <NUM>, and the two recomposed waveforms <NUM> and <NUM> each representing a superposition of a plurality of intrinsic oscillatory modes of the PVW waveform <NUM>. The waveform <NUM> can be referred to herein as the high frequency dissipative shear waveform mode, in some cases composed of the sum of first four (<NUM>) (e.g., highest frequency) intrinsic oscillatory modes. Thus, the waveform <NUM> can represent the one or more second intrinsic oscillatory modes referred to herein. The waveform <NUM> can be referred to herein as the venous pressure pulse waveform mode, being the sum of typically the next five (e.g., lower frequency) intrinsic oscillatory modes of the PVW. Thus, the waveform <NUM> can represent the one or more first intrinsic oscillatory modes referred to herein. The number of the short period intrinsic modes that compose the waveform <NUM>, depend on the sensor type, its housing and how it is incorporated into the wrist band strap, and its attachment to the subject. The number of modes composed in waveform <NUM> can be automatically calculated by the computing device <NUM> from a ASD analysis, since the sum of the intrinsic modes has energy predominantly in the second order heart rate frequency harmonic and higher harmonics. Typically, the first two (<NUM>) (e.g., highest frequency) intrinsic modes are of such low amplitude and high frequency as to be ignored in further analysis for healthy patients. However, for patients suffering from edema, high frequency pressure waves are excited and reflected by the propagating pressure pulse due to presence of fluids surrounding the venous blood vessels, and as such the edema state of the patient can be correlated to the energy composed in this intrinsic mode and in higher intrinsic modes. As depicted in <FIG>, the computing device <NUM> can calculate and display (e.g., in real time) these recomposed waveforms <NUM> and <NUM>, thus providing valuable insight into the characteristics of the subject.

The high frequency highly dissipative waveform mode <NUM> is typical of the high frequency shear waves that are generated by the propagating venous pressure pulse as a highly dissipative conical wake of high frequency shear waves. Typically, the next five intrinsic modes, the fifth, sixth, seventh and eighth modes, are summed to yield a venous pulse pressure waveform <NUM>. The initiation, peak, and attenuation of the highly dissipative shear waveforms <NUM> can be seen to be correlated to the propagating venous pulse pressure waveform <NUM>. The ratio of the energy in the waveform <NUM> compared to energy in the waveform <NUM> is typically ~<NUM>% for the palmar and dorsal veins respectively for a healthy subject, and values that deviate from these values indicate stiffening, biological aging, arteriosclerosis, disease and plaque buildup in the patient's blood vessels.

<FIG> shows the PAW waveform <NUM>, and the two recomposed waveforms <NUM> and <NUM> each representing a superposition of a plurality of intrinsic oscillatory modes of the PAW waveform <NUM>. The waveform <NUM> can be referred to herein as the high frequency dissipative shear waveform mode, in some cases composed of the sum of first four (<NUM>) (e.g., highest frequency) intrinsic oscillatory modes. Thus, the waveform <NUM> can represent the one or more second intrinsic oscillatory modes referred to herein. The waveform <NUM> can be referred to herein as the arterial pressure pulse waveform mode, being the sum of typically the next five (e.g., lower frequency) intrinsic oscillatory modes of the PAW. Thus, the waveform <NUM> can represent the one or more first intrinsic oscillatory modes referred to herein. The number of the short period intrinsic modes that compose the waveform <NUM>, depend on the sensor type, its housing and how it is incorporated into the wrist band strap, and its attachment to the subject. The number of modes composed in waveform <NUM> can be automatically calculated by the computing device <NUM> from a ASD analysis, since the sum of the intrinsic modes has energy predominantly in the second order heart rate frequency harmonic and higher harmonics. Typically, the first two (<NUM>) (e.g., highest frequency) intrinsic modes are of such low amplitude and high frequency as to be ignored in further analysis for healthy patients. However, for patients suffering from edema, high frequency pressure waves are excited and reflected by the propagating pressure pulse due to presence of fluids surrounding the arterial blood vessels, and as such the edema state of the patient can be correlated to the energy composed in this intrinsic mode and in higher intrinsic modes. As depicted in <FIG>, the computing device <NUM> can calculate and display (e.g., in real time) these recomposed waveforms <NUM> and <NUM>, thus providing valuable insight into the characteristics of the subject.

