IV DRESSING WITH INTEGRATED SENSORS TO MEASURE FLUID INFILTRATION AND PHYSIOLOGICAL PARAMETERS.

Abstract

The invention provides an intravenous (IV) dressing system that helps secure an IV catheter to a patient while simultaneously using peripheral venous pressure (PVP), impedance, temperature, optical, and motion sensors to characterize the properties of the IV system (e.g., infiltration, extravasation, occlusion) and the patient's physiological parameters (e.g., heart rate, SpO2, respiratory rate, temperature, and blood pressure). Notably, the system converts PVP waveforms into arterial blood pressure values ​​(e.g., systolic and diastolic blood pressure).

Description

This application claims priority from, and the benefit of, U.S. Provisional Patent Application No. 63 / 064,690, filed on August 12, 2020, entitled IV DRESSING WITH INTEGRATED SENSORS FOR MEASURING FLUID INFILTRATION AND PHYSIOLOGICAL PARAMETERS, the contents of which are incorporated herein by reference in their entirety and are based upon. Background of the invention 1. Field of the invention The invention described herein relates to systems for the administration of drugs and fluids, and systems for monitoring patients in, for example, hospitals and medical clinics. 2. General Background Unless a term is expressly defined herein using the phrase "herein" or a similar phrase, there is no intention to limit the meaning of that term beyond its simple or ordinary meaning. To the extent any term is referred to herein in a manner consistent with a single meaning, that is done solely for the sake of clarity; it is not intended that such claim term be limited to that single meaning. Finally, unless an element of a claim is defined by reciting the word "means" and a function without mention of any structure, it is not intended that the scope of any element of a claim be interpreted upon application of 35 U.S.C. § 112(f). Appropriate care for hospitalized patients typically requires: 1) the delivery of medications and fluids via intravenous (IV) catheters and infusion pumps; and 2) the monitoring of vital signs and hemodynamic parameters using patient monitors. Intravenous catheters are usually inserted into veins in the patient's hands or arms, and patient monitors are connected to sensors placed (or inserted) on the patient's body. IV catheters are generally held in place with a large adhesive dressing or bandage, the most common of which is marketed under the brand name Tegaderm and is sold by 3M Corporation, based in Saint Paul, MN. In addition to its adhesive backing, Tegaderm may include an antimicrobial coating to reduce the occurrence of IV site infections.Tegaderm and related IV dressings typically lack sensors for measuring physiological parameters, such as those described above. Intravenous (IV) systems typically use an infusion pump or IV bag to control fluid delivery. The infusion pump or IV bag is connected via tubing or IV equipment to the catheter, which is inserted into the patient's vein. In some cases, the catheter may slip out of the vein and mistakenly deliver fluids to the surrounding tissue; this is referred to herein as an "IV infiltration." Common signs of IV infiltration include swelling, skin tightness, and pain around the catheter insertion site. When left unchecked and untreated, IV infiltration can lead to severe pain, infection, compartment syndrome, and even amputation of the affected limb. When the leaked solution from an infiltration is a vesicant drug, causing tissue injury, blistering, or severe tissue damage, it is called extravasation.Injuries from this type of IV failure can be severe and may result in loss of function in a limb and, if the damage is severe enough, tissue death (also known as necrosis). In still other cases, the catheter tip may become blocked by a blood clot or medication, preventing fluid flow into the patient's vein; this is referred to herein as an “IV occlusion.” Intravenous infiltration is a common complication and a source of failure with the IV system; possibly as many as 23% of peripheral IV lines fail due to infiltration (Helm RE, Klausner JD, Klemperer JD, Flint LM, Huang E., Accepted but unacceptable: peripheral IV catheter failure. , J Infus. Nurs. 2015;38(3): 189-203). There are many sources of IV infiltration, including clinician error during IV line placement, limb movement causing the catheter tip to detach or penetrate the vein, fragile veins rupturing due to high flow rates, and the effects of acidic or high-osmolarity medications on the vein wall. Extravasation, in turn, occurs in 0.1-6% of patients receiving chemotherapy (Al-Benna S, O'Boyle C, Holley J., “Extravasation injuries in adults, ISRN Dermatol. 2013;2013:856541). Due to a myriad of causes, the incidence of IV infiltration varies depending on the patient population and care setting. IV infiltration has the highest incidence in pediatric and neonatal populations, particularly in intensive care units serving this demographic. In these settings, peripheral IVs are common, but the smaller vasculature of these patients and the corresponding catheter gauges make them more difficult to place, leading to a relatively high incidence of IV infiltration. Other patient populations, such as the elderly or the morbidly obese, are also at increased risk for IV infiltration due to factors such as fragile veins and difficult catheter placement. In most hospital settings, patient monitors are used in conjunction with IV systems to measure a patient's vital signs and hemodynamic parameters. Conventional patient monitors typically measure electrocardiogram (ECG) and impedance-pneumography (IP) waveforms using electrodes worn on the torso, from which they calculate heart rate (HR), heart rate variability (HRV), and respiratory rate (RR). Most conventional monitors also measure optical signals, called photoplethysmogram (PPG) waveforms, with sensors that are usually attached to the patient's fingers or earlobes.These sensors can calculate blood oxygen levels (SpO2) and pulse rate (PR) from these PPG waveforms. More advanced monitors can also measure blood pressure (BP), specifically systolic (SBP), diastolic (DIA), and mean arterial pressure (MAP). Digital stethoscopes, which can be handheld or wearable, can measure phonocardiogram (PCG) waveforms that indicate heart sounds and murmurs. Blood pressure (BP) is a critically important vital sign that can be particularly difficult to measure. The gold standard for BP measurement is the arterial line, which is an invasive catheter with a transducer that directly measures blood pressure. The catheter is inserted into an artery (usually the radial, brachial, or femoral artery), and the transducer detects the mechanical pressure and converts it into kinetic energy that can be displayed on the patient monitor. The displayed measurements may include systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) values, along with a time-dependent pressure waveform. While the arterial line is widely used as a direct, beat-to-beat measurement, it is highly invasive. Therefore, it carries a risk of complications such as infection and can be painful for the patient. Unlike arterial lines, an indirect and non-invasive method for detecting blood pressure is a sphygmomanometer, which is an unreliable cuff that collapses and releases an underlying artery in a controlled manner. Sphygmomanometers rely on a manual palpation method that involves inflating a cuff on the patient's upper arm (e.g., biceps) while a clinician palpates the radial artery. The clinician inflates the cuff to a pressure that causes the pulse to disappear; as the cuff deflates, the pressure at which the pulse reappears due to the artery's release is the systolic blood pressure (SBP). Another manual method using a sphygmomanometer is auscultation, which involves listening to the artery through a stethoscope while inflating and then deflating a cuff wrapped around the patient's biceps. Similar to the palpation method, during auscultation the clinician inflates the cuff above the patient's blood pressure. The clinician then slowly deflates the cuff, resulting in a Korotkoff sound that signals systolic arterial occlusion (SAO). Korotkoff sounds are generated when a bolus of blood spurts through the occluded artery as the pressure in the artery rises above the pressure in the cuff. The spurting blood creates turbulence, producing an audible sound. Once the cuff is deflated sufficiently, the Korotkoff sounds disappear, indicating diastolic arterial occlusion (DAO) as laminar blood flow is restored through the artery. Automated methods using cuff-based systems similar to a sphygmomanometer are also widely used to measure blood pressure. One of the most common methods is oscillometry. Here, the cuff has a pressure transducer that detects time-dependent changes in cuff pressure. During a measurement, with each arterial pulse, blood flow causes the volume of the patient's arm to change slightly, creating a small pressure pulse in the cuff that the pressure transducer detects. As the cuff inflates, the device can detect when blood flow stops by the absence of pulses. The device then slowly deflates the cuff, at which point the appearance of small pressure pulses indicates systemic systolic blood flow (SIS), and the subsequent disappearance of these pulses indicates diastolic blood flow (DIA) and the return of laminar blood flow. While auscultation and oscillometry methods are non-invasive, patient tolerance varies due to the uncomfortable nature of the cuff. Furthermore, these methods are intermittent and have