SYSTEM AND METHOD FOR CONTINUOUS NON-INVASIVE BLOOD PRESSURE MEASUREMENT

The present technology relates to patient monitoring systems and methods using plural PPG sensors in contact with a patient at different locations, wherein a comparison of the PPG data is performed to calculate a differential pulse transit time (DPTT) between the first and second locations, followed by a determination of continuous non-invasive blood pressure (CNIBP) using the PPG data from the plural and the calculated DPTT.

FIELD

The present technology is generally related to a system and method for continuous non-invasive blood pressure (CNIBP) measurement, for example using deep learning artificial intelligence (AI) utilizing differential pulse transit time (DPTT).

BACKGROUND

In the field of medicine, doctors often desire to monitor certain physiological characteristics of their patients. Accordingly, a wide variety of devices have been developed for monitoring many such physiological characteristics. Such devices provide doctors and other healthcare personnel with the information they need to provide the best possible healthcare for their patients. As a result, such monitoring devices have become an indispensable part of modern medicine.

One technique for monitoring certain physiological characteristics of a patient uses attenuation of light to determine physiological characteristics of a patient. This is used in pulse oximetry, and the devices built are based upon pulse oximetry techniques. Light attenuation is also used for regional or cerebral oximetry. Oximetry may be used to measure various blood characteristics, such as the oxygen saturation of hemoglobin in blood or tissue, the volume of individual blood pulsations supplying the tissue, and/or the rate of blood pulsations corresponding to each heartbeat of a patient. The signals can lead to further physiological measurements, such as respiration rate, glucose levels or blood pressure.

Many conventional medical monitors require attachment of a sensor to a patient in order to detect physiologic signals from the patient and to transmit detected signals through a cable to the monitor. These monitors process the received signals and determine vital signs such as the patient's pulse rate, respiration rate, and arterial oxygen saturation. For example, a pulse oximeter is a finger sensor that can include two light emitters and a photodetector. The sensor emits light into the patient's finger and transmits the detected light signal to a monitor. The monitor includes a processor that processes the signal, determines vital signs (e.g., pulse rate, respiration rate, arterial oxygen saturation), and displays the vital signs on a display.

Other monitoring systems include other types of monitors and sensors, such as electroencephalogram (EEG) sensors, blood pressure cuffs, temperature probes, air flow measurement devices (e.g., spirometer), and others. Some wireless, wearable sensors have been developed, such as wireless EEG patches and wireless pulse oximetry sensors.

Determination of blood pressure non-invasively and continuously presents a significant technical challenge in the medical device industry. For that reason, blood pressure is typically measured intermittently via a separate blood pressure cuff or continuously using invasive techniques, for example using of an invasive arterial line, with the various monitoring devices being connected to one or more patient monitors to present patient measurements.

What is needed in the art are systems and methods allowing for continuous, non-invasive blood pressure measurement.

SUMMARY

The techniques of this disclosure generally relate to systems and methods for continuous non-invasive blood pressure (CNIBP) measurement. In exemplary aspects described herein, CNIBP is measured using deep learning artificial intelligence (AI) utilizing differential pulse transit time (DPTT).

In one aspect, a patient monitoring system includes a first PPG sensor in contact with a patient at a first location, the first sensor providing first data over a first time period related to the patient to determine one or more patient parameters; a second PPG sensor in contact with a patient at a second patient location different from the first location, the second sensor providing second data over said first time period related to the patient to determine one or more patient parameters; and a processor configured to: compare at least a portion of the first data and at least a portion of the second data in order to calculate a differential pulse transit time (DPTT) between the first and second locations; and determine continuous non-invasive blood pressure (CNIBP) using the first data, the second data and the DPTT.

In another aspect, a method for patient monitoring includes configuring a first PPG sensor to contact a patient at a first location, the first sensor configured to provide first data over a first time period related to the patient to determine one or more patient parameters; configuring a second PPG sensor to contact a patient at a second patient location different from the first location, the second sensor providing second data over said first time period related to the patient to determine one or more patient parameters; and with a processor: comparing at least a portion of the first data and at least a portion of the second data in order to calculate a differential pulse transit time (DPTT) between the first and second locations; and determining continuous non-invasive blood pressure (CNIBP) using the first data, the second data and the DPTT.

In an exemplary aspect, the processor compares at least one fiducial point in the first data and in the second data in order to calculate DPTT. Exemplary fiducial points include: a peak of the pulse, the trough of the pulse; or the location of maximum upslope gradient.