The high frequency highly dissipative waveform mode <NUM> is typical of the high frequency shear waves that are generated by the propagating arterial pressure pulse as a highly dissipative conical wake of high frequency shear waves. Typically, the next five intrinsic modes, the fifth, sixth, seventh and eighth modes, are summed to yield an arterial pulse pressure waveform <NUM>. The initiation, peak, and attenuation of the highly dissipative shear waveforms <NUM> can be seen to be correlated to the propagating arterial pulse pressure waveform <NUM>. The ratio of the energy of typically the two (<NUM>) highest frequency intrinsic modes (e.g., the waveform <NUM>) to the energy contained in the waveform <NUM> quantify the degree of edema presence in the patient. The ratio of the energy in the waveform <NUM> compared to energy in the waveform <NUM> is typically ~<NUM>% for the arteries of a healthy subject, and values that deviate from these values indicate stiffening, biological aging, arteriosclerosis, disease and plaque buildup in the patient's blood vessels.

<FIG> shows an amplitude spectral density (ASD) plot with respect to oscillation frequency of the waveforms <NUM> and <NUM> (see <FIG> and <FIG>) for a subject prior to exercise. In practice, the waveform <NUM> could correspond to the dorsal vein <NUM> or the palmar vein <NUM>, and the waveform <NUM> could correspond to the radial artery <NUM>. There are four (<NUM>) collections of prominent peaks in <FIG>, namely peaks <NUM>, <NUM>, and <NUM>, and a higher order collection of peaks that is not marked. Peak <NUM> corresponds to the heart rate of the subject, peak <NUM> corresponds to the first higher order harmonic (e.g., double the heart rate), and peak <NUM> corresponds to the second higher order harmonic (e.g., triple the heart rate), and so on. The respective amplitudes <NUM> of the peaks <NUM> have been mutually normalized for the radial artery <NUM>, the dorsal vein <NUM>, and the palmar vein <NUM>. It is the ratio of the amplitudes of the peaks <NUM> to the peaks <NUM> that is generally of interest for determining blood volume status. In this data set, the ratio of the peak <NUM> to the peak <NUM> for the radial artery <NUM> is <NUM>. The ratio of peak <NUM> to peak <NUM> for the dorsal vein <NUM> is <NUM>. The ratio of peak <NUM> to peak <NUM> for the palmar vein <NUM> is <NUM>.

<FIG> shows an amplitude spectral density (ASD) plot of the non-invasive indirect recomposed peripheral pressure pulse waveforms <NUM> and <NUM> (see <FIG> and <FIG>). The waveform <NUM> (e.g., the one or more first intrinsic oscillatory modes) typically represents a sum of the fifth, sixth, seventh and eighth intrinsic oscillatory modes of the PVW <NUM> prior to exercise for the dorsal vein <NUM> or the palmar vein <NUM>. The waveform <NUM> (e.g., the one or more first intrinsic oscillatory modes) typically represents a sum of the fifth, sixth, seventh and eighth intrinsic oscillatory modes of the PAW <NUM> prior to exercise for the radial artery <NUM>. There are two (<NUM>) distinct (marked) collections of peaks in <FIG>. Peak <NUM> corresponds to the heart rate of the subject and peak <NUM> corresponds to the first higher order harmonic (e.g., double the heart rate). The amplitudes <NUM> of the peak <NUM> have been normalized for the artery and the two veins measured, and it is the ratio of the amplitudes of the peak <NUM> to the peak <NUM> that is generally of interest for determining blood volume status. As denoted for the two veins measured, after isolating the waveform <NUM>, these amplitude ratios are now almost equal for the two veins <NUM> and <NUM>, being about <NUM>, while the amplitude ratio for the radial artery <NUM> (waveform <NUM>) is <NUM>.