limited value in situations where continuous blood pressure monitoring would be clinically useful, such as vasopressor titration. Recent advances have also led to non-invasive, continuous blood pressure (BP) measurements. Such methods involve the use of volume fixation techniques, arterial applanation tonometry, optical sensors, and multisensor techniques that measure systolic time intervals and then use algorithms to convert them into BP values. The volume clamp technique, such as that used by Clearsight (from Edwards Scientific, based in Irvine, CA), features a finger cuff and an optical sensor that includes a light source and a photodiode. The finger cuff is inflated to maintain a constant arterial diameter in a finger, which is then measured by the optical sensor. The finger cuff adjusts the pressure to maintain the arterial diameter. These adjustments can be used to calculate a pressure curve that corresponds to SIS and DIA. Arterial applanation tonometry involves placing a pressure sensor over an artery (typically the radial artery) that is positioned over bone. During a measurement, the pressure applied by the device causes the sensor to press against the artery. The pressure sensor measures the pressure required to flatten the artery wall, leading to SIS and DIA measurements. In another non-invasive, continuous technique, sensors that simultaneously measure PPG and ECG waveforms can provide an estimate of blood pressure by measuring systolic time intervals—that is, the time it takes for a signal to propagate between two points on the patient. One specific technique, called pulse transit time (PTT), is the time between a heartbeat-induced pulse on a PPG or PCG waveform (usually measured on the chest or arm) and a pulse measured at a different location on the body (usually a PPG waveform measured on the finger). Pulse arrival time (PAT) uses a similar concept, except that it measures the time between an ECG R wave (usually measured from the chest) and a pulse on a PPG waveform (usually measured on the finger).PAT differs from PTT in that it includes the pre-ejection period (hereafter, “PEP”) and the isovolumetric contraction time (hereafter, “ICT”). Both PTT and PAT are inversely related to BP, and most measurements based on these techniques are calibrated with a cuff-based system and, typically, an automated oscillometry-based system to produce absolute SIS and DIA measurements. The “VÍSÍ” system (from Sofera Wireless, based in San Diego, CA) is a commercially available PAT-based BP measurement device. Some patient monitors are worn entirely on the body. These typically take the form of patches that measure ECG, HR, HRV, and, in some cases, RR. Such patches may also include accelerometers that measure motion waveforms (hereafter referred to as ACC). Algorithms can determine the patient's posture, degree of movement, falls, and other related parameters from the ACC waveforms. Patients usually wear these types of patches in the hospital; alternatively, they are used for outpatient and home use. The patches are typically worn for relatively short periods of time (e.g., from a few days to several weeks).They are usually wireless and generally include technologies such as Bluetooth® transceivers to transmit information over a short range to a secondary gateway device, which usually includes a cellular radio or Wi-Fi to transmit the information to a cloud-based system. o / uui oí i Even more complex patient monitors measure parameters such as stroke volume (SV), cardiac output (CO), and cardiac wedge pressure using an invasive sensor called a Swan-Ganz catheter or pulmonary artery catheter. To perform a measurement, these sensors are placed on the left side of the patient's heart, where they are inserted into a small pulmonary blood vessel using a balloon catheter. As an alternative to this highly invasive measurement, patient monitors can use non-invasive techniques such as bioimpedance and bioreactance to measure similar parameters. These methods deploy electrodes worn on the body (and are typically deployed on the patient's chest, legs, and / or neck) to measure bioimpedance (IMP) and / or bioreactance (BR) plethysmogram waveforms.Analysis of IMP and BR waveforms yields SV, CO, and thoracic impedance, which is an indicator of fluid volume in the patient's thorax (hereafter referred to as FLUIDS). In particular, IMP and BR waveforms generally have similar shapes and are detected using similar measurement techniques; therefore, they are used interchangeably herein. Devices that measure blood pressure (BP) and, less frequently, stroke volume (SV), carbon dioxide (CO2), and fluids can generate metrics that allow clinicians to estimate a patient's blood volume, fluid responsiveness, and, in some cases, related metrics such as central venous pressure (CVP). Taken together, these parameters can diagnose certain medical conditions and guide resuscitation efforts. However, the highly invasive nature of Swan-Ganz and pulmonary artery catheters can be a drawback and carries a high risk of infection. Furthermore, CVP measurements may take longer to change in response to certain acute conditions, such as when the circulatory system attempts to compensate for blood volume imbalance (particularly hypovolemia) by protecting blood volume levels in the central circulatory system at the expense of the periphery.For example, constriction of peripheral blood vessels can reduce the effect of fluid loss on the central nervous system, thus temporarily masking blood loss in conventional CVP measurements. Such masking can lead to a delay in recognizing and treating the patient's condition, thereby worsening outcomes. To address these and other shortcomings, a measurement technique called peripheral intravenous waveform analysis (hereafter, PIVA) has been developed, as described in U.S. Patent Application No. 14 / 853,504 (filed September 14, 2015, and published as U.S. Patent Publication No. 2016 / 0073959) and PCT Application No. PCT / US16 / 16420 (filed February 3, 2016, and published as WO 2016 / 126856), the contents of which are incorporated herein by reference. These documents describe sensors with pressure transducers that receive signals from indwelling catheters inserted into a patient's venous system and are connected by cables to remote electronic components that process the signals generated by the catheters (hereafter, the PIVA sensor).PIVA sensors measure time-dependent waveforms that indicate peripheral venous pressure (PVP) using existing IV lines, typically IV tubing connected to a saline drip or infusion pump. PVP waveforms can be filtered to display relatively high-frequency signal components (hereafter, waveforms). PVP-AC waveforms) and low-frequency signal components (hereafter, PVP-DC waveforms). The term 'AC' is normally used to describe alternating current, but is used here to denote a signal component that changes rapidly over time. Similarly, the low-frequency components of PVP waveforms are relatively stable and invariant over time and are therefore denoted by the term 'DC', which is normally used to describe direct current and the corresponding signals that do not change rapidly over time. Measurements made with PIVA sensors typically involve a mathematical transformation of the PVP waveforms (and, typically, the PVP-AC waveforms) into the frequency domain, performed by a remote computer, using a methodology called the Fast Fourier Transform (FFT).Analysis of a frequency domain spectrum generated with an FFT can yield an FR frequency (hereafter F0) and an FC frequency (hereafter F1) that indicate the patient's HR and RR, respectively. Further analysis of F0 and F1, for example, using a computer algorithm to determine the amplitude of these peaks or, alternatively, integrating an area under the curve centered around the peak's maximum amplitude, determines the energy of these features. Subsequent processing of these energies provides an indication of the patient's blood volume status. Such measurements have been described, for example, in the following references, the content of which is incorporated herein as a reference: 1) Hocking et al., “Peripheral venous waveform analysis for detecting hemorrhage and iatrogenic volume overload in a porcine model”, Shock. 2016 Oct;46(4):447-52; 2) Sileshi et al., “Peripheral venous waveform analysis for detecting early hemorrhage: a pilot study.”, Intensive Care Med. 2015 Jun;41 (6):1147-8; 3) Miles et al., “Peripheral intravenous volume analysis (PIVA) for quantitating volume overload in patients hospitalized with acute decompensated heart failure - a pilot study.”, J Card Fail. 2018 Aug;24(8):525-532; y 4) Hocking et al., “Peripheral i.v. analysis (PIVA) of venous waveforms for volume assessment in patients undergoing haemodialysis.”, Br J Anaesth. 