In another exemplary aspect, at least a portion of the first sensor data, at least a portion of the second sensor data, and the calculated DPTT are input into a deep learning AI model to determine CNIBP. Exemplary deep learning AI models include an LSTM model, a CNN model, and a hybrid CNN-LSTM model.

DETAILED DESCRIPTION

The following disclosure describes systems and methods for continuous non-invasive blood pressure (CNIBP) measurement. In exemplary aspects described herein, CNIBP is measured using deep learning artificial intelligence (AI) utilizing differential pulse transit time (DPTT).

In exemplary aspects, devices, systems, and/or methods configured in accordance with embodiments of the present technology can include one or more sensors or probes associated with (e.g., contacting) a patient that can be configured to capture data (e.g., temperature, blood pressure, heart rate, arterial oxygen saturation, etc.) related to a patient. The devices, systems, and/or methods can transmit the captured data to a monitoring device, hub, mobile patient management system (MPM), or the like. In some embodiments, the devices, systems, and/or methods can analyze the captured data to determine and/or monitor one or more patient parameters. In these and still other embodiments, the devices, systems, and/or methods can trigger alerts and/or alarms when the devices, systems, and/or methods detect one or more patient parameter abnormalities.

In some embodiments, one or more sensors or probes associated with (e.g., contacting) a patient can be configured to capture data related to a patient, e.g. as a PPG signal. A signal representing light intensity versus time or a mathematical manipulation of this signal (e.g., a scaled version thereof, a log taken thereof, a scaled version of a log taken thereof, etc.) may be referred to as the photoplethysmograph (PPG) signal. In addition, the term “PPG signal,” as used herein, may also refer to an absorption signal (i.e., representing the amount of light absorbed by the tissue) or any suitable mathematical manipulation thereof. The amount of light detected or absorbed may then be used to calculate any of a number of physiological parameters, including oxygen saturation (the saturation of oxygen in pulsatile blood, SpO2), an amount of a blood constituent (e.g., oxyhemoglobin), as well as a physiological rate (e.g., pulse rate or respiration rate) and when each individual pulse or breath occurs. For SpO2, red and infrared (IR) wavelengths may be used because it has been observed that highly oxygenated blood will absorb relatively less Red light and more IR light than blood with a lower oxygen saturation. By comparing the intensities of two wavelengths at different points in the pulse cycle, it is possible to estimate the blood oxygen saturation of hemoglobin in arterial blood, such as from empirical data that may be indexed by values of a ratio, a lookup table, and/or from curve fitting and/or other interpolative techniques.

In exemplary aspects described herein, CNIBP is measured using deep learning artificial intelligence (AI) utilizing differential pulse transit time (DPTT) utilizing plural PPG signals obtained via separate locations of a patient. The DPTT may be defined as the difference in time that a pulse wave takes to arrive at two distinct arterial locations. The DPTT derived from the PPG signals acquired from pulse oximeter probes placed at such distinct locations may be used as an input in an AI model to determine CNIBP.

Specific details of several embodiments of the present technology are described herein with reference toFIGS. 1-12. Although many of the embodiments are described with respect to devices, systems, and methods for CNIBP monitoring of a human patient, other applications and other embodiments in addition to those described herein are within the scope of the present technology. For example, at least some embodiments of the present technology can be useful for detection and/or monitoring of one or more parameters of other animals and/or in non-patients (e.g., elderly or neonatal individuals within their homes, individuals in a search and rescue or stranded context, etc.). It should be noted that other embodiments in addition to those disclosed herein are within the scope of the present technology. Further, embodiments of the present technology can have different configurations, components, and/or procedures than those shown or described herein. Moreover, a person of ordinary skill in the art will understand that embodiments of the present technology can have configurations, components, and/or procedures in addition to those shown or described herein and that these and other embodiments can be without several of the configurations, components, and/or procedures shown or described herein without deviating from the present technology.

FIG. 1is a schematic view of an exemplary monitoring system shown generally at10, including a patient monitor12and sensors13and14, such as a pulse oximetry sensor, to monitor physiological parameters of a patient. By way of example, the sensors may be a NELLCOR™, or INVOS™ sensor available from Medtronic (Boulder, Colo.), or another type of oximetry sensor. Sensor14is provided on a patient's fingertip; and sensor13is provided on the patient's forehead. Although some depicted embodiments relate to sensors for use on a patient's fingertip and forehead, it should be understood that, in certain embodiments, the features of the sensors as provided herein may be incorporated into sensors for use on other tissue locations (e.g., toe, wrist, earlobe, etc.). Further, exemplary systems may include more than two sensors provided at discrete arterial locations of a patient. Referring still toFIG. 1, an optional blood pressure cuff is shown at11. The blood pressure cuff includes a communications link20, which may be wired or wireless.