<FIG> shows an amplitude spectral density (ASD) plot of the non-invasive indirect recomposed peripheral pressure pulse waveforms <NUM> (e.g., the one or more first intrinsic oscillatory modes) and <NUM> (e.g., the one or more first intrinsic oscillatory modes), being the sum of the last seven EEMD intrinsic oscillatory modes of the waveforms <NUM> and <NUM>, respectively, for a patient prior to exercise, for the radial artery <NUM>, the dorsal vein <NUM>, and the palmar vein <NUM>. There are two (<NUM>) distinct collections of peaks <NUM> and <NUM> in <FIG>. Peak <NUM> corresponds to the subject's heart rate and peak <NUM> is its first higher order harmonic (e.g., double the heart rate). The amplitudes <NUM> of the peak <NUM>, have been mutually normalized for the artery and the two veins measured, and it is the ratio of the amplitudes of the peak <NUM> to the peak <NUM> that is generally of interest for determining the blood volume status of the patient. These amplitude ratios are now almost equal for the artery and the two veins, being about <NUM>.

<FIG> shows an amplitude spectral density (ASD) plot of the non-invasive indirect peripheral pressure pulse waveforms <NUM> (e.g., the one or more first intrinsic oscillatory modes) and <NUM> (e.g., the one or more first intrinsic oscillatory modes) for a subject following exercise and a loss of blood fluids, for the radial artery <NUM>, the dorsal vein <NUM>, and the palmar vein <NUM>. There are three (<NUM>) distinct collections of peaks <NUM>, <NUM>, and <NUM> in <FIG>. Peak <NUM> corresponds to the subject's heart rate, peak <NUM> corresponds to the first higher order harmonic (e.g., double the heart rate), and peak <NUM> corresponds to the second higher order harmonic (e.g., triple the heart rate), and so on. The amplitudes <NUM> of the peak <NUM> have been normalized for the artery and two veins measured, and it is the ratio of the amplitudes of the peak <NUM> to the peak <NUM> that is generally of interest for determining the blood volume status of the subject. These amplitude ratios are not equal for the two veins measured, being <NUM> and <NUM> for the dorsal vein <NUM> and the palmar vein <NUM> respectively, while the amplitude ratio for the radial artery is about <NUM>.

<FIG> shows an amplitude spectral density (ASD) plot of the non-invasive indirect peripheral pressure pulse waveforms <NUM> (e.g., the one or more first intrinsic oscillatory modes) and <NUM> (e.g., the one or more first intrinsic oscillatory modes), being the sum of the fifth, sixth, seventh and eighth EMD intrinsic oscillatory modes of the waveforms <NUM> and <NUM>, respectively for a patient following exercise and a loss of blood fluids, for the radial artery <NUM>, for the dorsal vein <NUM>, and the palmar vein <NUM>. There are two (<NUM>) distinct (marked) collections of peaks in <FIG>. Peak <NUM> corresponds to the subject's heart rate and peak <NUM> corresponds to the first higher order harmonic (e.g., double the heart rate). The amplitudes <NUM> of the peak <NUM> have been mutually normalized for the artery and the two veins measured, and it is the ratio of the amplitudes <NUM> of the peak <NUM> to the amplitudes <NUM> of the peak <NUM> that is generally of interest for determining the blood volume status of the subject. These amplitude ratios are equal for the artery and the two veins, being <NUM>. The subject data shown in <FIG> is for a state of a loss of blood fluids, compared to the same subject prior to exercise, as shown in <FIG>. The ratio of the amplitude peaks of the second to the first harmonics represent an absolute measure of the patient blood volume state, with the amplitude ratio rising from <NUM> to <NUM>, upon the patient experiencing a loss of blood fluids, and this amplitude ratio is a direct representation of the subject's blood volume status.

The patient in <FIG> has just completely mild exercise, and as such their augmentation index is zero, since the body has adjusted the arterial vessels' compliance to be matched during exercise, and thus the arterial waves do not have any reflected "backward" traveling waves. In this state, the amplitude ratio as determined by EMD for the subject are the same for measurements over an artery or a vein, and represents an absolute value of the subject's blood volume status. The data represented by <FIG> was collected prior to exercise, and as such the subject's augmentation index was high, and thus the arterial waves have reflected "backward" traveling waves, and in this state, the amplitude ratio was determined by EEMD for the patient PAW.