2017 Dec 1;119(6):1135-1140. Unfortunately, during typical PIVA sensor measurements, the PVP waveforms induced by heart rate and respiratory rate events (typically 5–20 mmHg) are much weaker than their blood pressure counterparts (typically 60–150 mmHg). This means that the magnitudes of the corresponding signals in time-dependent PVP waveforms measured by conventional pressure transducers are often very weak (e.g., typically 5–50 V). Furthermore, PVP waveforms are generally amplified, conditioned, digitized, and ultimately processed by electronic systems located at a distance from the patient. Therefore, prior to these steps, the analog versions of the waveforms travel through cables that can attenuate them and add noise (due, for example, to movement). And in some cases, the PVP waveforms simply lack corresponding F0 and F1 signatures.Or the peaks of one primary frequency are obscured by harmonics (i.e., integer multiples of a given frequency) of the other primary frequency. This can make it difficult or impossible for an automated medical device to accurately determine F0 and F1, and the energy associated with these characteristics. iviA / a / ¿u¿ó / uu io ι i Brief description of the invention In light of the above, it would be beneficial to provide an IV dressing system (hereafter referred to as IVDS) that performs the functions of a Tegaderm-type dressing—that is, a dressing-type component that secures an IV to a patient—while simultaneously characterizing the properties of the IV system (e.g., infiltration, extravasation, occlusion) and the patient's physiological parameters (e.g., HR, HRV, SpO2, RR, TEMP, and BP). In particular, it would be beneficial if the IVDS could measure the PVP signals, which originate from the patient's venous system, and convert them into arterial BP values ​​(e.g., SBP, MAP, DIA). To perform such measurements, the IVDS would improve upon a conventional PIVA sensor to overcome historical problems related to weak and noisy PVP waveforms, and would also incorporate a suite of sensors that simultaneously measure signals related to the IV system and the patient. Such a system could improve how patients are monitored in hospitals and medical clinics. To address these and other shortcomings, the IVDS incorporates impedance, temperature, and motion sensors, as well as an enhanced and augmented PVP sensor that includes a circuit board located very close to a permanent venous catheter. This circuit board amplifies, filters, and digitizes PVP waveforms immediately after they are detected by a pressure sensor (e.g., directly on the patient's body). Furthermore, according to the invention, the PVP sensor measurements can be coupled with independent measurements of hemodynamic parameters, e.g., SV, CO, and FLUIDS (which can be performed with the patch sensor or a comparable patient monitor) to gain a better understanding of the patient's fluid status. The IVDS described herein is designed to operate with a conventional IV system and features a flexible, adhesive dressing component that connects the indwelling catheter to the patient. The IV system, dressing, and catheter are standard equipment used in hospitals. The dressing typically includes at least four embedded electrodes, usually made of a hydrogel-based material, that perform impedance measurements to detect fluid accumulation. This fluid, during some IV treatments, is mistakenly deposited outside the patient's vein and accumulates in the surrounding tissue. Additionally, the dressing may include a temperature sensor and an optical sensor that detect changes in temperature and optical absorption, respectively, which are related to fluid accumulation.A motion sensor (e.g., an accelerometer and / or gyroscope) within the IVDS characterizes the patient's movement to eliminate false negative and positive readings, while simultaneously characterizing the patient's posture (e.g., standing, sitting, supine) and activity level (e.g., walking, sleeping, falling). The catheter includes a housing, worn near or on the patient's body, typically in their arm or hand, that encloses a PVP conditioning circuit board. This circuit board features a complex circuit that amplifies, filters, and digitizes analog PVP waveforms. The circuit board may also include components for processing and storing the digitized signals and for wirelessly transmitting information (e.g., a Bluetooth® transmitter).In this way, the circuit board can be integrated with a remote processor (e.g., a server, gateway, tablet, smartphone, computer, infusion pump, or some combination thereof) that can display IVDS information, generate alarms and alerts related to patient physiology and the IV system, and collectively analyze supplementary information from other devices worn by the patient, e.g., a patch sensor. The IVDS described herein simplifies the processes of securing an IV to a patient, characterizing IV performance, and measuring traditional vital signs and hemodynamic parameters, which can involve multiple devices and take several minutes to perform. The remote processor, which wirelessly connects to the IVDS, can also be integrated with existing hospital infrastructure and notification systems, such as a hospital's electronic medical record (EMR) system. Such a system can alert caregivers to changes in a patient's condition, enabling them to intervene. The IVDS typically features a low-cost, disposable system with electrodes on its underside that secure it to the patient's body without cumbersome wires. This disposable system is usually connected to a reusable system containing relatively expensive electronic components, such as a printed circuit board (PCB) with a microprocessor, memory, sensing electronics, a wireless transmitter, and a rechargeable lithium-ion battery. In some models, the disposable component is connected to the reusable component via magnets, allowing one component to be easily reattached if removed. The entire IVDS, including both the reusable and disposable components, is typically lightweight, weighing approximately 20 grams.The lithium-ion battery can be recharged with a conventional cable (e.g., one that connects to a remote infusion pump or a display module) or via a wireless mechanism. Given the above, in one aspect, the invention provides a system for determining a patient's arterial blood pressure (i.e., systolic, diastolic, and MAP) value. The system comprises: 1) a catheter inserted into the patient's venous system; 2) a pressure sensor connected to the catheter that measures physiological signals indicating pressure in the patient's venous system; and 3) a processing system configured to: i) receive the physiological signals from the pressure sensor; and ii) process the physiological signals using an algorithm to determine the arterial blood pressure value. In some modalities, the processing system is also configured to operate an algorithm that filters the respiratory components of the physiological signals to determine the arterial blood pressure (AB) value. For example, to perform this filtering, the algorithm can operate a bandpass filter or use a wavelet-based filtering approach (e.g., a continuous wavelet transform (CWT), a discrete wavelet transform (DWT), or an adaptive filter that uses parameters determined by another sensor, e.g., a patch sensor) to filter out the respiratory components. In other modalities, the IVDS includes an enclosure that connects directly to the patient and houses the processing system, which is typically a circuit board containing a microprocessor. The processing system may also include a motion detection sensor, such as an accelerometer (and typically a 3-axis accelerometer) or a gyroscope. In some modalities, the processing system is further configured to receive signals from the motion detection sensor and process them to determine the degree of patient movement. The processing system then collectively processes this parameter and the patient's physiological signals to determine blood pressure. In other modalities, the processing system is further configured to process signals from the motion detection sensor to determine the relative height of a body part (e.g., an arm, wrist, or hand) associated with the patient.Here, for example, the signals could be those detected along one axis of the 3-axis accelerometer. The processing system can then collectively process the relative height associated with the body part and the physiological signals to determine the arterial blood pressure value. In other modalities, the system interacts with an external calibration source (e.g., a blood pressure cuff or arterial catheter) that measures blood pressure using established, conventional technology. In the present modality, the processing system is further configured to receive a calibration blood pressure value from the external source and then processes this value along with physiological signals to determine the arterial blood pressure value. In related modalities, the processing system is further configured to determine and then process a patient-specific ratio of venous to arterial blood pressure, along with the calibration blood pressure value and physiological signals, to determine the arterial blood pressure value.Here, the patient-specific relationship between venous BP and arterial BP can be derived from the physiological signals measured by the pressure sensor, or from the patient's biometric information (e.g., the patient's sex, age, weight, height, or BMI). In other configurations, the system also includes a wireless transceiver (e.g., a Bluetooth®, Wi-Fi, or cellular transceiver) that wirelessly receives the calibration blood pressure value from the external source, which in turn includes a paired wireless transceiver. Furthermore, the wireless transceiver can also wirelessly transmit the arterial blood pressure value to an external display system (e.g., an infusion pump, a remote display, a computer, a mobile phone, or an electronic health record system). In another aspect, the invention provides a system for determining when a liquid solution (e.g., saline solution or drug mixed with a liquid such as saline solution) delivered by an intravenous delivery system is administered outside of a vein within a patient.The system includes: 1) a catheter that is inserted into the vein; 2) a pressure sensor connected to the catheter that measures pressure