FIG. 2is a schematic view showing the exemplary patient monitor12and sensor14in more detail. In the embodiment ofFIG. 2, the sensor14is a pulse oximetry sensor that includes one or more emitters16and one or more detectors18. For pulse oximetry applications, the emitter16transmits at least two wavelengths of light (e.g., red and infrared (IR)) into a tissue of the patient. For other applications, the emitter16may transmit 3, 4, or 5 or more wavelengths of light into the tissue of a patient. The detector(s)18include a photodetector selected to receive light in the range of wavelengths emitted from the emitter(s)16, after the light has passed through the tissue. Additionally, the emitter(s)16and the detector(s)18may operate in various modes (e.g., reflectance or transmission).

The sensor14also includes a sensor body46to house or carry the components of the sensor14. The body46includes a backing, or liner, provided around the emitter16and the detector18, as well as an adhesive layer (not shown) on the patient side. The sensor14may be reusable (such as a durable plastic clip sensor), disposable (such as an adhesive sensor including a bandage/liner), or partially reusable and partially disposable.

In the embodiments shown, the sensors14is communicatively coupled to the patient monitor12. In certain embodiments, the sensors may include a wireless module configured to establish a wireless communication15with the patient monitor12using any suitable wireless standard. For example, the sensors may include a transceiver that enables wireless signals to be transmitted to and received from an external device (e.g., the patient monitor12, a charging device, etc.). The transceiver may establish wireless communication15with a transceiver of the patient monitor12using any suitable protocol. For example, the transceiver may be configured to transmit signals using one or more of the ZigBee standard, 802.15.4x standards WirelessHART standard, Bluetooth standard, IEEE 802.11x standards, or MiWi standard. Additionally, the transceiver may transmit a raw digitized detector signal, a processed digitized detector signal, and/or a calculated physiological parameter, as well as any data that may be stored in the sensor, such as data relating to wavelengths of the emitters16, or data relating to input specification for the emitters16. Additionally, or alternatively, the emitters16and detectors18of the sensor14may be coupled to the patient monitor12via a cable24through a plug26(e.g., a connector having one or more conductors) coupled to a sensor port29of the monitor. In certain embodiments, the sensor14is configured to operate in both a wireless mode and a wired mode. Accordingly, in certain embodiments, the cable24is removably attached to the sensor14such that the sensor14can be detached from the cable to increase the patient's range of motion while wearing the sensor14. It should be recognized that wired or wireless configurations, as with sensor14, are also contemplated with regard to sensor13, as well as optional blood pressure cuff11, which are shown inFIG. 1.

The patient monitor12is configured to calculate physiological parameters of the patient relating to the physiological signal received from the sensors13,14. For example, the patient monitor12may include a processor configured to calculate the patient's arterial blood oxygen saturation, tissue oxygen saturation, pulse rate, respiration rate, blood pressure, blood pressure characteristic measure, autoregulation status, brain activity, and/or any other suitable physiological characteristics. Additionally, the patient monitor12may include a monitor display30configured to display information regarding the physiological parameters, information about the system (e.g., instructions for disinfecting and/or charging the sensor14), and/or alarm indications. The patient monitor12may include various input components32, such as knobs, switches, keys and keypads, buttons, etc., to provide for operation and configuration of the patient monitor12. The patient monitor12may also display information related to alarms, monitor settings, and/or signal quality via one or more indicator lights and/or one or more speakers or audible indicators. The patient monitor12may also include an upgrade slot28, in which additional modules can be inserted so that the patient monitor12can measure and display additional physiological parameters.

Because the sensors13,14may be configured to operate in a wireless mode and, in certain embodiments, may not receive power from the patient monitor12while operating in the wireless mode, the sensors13,14may include a battery to provide power to the components of the sensor (e.g., the emitter(s)16and the detector(s)18). In certain embodiments, the battery may be a rechargeable battery such as, for example, a lithium ion, lithium polymer, nickel-metal hydride, or nickel-cadmium battery. However, any suitable power source may be utilized, such as, one or more capacitors and/or an energy harvesting power supply (e.g., a motion generated energy harvesting device, thermoelectric generated energy harvesting device, or similar devices).