This data confirms that the pulse waveform in both arteries and veins takes the form of a soliton, since encoded data in the pulse is maintained as the pulse travels from the heart, through the arteries and onward to the veins. The subject was evaluated prior to exercise, and thus had a high augmentation index, and thus reflected "backward" traveling waves are present in the arteries, and is the reason for the difference between the amplitude ratios of the artery compared to the veins, using the EMD method. In this case, the amplitude ratio from the venous data represents an absolute value of the subject's blood volume status. To remove the reflected "backward" traveling wave from the artery represented in <FIG> typically requires a non-linear procedure since the superposition of two (<NUM>) solitons is not linear. Due to the close proximity of reflectors in the artery, such as junction, termination, etc., the PAW becomes more complex especially from the reflected "backward" traveling wave. In this case, EMD tends to mode mix the intrinsic oscillatory modes, and therefore EEMD replaces EMD for the mode decomposition of the PAW, as shown in <FIG>, with the amplitude ratios shown being equal for both the artery and the veins as a value of <NUM>, representing the subject's blood volume status.

<FIG> shows the PVW waveform <NUM>, the venous pressure pulse waveform mode <NUM>, and the high frequency highly dissipative shear waveform (venous) mode <NUM> (e.g., the one or more second intrinsic oscillatory modes). The high frequency highly dissipative waveform mode <NUM> is typical of the high frequency shear waves that are generated by the propagating venous pressure pulse as a highly dissipative conical wake of high frequency shear waves. The initiation, peak, and attenuation of the highly dissipative shear waveforms <NUM> can be seen to be correlated with the propagating venous pulse pressure waveform <NUM>. The high frequency highly dissipative waveform mode <NUM> is initiated and generated by the propagating venous pressure pulse waveform <NUM> as a conical wake as shown by viewing the superimposed time histories of <NUM> and <NUM> as depicted. The rise form of <NUM> denoted as <NUM> is dependent on the pulse waveform <NUM>, its propagating velocity and the properties of the blood and venous blood vessels. The attenuation or decay of <NUM> as denoted by <NUM> is dependent on the material properties of the venous blood vessels. The attenuation or decay can be computed via the logarithm decrement and the period of oscillation to yield the natural frequency and damping coefficient of the venous blood vessels walls in the vicinity of the intravenous line inserted in the subject. This data can be used to assess the state of the subject's venous blood vessels and also quantify over time any change in the state of the subject's fistulas used for dialysis treatment.

In equation (<NUM>), "Q" represents a quality factor and δ is the logarithmic decrement of the waveform <NUM>. The logarithmic decrement δ denoted by <NUM> of the waveform <NUM> is typically about <NUM> for a healthy patient, yielding a quality factor of about Q=<NUM>. Healthy arterial blood vessels have a quality factor of about Q≈<NUM> and healthy venous blood vessels have a quality factor of about Q≈<NUM>. Q values greater than these values quantify the lack of anelasticity of the blood vessels, due to biological aging, arteriosclerosis, and/or disease. In the case of arteries, a Q><NUM> leads to increased circumferential tensile stresses at the artery inner wall due to the artery pressure pulse, and can lead to higher likelihood of aneurysms. The ratio of <NUM>/Q is the normalized energy lost due to anelasticity of the blood vessel, during a complete load/unload (pressurize/depressurize) cycle as the pressure pulse travels along the blood vessel.

<FIG> shows the PAW waveform <NUM>, the arterial pressure pulse waveform mode <NUM>, and the high frequency highly dissipative shear waveform (arterial) mode <NUM>. The high frequency highly dissipative waveform mode <NUM> is typical of the high frequency shear waves that are generated by the propagating arterial pressure pulse as a highly dissipative conical wake of high frequency shear waves. The initiation, peak and attenuation of the highly dissipative shear waveform <NUM> can be seen to be correlated to the propagating arterial pulse pressure waveform <NUM>. The high frequency highly dissipative waveform mode <NUM> is initiated and generated by the propagating arterial pressure pulse waveform <NUM> as a conical wake as shown by viewing the superimposed time histories of <NUM> and <NUM> as depicted. The rise form of <NUM> denoted as <NUM> is dependent on the pulse waveform of <NUM>, its propagating velocity and the properties of the blood and arterial blood vessels. The attenuation or decay of <NUM> as denoted by <NUM> is dependent on the material properties of the arterial blood vessels. The attenuation or decay can be computed via the logarithm decrement and the period of oscillation to yield the natural frequency and damping coefficient of the arterial blood vessels walls in the vicinity of the intravenous line inserted in the patient. These data can assess the state of the patient's arterial blood vessels and also quantify over time any change in the state of a patient's fistulas used for dialysis treatment.