signals indicating pressure within the vein; 3) an impedance measurement system that measures impedance signals indicating the electrical impedance of the tissue near the vein; and 4) a processing system configured to: i) receive pressure signals from the pressure sensor; ii) receive impedance signals from the impedance measurement system; and iii) collectively process the pressure and impedance signals with an algorithm to determine when the fluid solution provided by the intravenous delivery system is administered outside the vein. iviA / a / ¿u¿ó / uu io ι i In certain modalities, the algorithm is configured to evaluate time-dependent changes in pressure signals to determine when the fluid delivered by the intravenous administration system is being administered outside the vein. For example, time-dependent changes might indicate that the pressure is increasing or decreasing (typically rapidly) within the vein. Or they might be the sudden presence or absence of short-term pressure pulses induced by the patient's heart, or the presence or absence of long-term pressure pulses induced by the intravenous administration system. In related modalities, the algorithm is further configured to evaluate time-dependent changes in pressure signals to determine when the fluid delivered by the intravenous administration system is being delivered outside the vein. For example, time-dependent changes in impedance signals might be an increase or decrease in the electrical impedance measured from the tissue proximal to the vein. In related modalities, the processing system is further configured to evaluate the electrical conductivity of the fluid delivered by an intravenous administration system. This is because a fluid with relatively high electrical conductivity (compared to the patient's tissue) will cause the measured impedance to decrease, while a fluid with relatively low conductivity will cause it to increase. In other modalities, the system includes a flexible substrate (e.g., a pad or adhesive bandage) that secures the catheter to the patient. The flexible substrate may include an electrode array (e.g., those made of a hydrogel material). In some modalities, each electrode in the electrode array is in electrical contact with the impedance measurement system, and at least one electrode is configured to inject electrical current into the tissue near the vein, while at least one other electrode in the electrode array is configured to measure a signal induced by the electrical current. For example, in some modalities, at least two electrodes in the electrode array are configured to measure a voltage change induced by the electrical current. In some models, the impedance measurement system consists of a collection of discrete circuit components. Alternatively, it can be a single integrated circuit. In other configurations, the system also includes a temperature sensor that measures time-dependent temperature signals indicating the temperature of the tissue near the vein. IV infiltration is typically characterized by a rapid drop in temperature, since the infiltrated fluid is usually at room temperature (e.g., approximately 21 °C (70 °F)), while the human body has a relatively higher temperature (e.g., around 36.6–37.2 °C (98–99 °F)). In some cases, however, a rise in temperature indicates IV infiltration.In any case, in this mode, the processing system is also configured to: 1) receive temperature signals from the temperature sensor; and 2) collectively process the temperature signals, along with the pressure and impedance signals, with an algorithm to determine when the liquid solution provided by the intravenous delivery system is administered outside the vein. iviA / a / ¿u¿ó / uu io ι i In other modalities, the processing system is also configured to process pressure signals or impedance signals, or some combination thereof, to determine at least one physiological parameter (e.g., HR, RR, or FLUIDS) corresponding to the patient. In some modalities, the processing system further processes the signal components related to the patient's HR and RR to determine a physiological parameter (e.g., wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary arterial pressure) that indicates the patient's fluid status. In these modalities, the processing system transforms the signals in the frequency domain to generate a signal in the frequency domain before determining the physiological parameter. The transformation method is usually an FFT, CWT, or DWT. In some configurations, the low-pass filter typically separates a signal component containing free-flow (FC) and free-flow (FR) components from the amplified signal. The low-pass filter generally includes circuit components that generate a filter cutoff between 10 and 30 Hz. In other configurations, the circuit system also includes a high-pass filter that receives the signals amplified twice and, in response, generates a signal filtered twice. In this case, the high-pass filter typically includes circuit components that generate a filter cutoff between 0.01 and 1 Hz. In some models, the circuit system also includes a secondary low-pass filter that receives the signals amplified twice and, in response, generates a signal filtered three times. In this case, the secondary low-pass filter typically includes circuit components that generate a filter cutoff between 10 and 30 Hz. In other modalities, the system additionally includes a flash memory system that stores a digital representation of the signal amplified twice or a signal derived from it. In some modalities, the bioimpedance system can be replaced by a bioreactance detection system. In other modalities, the physiological parameters measured by the system are selected from a group that includes blood pressure, SpO2, stroke volume, stroke index, cardiac index, thoracic impedance, fluid volume, intercellular fluid volume, and extracellular fluid volume. In still other modalities, the second set of parameters is selected from a group that includes F0, F1, energies associated with F0 and F1, mathematical combinations of F0 and F1, and parameters derived from these. The processing system can operate a linear mathematical model to collectively process the signals described above. Alternatively, it can operate an artificial intelligence-based algorithm to collectively process the first and second sets of parameters. In another aspect, the invention provides a system for monitoring a patient's physiological parameter and determining when a liquid solution delivered by a catheter inserted into a vein is administered outside the vein. The system comprises a flexible substrate (e.g., a bandage-like component) that secures the catheter to the patient and includes at least one sensor. The sensor measures signals indicating the physiological parameter and determines when the liquid solution is administered outside the vein. The system also includes a processing system that: i) receives the signals from the sensor; ii) processes the signals with a first algorithm to determine the physiological parameter; and iii) processes the signals with a second algorithm to determine when the liquid solution delivered by the catheter is administered outside the vein. In some models, the sensor is at least one electrode (for example, an electrode with a hydrogel component). More typically, the sensor includes at least four electrodes, and the system also includes an electrical impedance circuit that is electrically connected to each of the four electrodes. The electrical impedance circuit can inject electrical current into a first set of electrodes and measure bioelectrical signals from a second set of electrodes. During a measurement, the circuit processes the bioelectrical signals from the second set of electrodes to generate a time-dependent IMP waveform. The processing system then receives the time-dependent IMP waveform, and the first algorithm that operates processes the time-dependent IMP waveform to determine a heart rate (HR), respiratory rate (RR), or fluid volume value.The second algorithm that operates additionally processes the time-dependent IMP waveform to determine when the liquid solution delivered by the catheter is administered outside the vein. In another configuration, the sensor is a temperature sensor (e.g., a thermistor, thermocouple, resistance temperature detector, thermometer, optical sensor, or thermal flow sensor). Here, the system also includes a temperature measurement circuit that is electrically connected to the temperature sensor. During a measurement, the temperature measurement circuit processes the signals from the temperature sensor to generate a time-dependent temperature waveform. The processing system then receives this time-dependent IMP waveform, and the first operating algorithm processes it to determine a skin or core temperature value. The second operating algorithm further processes the time-dependent temperature waveform to determine when the fluid delivered by the catheter is being administered outside the vein. In other configurations, the system includes a motion sensor (e.g., an accelerometer or gyroscope), and the motion sensor generates a time-dependent motion waveform (e.g., along one of its three axes). The processing system can receive and analyze this time-dependent motion waveform and the signals generated by the sensor to determine the physiological parameter. Furthermore, the processing system is also configured to receive and analyze this time-dependent motion waveform and the signals generated by the sensor to determine when the fluid delivered by the catheter