As noted above, in an embodiment, the patient monitor12is a pulse oximetry monitor and the sensor14is a pulse oximetry sensor. The sensor14may be placed at a site on a patient with pulsatile arterial flow, typically a fingertip, toe, forehead or earlobe, or in the case of a neonate, across a foot. Additional suitable sensor locations include, without limitation, the neck to monitor carotid artery pulsatile flow, the wrist to monitor radial artery pulsatile flow, the inside of a patient's thigh to monitor femoral artery pulsatile flow, the ankle to monitor tibial artery pulsatile flow, and around or in front of the ear. As shown inFIG. 1, the patient monitoring system10may include sensors13,14at multiple locations. The emitter16emits light which passes through the blood perfused tissue, and the detector18photoelectrically senses the amount of light reflected or transmitted by the tissue. The patient monitoring system10measures the intensity of light that is received at the detector18as a function of time.

As we have noted, exemplary systems and methods described herein determine continuous non-invasive blood pressure (CNIBP) using an AI model, where the inputs include, among other possible inputs, differential pulse transit time (DPTT). Referring toFIG. 1, PPG signals from pulse oximeter sensors13,14, which are placed at separate patient locations may be used to derive DPTT, which may be defined as the difference in time that a pulse wave takes to arrive at two distinct arterial locations.

FIG. 3shows a blood pressure (BP) signal102and a PPG signal104taken at around the same time. As can be seen, signals102and104look relatively similar in morphology.

However,FIG. 4shows two different signals,202and204, also taken at similar times, but with different looking morphology. InFIG. 4, the blood pressure (BP) signal202is taken using an (invasive) arterial line, whereas the PPG signal is generated through the volume change at the finger, generated as the arterial pulse (shock wave) hits the peripheries and is measured by a pulse oximeter. Mapping this pressure-mechanical-light coupling, from BP to PPG (or vice-versa) is difficult to achieve due to numerous confounders, including influence of contact force, ambient temperature, drugs, vasomotion, movement, arterial stiffening, posture changes, etc. The present disclosure recognizes these challenges and presents a solution by examining PPG signals at two separate points on the body, e.g., at the finger and forehead as inFIG. 1, to determine differential pulse transit time (DPTT). In doing so, systems and methods described herein advantageously avoid the problems associated traditional pulse transit time (PTT) taken between an electrocardiogram (ECG) signal and the pulse wave, which includes electromechanical delay (EMD).

In exemplary embodiments, and with further reference toFIG. 1, DPTT is calculated from two PPG signals taken at different body locations by measuring the time difference between two corresponding fiducial points on each signal.FIG. 5illustrates a first PPG signal from a first pulse oximetry sensor/probe (e.g., sensor13inFIG. 1) at302and a second PPG signal from a second pulse oximetry sensor (e.g., sensor14inFIG. 1) at304. The PPG signals used may be from the same or different wavelengths of light, e.g., red signals from sensors13,14inFIG. 1, infrared signals from sensors13,14, a red signal from sensor13and an infrared signal from sensor14, or an infrared signal from sensor13and a red signal from sensor14

FIG. 5illustrates generally at300PPG data from two pulse oximetry sensors at different locations on the body. With regard toFIG. 5, one or more corresponding fiducial points on each signal may be identified, for example the peak of the pulses in each PPG signal, in order to measure the time difference between those fiducial points for the DPTT calculation. Peak306is identified on signal302. Peak308is identified on signal304. DPTT is shown at310as the difference in time between fiducial points306and308. It should be noted that while peaks306and308are shown inFIG. 5, other fiducial points are contemplated herein, for example, the trough, the location of maximum upslope gradient, or other corresponding points. Further, any two locations on the body may be used to produce a DPTT, e.g., finger and toe, ear and toe, finger and forehead, etc.

In further exemplary embodiments, systems and methods described herein make use of both wavelengths from two or more PPG signals in a deep learning model. For example,FIG. 6illustrates deep learning model inputs generally at400, including red404and infrared406PPG signals from a first pulse oximetry sensor402, red410and infrared412PPG signals from a second pulse oximetry sensor408and a time vector414for the PPGs. In such a way, the system and method can capture DPTT information, as well as morphological information at each location that is contained within each PPG and correlate to BP changes.

WhileFIG. 6illustrates the use of two sets of PPG signals, from two locations, as inputs to a deep learning method, we note that any number of sets of signals from different sites may be utilized. For example, use of pulse oximetry sensors at the finger, toe and forehead would allow for the generation of three sets of DPTTs corresponding to finger-forehead, forehead-toe and toe-finger.