<FIG> shows a thick wall anelastic power model <NUM> of a blood vessel, with an inner wall radius <NUM> and an outer wall radius <NUM>. <FIG> also shows a pulse pressure plot versus change in area of an Ovine artery <NUM>, with the artery in vitro pressure area plot data <NUM>, and the thick wall anelastic power model fit <NUM>, for both the loading (pressurizing) <NUM> and unloading (depressurizing) <NUM> pulse pressure paths, with loading and unloading anelastic power law model fit shown as <NUM>. <FIG> also shows the pulse pressure plot versus change in area of an Ovine vein <NUM>, with the vein in vitro pressure versus area plot data <NUM>, and the thick wall anelastic power model fit <NUM>, for both the loading (pressurizing) <NUM> and unloading (depressurizing) <NUM> pulse pressure paths, with the anelastic power law model fit shown as <NUM>.

The anelastic thick wall power law model is given as:
<MAT>.

Where ΔA is the change in incremental cross-section area, A is the original cross-section area, α is a stiffness coefficient, Δp is the incremental pulse pressure above diastolic, and β is the power law coefficient, that can be different for the loading (pressurizing) path, as βL, and βU for the unloading (depressurizing) path.

The power law coefficients that best fit the Ovine artery in vitro data in <FIG> are the same for the loading and unloading paths. That is, βL = βU, having a value of about <NUM> for a healthy artery. The importance of healthy anelastic arterial power law coefficients on a subject's state of health can be quantified from the thick wall anelastic power model as given by:
<MAT>.

Where σθ is the circumferential wall stress at a radius of r, "a" is the inner wall radius, and "b" is the outer wall radius, with σθ denoted as a tensile stress for negative values. From equation (<NUM>), and assuming an artery power law coefficient of exactly <NUM>, the circumferential wall stress is a constant throughout the wall thickness, i.e. the inner wall tensile circumferential stress is equal to outer wall circumferential stress, and is the optimum case to minimize the inner wall circumferential tensile stress to be a minimum for a positive pulse pressure.

The Quality factor (Q) and the anelastic power law coefficient (β) are related by:
<MAT>.

<FIG> shows the thick wall anelastic power law model <NUM>, a quantified circumferential tensile stress ratio versus Q plot, with the relationship of the ratio of inner wall to outer wall circumferential tensile stress denoted as <NUM>, for an artery of b/a=<NUM>. At a Q=<NUM> denoted at <NUM>, the inner and outer wall circumferential tensile stresses are equal yielding a stress ratio of <NUM> as shown at <NUM>. If a loss of anelasticity of the artery were to occur to increase the artery's Q value to <NUM>, for example, the inner wall circumferential tensile stress is <NUM>% higher than the outer wall circumferential tensile stress corresponding to a tensile stress ratio <NUM> denoted at <NUM>.

Claim 1:
A method comprising:
(a) generating (<NUM>), via a sensor (<NUM>) of a computing device (<NUM>), a signal representing vibrations originating from a blood vessel of a subject;
(b) decomposing (<NUM>) the signal into one or more first intrinsic oscillatory modes (<NUM>, <NUM>) and one or more second intrinsic oscillatory modes (<NUM>, <NUM>) wherein the one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes;
(c) obtaining (<NUM>) an intensity spectrum of the one or more first intrinsic oscillatory modes over a range of frequencies, wherein obtaining the intensity spectrum comprises performing a fast Fourier transform (FFT) upon the one or more first intrinsic oscillatory modes to yield one or more intensities corresponding respectively to one or more frequencies of the vibrations; and
(d) using (<NUM>) the obtained intensity spectrum to determine a blood volume status of the subj ect.