is being administered outside the vein. In light of this disclosure, and without limiting the scope of the invention in any way, in a first aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, a system for determining a patient's arterial blood pressure value includes a catheter, a pressure sensor, and a processing system. The catheter is configured to be inserted into the patient's venous system. The pressure sensor is connected to the catheter and configured to measure physiological signals indicating pressure in the patient's venous system. The processing system is configured to: i) receive the physiological signals from the pressure sensor; and ii) process the physiological signals with an algorithm to determine the arterial blood pressure value. In a second aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to operate an algorithm that filters the respiratory components of physiological signals to determine the value of arterial blood pressure. In a third aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the algorithm is further configured to operate a bandpass filter to filter out the respiratory components of the physiological signals. In a fourth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the algorithm is further configured to operate a wavelet-based filter to filter respiratory components from physiological signals. In a fifth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is enclosed in a housing that is configured to attach directly to the patient. In a sixth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system further comprises a motion detection sensor. In a seventh aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the motion detection sensor is one of an accelerometer and a gyroscope. In an eighth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to receive signals from the motion detection sensor and process them to determine the degree of patient movement. In a ninth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to collectively process the degree of patient movement and physiological signals to determine the value of arterial blood pressure. In a tenth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to receive signals from the motion detection sensor and process them to determine a relative height associated with a body part associated with the patient. In an eleventh aspect of the present description, which may be combined with any other aspect listed herein unless otherwise specified, the body part is the patient's arm. In a twelfth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the iviA / a / ¿u¿ó / uu io ι i processing system is further configured to collectively process the relative height associated with the patient's associated body part and physiological signals to determine the blood pressure value. In a thirteenth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to receive a blood pressure calibration value from an external source. In a fourteenth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to process the calibration blood pressure value with physiological signals to determine the blood pressure value. In a fifteenth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the external source is a blood pressure cuff and arterial catheter. In a sixteenth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to process a patient-specific ratio of venous blood pressure to arterial blood pressure, together with the blood pressure calibration value and physiological signals, to determine the arterial blood pressure value. In a seventeenth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to process physiological signals to determine the patient-specific relationship between venous blood pressure and arterial blood pressure. In an eighteenth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to process patient-specific biometric information to determine the patient-specific relationship between venous blood pressure and arterial blood pressure. In a nineteenth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, biometric information includes at least one of the following: patient sex, age, weight, height, and BMI. In a twentieth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the system further includes a wireless transceiver configured to wirelessly receive the calibration blood pressure value from the external source. In a twenty-first aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the wireless transceiver is a Bluetooth®, Wi-Fi, or cellular transceiver. In a twenty-second aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the system further includes a wireless transceiver configured to wirelessly transmit the arterial blood pressure value to an external display system. In a twenty-third aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the external display system is an infusion pump, a remote display, a computer, a mobile phone, or a medical records system. In a twenty-fourth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, a system for determining a patient's arterial blood pressure value includes a catheter, a pressure sensor, a motion sensor, and a processing system. The catheter is configured for insertion into the patient's venous system. The pressure sensor is connected to the catheter and configured to measure physiological signals indicating pressure in the patient's venous system. The motion sensor is configured to measure motion signals.The processing system is configured to: i) receive physiological signals from the pressure sensor; ii) receive movement signals from the movement sensor; iii) process the movement signals by comparing them with a predetermined threshold value to determine when the patient has a relatively low degree of movement; and iv) process the physiological signals to determine the value of arterial blood pressure. In a twenty-fifth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, a system for determining a patient's arterial blood pressure value includes a catheter, a pressure sensor, a motion sensor, and a processing system. The catheter is configured for insertion into the patient's venous system. The pressure sensor is connected to the catheter and configured to measure physiological signals indicating pressure in the patient's venous system. The motion sensor is configured to measure motion signals.The processing system is configured to: i) receive physiological signals from the pressure sensor; ii) receive motion signals from the motion sensor; iii) process the motion signals to determine a relative height between a body part associated with the patient and an infusion system; and iv) process the physiological signals and the relative height to determine the arterial blood pressure value. In a twenty-sixth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, a system for determining when a liquid solution delivered by an intravenous administration system is administered outside of a vein within a patient includes a catheter, a pressure sensor, an impedance measurement system, and a processing system. The catheter is configured for insertion into the vein. The pressure sensor is connected to the catheter and configured to measure pressure signals indicating pressure within the vein. The impedance measurement system is configured to measure impedance signals indicating the electrical impedance of the tissue adjacent to the vein.The processing system is configured to: i) receive pressure signals from the pressure sensor; ii) receive impedance signals from the impedance measurement system; and iii) collectively process the pressure signals and impedance signals with an algorithm to determine when the liquid solution provided by the intravenous delivery system is administered outside the vein. In a twenty-seventh aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the algorithm is configured to evaluate time-dependent changes in pressure signals to determine when the liquid solution provided by the intravenous delivery system is being administered out of the vein. In a twenty-eighth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, time-dependent changes in pressure signals are one of increase and decrease of pressure within the vein. In a twenty-ninth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, time-dependent changes in pressure signals are one of the presence or absence of pressure pulses induced by the patient's heart. In a thirtieth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, time-dependent changes in pressure signals are one of the presence or absence of pressure pulses induced by the intravenous delivery system. In a thirty-first aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the algorithm is further configured to evaluate time-dependent changes in impedance signals to determine when the liquid solution provided by the intravenous delivery system is administered out of the vein. In a thirty-second aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, time-dependent changes in impedance signals are one of increase and decrease in the electrical impedance of tissue proximal to the vein. In a thirty-third aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to evaluate the electrical conductivity of the liquid solution provided by an intravenous delivery system. In a thirty-fourth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the system further includes a flexible substrate configured to secure the catheter to the patient. In a thirty-fifth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the flexible substrate comprises an assembly of electrodes. In a