In other exemplary embodiments, inputs may be provided corresponding to features derived from the raw PPG signals. For example, these may include characteristic features from each PPG waveform including: pulse duration, relative position of maximum upslope of the systolic rise, peak location and amplitude, perfusion index, baseline trend, respiratory cycle information, area of upstroke, downstroke, max gradient of upslope, baseline value, etc. Additionally, for each feature a sequence of values over time may be used. These may be once per period of time (i.e. once per second, or once per pulse, or a single value from a time window of longer period (e.g. 15 seconds, 30 seconds, etc.) In addition to these features, the DPTT calculated between each signal may be provided as an input. Thus, a matrix of feature values from each signal and the corresponding DPTT may be constructed, as shown generally at500inFIG. 7.

FIG. 7shows a first matrix of features from a first pulse oximetry probe at502; a second matrix of features from a second pulse oximetry probe at504; and DPTT between the location of the first probe and the second probe. The matrix of features for each probe may be selected, e.g., from features described above. For example, the matrix502from the first probe may include PPG upstroke area508, PPG downstroke area510, PPG amplitude512, PPG maximum slope516, etc. Matrix504includes the corresponding features from the second probe, with PPG upstroke area518, PPG downstroke area520, PPG amplitude522, PPG maximum slope524, etc.

Table 1, below outlines further exemplary features, for example relative to a finger PPG and the DPTT between the finger and a forehead:

TABLE 1Feature NameFeature Definitionamplitude_respirationamplitude of respiration component in the PPGIR_pulse_durationIR PPG pulse durationIR_sys_relative_positionPosition of the systolic peak in the pulse (percentage). If systolicpeak happens half-way between two diastoles, value is 50IR_maxslope_relative_positionPosition of the max slop (percentage in the pulse), same units asIR_sys_relative_positionIR_pulse_amplitudeIR PPG pulse amplitudeIR_pulse_amplitude_over_DCIR PPG pulse amplitude normalised by signal baseline (DC)IR_trendhow much the pulse rises/drops (difference between two diastolicpoints) - (in units of PPG/DC)IR_trend_pcthow much the pulse rises/drops (as a percentage of the pulseamplitude)IR_meanmean of the PPG over the pulse (integral divided by duration)IR_mean_over_DCIR_mean divided by the DC valueIR_max_gradient_upstrokemaximum gradient of the initial upslope of the PPG pulseIR_perfusion_indexperfusion index of the IR PPG over the pulseIR_DCmean baseline value of the IR PPG over the pulseIR_area_upstrokeArea of the pulse from the initial diastolic until the systolicIR_area_upstroke_over_DCIR_area_upstroke divided by DCIR_area_downstrokeArea of the pulse from the systolic peak until the final diastolicIR_area_downstroke_over_DCIR_area_downstroke divided by DCIR_width_50Pulse width, measured at height = 50% of pulse amplitudeIR_width_75Pulse width, measured at height = 75% of pulse amplitudeIR_width_90Pulse width, measured at height = 90% of pulse amplitudeIR_uphill_average_slopeSlope of the line defined by the points initial_diastolic and systolicIR_downhill_average_slopeSlope of the line defined by the points systolic and final_diastolicIR_skewnessskew of the pulse componentIR_kurtosiskurtosis of the pulse componentDPTTdifferential pulse transit time between finger and ear

In exemplary embodiments described herein, the AI model is trained to calculate a blood pressure signal from the provided inputs. This may be any characteristic blood pressure including, for example, the systolic pressure (SP), diastolic pressure (DP), mean arterial pressure (MAP) or pulse pressure (PP).

In further exemplary embodiments, feature matrices are input into the training cycle of a deep learning model with a target BP value associated with it. The model is then trained to associate the PPG morphological feature sequences with the blood pressure values. The model may then be tested using a test set of feature sequences previously unseen by the model to estimate the associated blood pressure. In this way a model may be generated with a given performance in terms of associating the PPG-based input to a BP.

In exemplary embodiments described herein, the deep learning model is a long short-term memory (LSTM) machine learning model, with an exemplary architecture illustrated generally at600inFIG. 8. The exemplary architecture includes: an input layer (Sequence input block602); a first dropout layer (DropoutLayer block604); a first bidirectional LSTM layer (BiLSTMLayer block606); a second dropout layer (DropoutLayer block608); a second bidirectional LSTM layer (BiLSTMLayer block610); a fully connected layer (FullyConnected block612); and a regression output layer (RegressionOut block614).