thirty-sixth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, each electrode in the electrode assembly comprises a hydrogel material. In a thirty-seventh aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, each electrode in the electrode assembly is in electrical contact with the impedance measurement system. In a thirty-eighth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, at least one electrode in the electrode array is configured to inject electrical current into the tissue proximal to the vein. In a thirty-ninth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, at least one electrode in the electrode array is configured to measure an electrical current-induced signal. In a fortieth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, at least two electrodes in the electrode array are configured to measure a voltage change induced by electric current. In a forty-first aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the impedance measurement system comprises a collection of discrete circuit components. In a forty-second aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the impedance measurement system comprises a single integrated circuit. In a forty-third aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the system further includes a temperature sensor configured to measure time-dependent temperature signals indicating the temperature in the tissue proximal to the vein. In a forty-fourth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the time-dependent temperature signals are one of increase and decrease in temperature near the vein. In a forty-fifth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to: 1) receive temperature signals from the temperature sensor; and ii) collectively process the temperature signals, along with the pressure and impedance signals, with an algorithm to determine when the liquid solution provided by the intravenous delivery system is administered outside the vein. iviA / a / ¿u¿ó / uu io ι i In a forty-sixth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to process pressure signals to determine at least one physiological parameter corresponding to the patient. In a forty-seventh aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the physiological parameter is one of heart rate and respiratory rate. In a forty-eighth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to process impedance signals to determine at least one physiological parameter corresponding to the patient. In a forty-ninth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the physiological parameter is one of heart rate and respiratory rate. In a fiftieth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, a system for determining when a liquid solution delivered by an intravenous administration system is administered outside of a vein within a patient includes a catheter, a pressure sensor, an impedance measurement system, a temperature measurement system, and a processing system. The catheter is configured for insertion into the vein. The pressure sensor is connected to the catheter and configured to measure pressure signals indicating pressure within the vein. The impedance measurement system is configured to measure impedance signals indicating the electrical impedance of the tissue adjacent to the vein. The temperature measurement system is configured to measure temperature signals indicating the temperature of the tissue adjacent to the vein.The processing system is configured to: i) receive pressure signals from the pressure sensor; ii) receive impedance signals from the impedance measurement system; iii) receive temperature signals from the temperature sensor; and iii) collectively process the pressure signals, impedance signals, and temperature signals with an algorithm to determine when the liquid solution provided by the intravenous delivery system is administered outside the vein. In a fifty-first aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, a system for determining a patient's physiological parameter, when a liquid solution delivered by an intravenous administration system is administered outside of a vein within the patient, includes a catheter, a pressure sensor, an impedance measurement system, and a processing system. The catheter is configured for insertion into the vein. The pressure sensor is connected to the catheter and configured to measure pressure signals indicating pressure within the vein. The impedance measurement system is configured to measure impedance signals indicating the electrical impedance of the tissue adjacent to the vein.The processing system is configured to: i) receive pressure signals from the pressure sensor; ii) receive impedance signals from the impedance measurement system; iii) collectively process the pressure and impedance signals with an algorithm to determine when the liquid solution provided by the intravenous administration system is administered outside the vein; and iv) process at least one of the pressure and impedance signals to determine the patient's physiological parameter. In a fifty-second aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, a system for monitoring a patient's physiological parameter and determining when a liquid solution delivered by a catheter configured to be inserted into a vein within the patient is administered outside the vein, includes a flexible substrate, a sensor, and a processing system. The flexible substrate includes at least one sensor and is configured to secure the catheter to the patient. The sensor is configured to measure signals indicating the physiological parameter and determine when the liquid solution is administered outside the vein.The processing system is configured to: i) receive the sensor signals; ii) process the signals with a first algorithm to determine the physiological parameter; and iii) process the signals with a second algorithm to determine when the liquid solution provided by the catheter is administered outside the vein. In a fifty-third aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the sensor is at least one electrode. In a fifty-fourth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the electrode comprises a hydrogel component. In a fifty-fifth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the sensor comprises at least four electrodes. In a fifty-sixth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the system further includes an electrical impedance circuit configured to be electrically connected to each of the four electrodes. In a fifty-seventh aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the electrical impedance circuit is configured to inject electrical current into a first set of electrodes, and measure bioelectrical signals from a second set of electrodes. In the fifty-eighth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the electrical impedance circuit is configured to process the bioelectrical signals from the second set of electrodes to generate a time-dependent impedance waveform. In a fifty-ninth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system receives the time-dependent impedance waveform, and the first algorithm operated by the processing system processes the time-dependent impedance waveform to determine a heart rate value. In a sixtieth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system receives the time-dependent impedance waveform, and the first algorithm operated by the processing system processes the time-dependent impedance waveform to determine a breathing rate value. In a sixty-first aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system receives the time-dependent impedance waveform, and the first algorithm operated by the processing system processes the time-dependent impedance waveform to determine a fluid value. In a sixty-second aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system receives the time-dependent impedance waveform, and the second algorithm operated by the processing system processes the time-dependent impedance waveform to determine when the liquid solution provided by the catheter is administered out of the vein. In a sixty-third aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the sensor is a temperature sensor. In a sixty-fourth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the temperature sensor is one of thermistor, thermocouple, resistance temperature detector, thermometer, optical sensor, and thermal flow sensor. In a sixty-fifth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the system further includes a temperature measuring circuit configured to be electrically connected to the temperature sensor. In a sixty-sixth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the temperature measuring circuit is configured to process the temperature sensor signals to generate a time-dependent temperature waveform. In a sixty-seventh aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system receives the time-dependent temperature waveform, and the first algorithm operated by the processing system processes the time-dependent temperature waveform to determine a skin temperature value. In a sixty-eighth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system receives the time-dependent temperature waveform, and the first algorithm iviA / a / ¿u¿ó / uu io ι i operated by the processing system processes the time-dependent temperature waveform to determine a core temperature value. In a sixty-ninth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system receives the time-dependent temperature waveform, and the second algorithm operated by the processing system processes the time-dependent temperature waveform to determine when the liquid solution provided by the catheter is administered out of the vein. In a seventieth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the system further includes a motion sensor. In a seventy-first aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the motion sensor is one of an accelerometer or gyroscope. In the seventy-second aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the motion sensor is configured to generate a time-dependent motion waveform. In a seventy-third aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to receive the time-dependent motion waveform and analyze it and the sensor signals to determine the physiological parameter. In a seventy-fourth aspect of this disclosure, which may be combined with any other aspect listed herein unless otherwise specified, the processing system is further configured to receive and analyze the time-dependent motion waveform and sensor signals to determine when the liquid solution delivered by the catheter is administered outside the vein. The additional features and advantages of the disclosed devices, systems, and methods are described and will be evident from the following Detailed Description and Figures. The features and advantages described herein are not exhaustive, and in particular, many additional features and advantages will be apparent to a person skilled in the art from the figures and description. Furthermore, any particular embodiment need not possess all the advantages listed herein. It should also be noted that the language used in the specification has been selected for readability and instruction, and not to limit the scope of the inventive subject matter. Brief description of the drawings Figure 1 is a drawing of the IVDS according to the invention; Figure 2A is a graph showing time-dependent waveforms of motion, temperature, IMP, and PVP measured before and after IV infiltration using the IVDS of Figure 1; Figures 2B, 2C, and 2D are schematic drawings showing how the PVP, IMP, and temperature sensors within the IVDS sensor respectively measure the corresponding signals from a patient; Figure 3A is a graph of the time-dependent PVP waveform from Figure 2A; Figures 3B and 3C are graphs of the time-dependent PVP waveform from Figure 3A measured, respectively, before and after IV infiltration; Figure 4A is a graph of SIS PA measured by both a cuff-based system and a cuffless technique of the previous technique based on pulse transit time; Figure 4B is a graph of SIS PA measured both by a catheter inserted into the artery of a porcine subject and by a technique for processing PVP waveforms used in the IVDS of Figure 1; Figure 5 is a schematic drawing of the IVDS in Figure 1 and an infusion pump attached to a patient in a hospital bed; Figure 6 is a schematic drawing showing how the IVDS in Figure 1 is attached to a patient and measures PVP waveforms; Figure 7A is an image of a PVP conditioning circuit board used in the IVDS of Figure 1 to amplify and condition the PVP signals generated by the sensor shown in Figure 6B; Figure 7B is a photograph of the PVP conditioning circuit board indicated by the image shown in Figure 7A; Figure 8 is an electrical schematic describing the PVP conditioning circuit board of Figures 7A and 7B, which feature circuits for filtering, amplifying, and digitizing PVP-AC and PVP-DC waveforms; Figure 9A is a time-dependent graph of a first PVP-AC waveform measured after a first amplification stage described by the electrical schematic in Figure 8; Figure 9B is a time-dependent graph of a second AC PVP waveform measured after a second filter / amplifier stage described by the electrical schematic in Figure 8; Figure 10A is a graph of a time-dependent PVP waveform featuring pulses generated by a conventional pulse algorithm; Figure 10B is a graph of a time-dependent PVP waveform featuring pulses generated by a pulse algorithm used in the IVDS of Figure 1; Figure 11A is a graph of a time-dependent arterial PA waveform featuring pulses generated by a pulse algorithm indicated by Figure 10B; Figure 11B is a graph of a time-dependent arterial PA waveform measured from a relatively short time segment of Figure 11A and showing both cardiac and respiratory components; Figure 11C is a graph of a time-dependent PVP waveform featuring pulses generated by a pulse algorithm indicated by Figure 10B; iviA / a / ¿u¿ó / uu io ι i Figure 11D is a graph of a time-dependent PVP waveform measured from a relatively short time segment of Figure 11C that shows both cardiac and respiratory components; Figures 12A-E are graphs of time-dependent PVP and arterial PA waveforms measured from five different porcine subjects; Figure 13A is a graph showing the relationship between pressure and volume changes for human veins and arteries; Figure 13B is a graph showing how the relationship between pressure and volume changes for human veins and arteries during periods of vascular smooth muscle contraction (e.g., during breathing), which reduces vascular distensibility; Figures 14A and 14B are pulse plots generated from, respectively, time-dependent PVP and arterial PA waveforms that are both unfiltered and filtered to remove a respiratory artifact; Figure 15 is a schematic drawing of the IVDS in Figure 1 connected via Bluetooth® to both a BP cuff that calibrates its BP measurement and an infusion pump that displays the information it generates; Figure 16 is a graph of time-dependent motion and PVP waveforms measured while a subject's arm was arranged in different positions; Figure 17 is a flowchart that shows an algorithm used by the IVDS in Figure 1 to determine SIS and DIA values ​​from PVP waveforms; Figures 18A-E are graphs of time-dependent SIS PA values ​​measured from an arterial PA waveform and a PVP waveform processed with the algorithm shown in Figure 17; Figure 19 is a graph derived from the information plotted in the graphs of Figures 18A-E that indicates the agreement between the SIS values ​​measured from both an arterial PA waveform and a PVP waveform processed with the algorithm indicated in Figure 17; Figure 20 is a graph showing time-dependent waveforms of motion, temperature, IMP, and PVP measured from a patient undergoing different postures and types of movement; and, Figures 21A and 21B are graphs showing, respectively, time-dependent PPG and IMP waveforms measured with the IVDS of Figure 1 and are used to calculate a patient's vital signs. Detailed description of the invention 1. Overview Although the following text provides a detailed description of numerous different embodiments, it should be understood that the legal scope of the invention described herein is defined by the words of the claims set forth at the end of this patent. The detailed description should be interpreted as illustrative only; it does not describe all possible embodiments, as this would be impractical, if not impossible. A person skilled in the art could implement numerous alternative embodiments, which would still fall within the scope of the claims. 2. IVDS Referring to Figure 1, an IVDS 80 according to the invention provides three main functions: 1) it secures an intravenous IV catheter 21 to a body component (e.g., an arm 23) of a patient to administer fluids (e.g., saline solution, medication dissolved in saline solution) into their venous system; 2) it simultaneously detects problems associated with the IV catheter (i.e., infiltration, extravasation, and occlusion) that may reduce the effectiveness of such administration; and 3) it simultaneously measures biometric signals which, once processed, generate physiological parameters of the patient (e.g., HR, RR, TEMP, SpO2), and most notably SIS and DIA. Computer systems in the hospital can analyze these physiological parameters and subsequently influence fluid administration to the patient, enabling a closed-loop system that can potentially improve patient care. The IVDS features a breathable, flexible polymer base 89, similar to that used in a large bandage, with a biocompatible adhesive on one side that secures the IV catheter 21 in place. In Figure 1, the IV catheter 21 is exposed, but during a medical procedure, it is inserted into a vein within the patient's arm 23. The polymer base 89 includes an array of electrodes 83 (typically four) made of a conventional hydrogel material; these are connected via a first set of embedded electrical traces 84 on a cable 88 that ultimately leads to an impedance circuit within an electronic module 94 enclosed in a housing 20 worn on the arm. The electrodes 83 are normally arranged in a linear configuration along the vein; alternatively, they can be arranged in a 'square' configuration, positioning them at the four corners of the polymer base 89.The electronic module 94 features a printed circuit board that supports various electronic components (e.g., circuits for signal amplification and power management; an accelerometer to characterize patient movement; a microprocessor and associated memory to process sensor-generated information; a wireless transmitter to transmit information to an external display; and a rechargeable battery to power the system) that enable the measurements described above. Located near the electronic module 94 is a PVP conditioning circuit board 95, described in more detail below with reference to Figures 6-9, which includes a series of analog amplifiers and filters that process signals from the pressure sensor 97, typically located at a first connector 91. The PVP conditioning circuit board 95 generates AC and DC PVP signals for follow-up