FIG. 9provides a flowchart at700, illustrating the features provided as a combined matrix702input into an LSTM machine learning model at704, with a blood pressure output at706.

In further exemplary embodiments, the deep learning model is a convolutional neural network (CNN) model, an exemplary architecture of which is shown generally at800inFIG. 10. The exemplary architecture includes the following steps in the illustrated arrangement: input802; convolution804; batch normalization806; ReLU (rectified linear unit)808; max pooling810; convolution812; batch normalization814; ReLU816; dropout818; convolution820; addition822; max pooling824; batch normalization826; ReLU828; dropout830; convolution832; batch normalization834; ReLU836; dropout838; convolution840; addition842; batch normalization844; ReLU846; fully connected layer848; and regression output850. We note that in exemplary embodiments the block indicated at852may be repeated during the process.

FIG. 11provides a flowchart at900, illustrating the features provided as a combined matrix902input into a CNN machine learning model at904, with a blood pressure output at906.

In additional exemplary embodiments, the deep learning model is a hybrid CNN-LSTM model, as is shown generally at1000in the flowchart ofFIG. 12. In the flowchart ofFIG. 12, a combined matrix1002is input into a CNN machine learning model at1004, followed by LSTM machine learning model at1006, with blood pressure output at1008. As an example,FIG. 13shows generally at1100an output1102from an LSTM model alongside a MAP reference signal1104, where pulse features from a single probe location (e.g., finger) were fed in addition to the corresponding DPTT between two locations (e.g., ear-finger).

In additional exemplary embodiments, the CNIBP system and method may be at least periodically calibrated, e.g., to account for possible loss of accuracy over time, e.g., due to confounders affecting the model being used. In this case, the CNIBP system may be intermittently calibrated using a blood pressure cuff, such as cuff11inFIG. 1.

The above detailed descriptions of embodiments of the technology are not intended to be exhaustive or to limit the technology to the precise form disclosed above. Although specific embodiments of, and examples for, the technology are described above for illustrative purposes, various equivalent modifications are possible within the scope of the technology, as those skilled in the relevant art will recognize. For example, while steps are presented in a given order, alternative embodiments can perform steps in a different order. Furthermore, the various embodiments described herein can also be combined to provide further embodiments.

The systems and methods described herein can be provided in the form of tangible and non-transitory machine-readable medium or media (such as a hard disk drive, hardware memory, etc.) having instructions recorded thereon for execution by a processor or computer. The set of instructions can include various commands that instruct the computer or processor to perform specific operations such as the methods and processes of the various embodiments described here. The set of instructions can be in the form of a software program or application. The computer storage media can include volatile and non-volatile media, and removable and non-removable media, for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media can include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technology, CD-ROM, DVD, or other optical storage, magnetic disk storage, or any other hardware medium which can be used to store desired information and that can be accessed by components of the system. Components of the system can communicate with each other via wired or wireless communication. The components can be separate from each other, or various combinations of components can be integrated together into a monitor or processor or contained within a workstation with standard computer hardware (for example, processors, circuitry, logic circuits, memory, and the like). The system can include processing devices such as microprocessors, microcontrollers, integrated circuits, control units, storage media, and other hardware.

From the foregoing, it will be appreciated that specific embodiments of the technology have been described herein for purposes of illustration, but well-known structures and functions have not been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments of the technology. To the extent any materials incorporated herein by reference conflict with the present disclosure, the present disclosure controls. Where the context permits, singular or plural terms can also include the plural or singular term, respectively. Moreover, unless the word “or” is expressly limited to mean only a single item exclusive from the other items in reference to a list of two or more items, then the use of “or” in such a list is to be interpreted as including (a) any single item in the list, (b) all of the items in the list, or (c) any combination of the items in the list. Where the context permits, singular or plural terms can also include the plural or singular term, respectively. Additionally, the terms “comprising,” “including,” “having” and “with” are used throughout to mean including at least the recited feature(s) such that any greater number of the same feature and/or additional types of other features are not precluded.

From the foregoing, it will also be appreciated that various modifications can be made without deviating from the technology. For example, various components of the technology can be further divided into subcomponents, or various components and functions of the technology can be combined and/or integrated. Furthermore, although advantages associated with certain embodiments of the technology have been described in the context of those embodiments, other embodiments can also exhibit such advantages, and not all embodiments need necessarily exhibit such advantages to fall within the scope of the technology. Accordingly, the disclosure and associated technology can encompass other embodiments not expressly shown or described herein.