processing. During use, the electrode array 83 adheres to the patient's skin to measure bioelectrical signals. These signals, once processed by the electronic module 94, indicate the electrical impedance of the tissue beneath the polymer base 89. The polymer base 89 also includes a temperature sensor 85, which connects via a second set of electrical traces 86 to cable 88. This cable transmits electrical signals from the electrodes 83 and the temperature sensor 85 to the first connector 91. The first connector 91 mates with a second connector 92, which transmits the electrical signals to the electronic module 94 inside the arm-worn housing 20. Typically, the second connector 92, the electronic module 94, and the arm-worn housing are considered reusable components of the IVDS, while the other components shown in Figure 1 are considered disposable. During use, catheter 21 is inserted into the patient's vein and connected to an infusion pump (not shown in the figure but indicated in Figure 15) via a segment of IV tubing 18a. A portion of tubing 18b passes through connector 91, which features the small pressure sensor 97 that measures the pressure of a fluid column within the tubing segment 18b. Small pressure fluctuations within the patient's venous system, in turn, modulate the pressure within the fluid column.Pressure sensor 97 measures these pressure fluctuations and, in response, generates electrical signals that pass through the first connector 91, the second connector 92, and to the electronic module 94, where they are conditioned (e.g., filtered, amplified) by the PVP conditioning circuit board 95 and then processed, as described in more detail below, to simultaneously measure parameters related to IV system performance and patient physiology. Figures 2A–D illustrate how the IVDS shown in Figure 1 can characterize catheter infiltration. More specifically, Figure 2A shows a graph of time-dependent waveforms of motion, temperature, IMP, and PVP measured using the IVDS. For these measurements, an infusion pump delivering fluids at a rate of 60 mL / hour was connected to a patient wearing a special arm-mounted device that facilitated infiltration. Sensors measuring temperature, IMP, PVP, and patient motion were connected directly to the arm-mounted device and via cables similar to those described in Figure 1 to an electronic module within a housing that was also placed on the arm. As shown in the graph, infiltration began at approximately 60 seconds. Fluctuations in the motion waveform indicate that the patient moved at that time, causing catheter 21 to push from inside a vein 124 in the arm-worn kit into the surrounding tissue 122, which is typically composed of agar, a conductive, gelatinous material. The arm-worn kit also includes synthetic components representing bone 126 and skin 120. In addition, a control circuit and motorized pump (not shown in the figure) are connected to the vein and pump a blood-like conductive fluid at a heart rate of approximately 60 beats / min. Referring to Figure 2C, electrodes 83a-d are connected to the skin of the device worn on the arm, and they detect signals that are processed by the impedance circuit within the electronic module to determine the electrical impedance of the tissue beneath them. More specifically, for impedance measurement, external electrodes 83a and 83b inject a high-frequency (typically 20-100 kHz), low-amperage (typically 10-1000 A) current through the skin. 120 and into the surrounding tissue 122. The injected current propagates into the surrounding tissue, which has an electrical conductivity similar to that of human tissue. The resistance of the surrounding tissue affects the current flow, which manifests as a voltage drop that is measured by a pair of internal electrodes 83c, 83d. This voltage drop is digitized by the impedance system to produce the IMP waveform. As shown in the graph in Figure 2A, before infiltration, the IMP waveform is relatively stable. Immediately after infiltration, its value steadily decreases; this trend continues for at least 600 seconds, at which point the test ends. This is because, prior to infiltration, the infusion pump delivers fluid (which in this case is conductive) directly into the vein, where the blood-like fluid flow driven by the control circuit and motorized pump quickly removes it, thus minimizing its impact on the impedance of the surrounding tissue.122 However, after the catheter is pushed through the vein,124 the fluid from the infusion pump flows directly into the surrounding tissue.122 And because the fluid is conductive, it reduces the impedance (i.e., resistance) of the tissue, causing the IMP waveform to gradually decrease. A similar situation exists for the temperature waveform, as shown in the graph in Figure 2A. Here, the temperature of the delivered fluid from the infusion pump is kept approximately 20°F (6.7°C) cooler than that of the components within the equipment worn on the arm. This is intended to mimic the situation in typical hospital settings, where fluids and medications are generally kept at room temperature (approximately 72°F (22°C)) when administered via IV systems, while the human body is more than 20°F (6.7°C) warmer. The relatively cooler temperature of the fluid from the infusion pump infiltrating from vein 124 into the surrounding tissue 122 causes the temperature of the surrounding tissue to decrease. This is measured by temperature sensor 85, as shown in Figure 2D.As shown in Figure 2A, this results in a temperature waveform that slowly decreases after infiltration in a manner similar to the IMP waveform. The PVP waveform is measured with a pressure sensor configured as shown in Figure 1 and exhibits several signal components that change after infiltration. As indicated by Figures 2A, 2B, and 3A–3C, the PVP waveform, like the temperature and IMP waveforms, is relatively stable before infiltration. As shown in Figure 3B, a close-up view of the PVP waveform taken from a time period within circle 142 in Figure 3A, prior to infiltration, the PVP waveform exhibits a set of small, periodic pulses 144, representing the flow of blood-like fluid driven by the control circuit and motorized pump through the vein. Notably, in Figure 3B, the periodic pulses 144 occur at a frequency of approximately 60 beats / min, as set by the control circuit.In addition, prior to infiltration, the PVP waveform exhibits periods of high-frequency noise 146 caused by the infusion pump, which periodically delivers fluid to the vein at a rate of 60 mL / hour. iviA / a / ¿u¿ó / uu io ι i Several things happen to the PVP waveform after infiltration. Referring to Figures 3A and 3C, the latter being a close-up view of the PVP waveform taken from a time period within circle 140 in Figure 3A, immediately after infiltration, the fluid from the infusion pump is no longer delivered to the vein and flows into the surrounding tissue. This manifests as a rapid increase in pressure from around 20 mmHg before infiltration to nearly 300 mmHg after infiltration. Furthermore, because the catheter is no longer in the vein, the heartbeat-induced pulses evident in Figure 3B are no longer present.Furthermore, because the surrounding tissue is decidedly less efficient at eliminating fluids, each bolus delivered by the infusion pump causes a pressure pulse 150 that rises from a baseline of approximately 250 mmHg to a peak of approximately 300 mmHg, before decaying in a manner that reflects the fluid diffusing into the surrounding tissue. Each pressure pulse 150 is caused entirely by the infusion pump and thus exhibits high-frequency noise 148, similar to component 146 in Figure 3B. In summary, the PVP waveform contains several signal components (rapid pressure rise, pulses induced by heartbeats and their subsequent disappearance, large pressure pulses) that an algorithm can process to characterize IV infiltration. Such an algorithm can collectively process PVP waveforms along with IMP, temperature, and motion waveforms to better detect this event. Furthermore, other sensors, such as those measuring optical, acoustic, bioreactant, and other waveforms, can be added to the IVDS to further enhance detection. Additional algorithms can also process the PVP waveform, which represents venous pressure, to determine arterial blood pressure, as shown in Figures 10-13 and 16-18, and the associated descriptions of these figures below. Figure 4B illustrates the accuracy of such a blood pressure measurement, particularly when compared with prior art cuffless approaches based on technologies such as PTT and PAT. For example, the graph shown in Figure 4A displays typical results for SIS measured using a PTT-based approach. The figure indicates a reasonable correlation with a reference measurement (in this case, taken by a pair of clinicians measuring blood pressure by auscultation). However, the PTT-based approach is relatively insensitive to the rapid changes in blood pressure detected by the reference measurement.In contrast, Figure 4B shows the continuous arterial blood pressure (specifically SIS) measured from a subject using a permanent arterial line, along with the blood pressure calculated from a corresponding PVP waveform simultaneously measured from the same subject using an algorithm described herein. Here, the SIS value determined by PVP is highly correlated with that of the reference measurement, even for rapid and brief increases and decreases in blood pressure. Similar measurements are described in more detail below, particularly with reference to Figures 11, 12, 14, and 18. This indicates that the IVDS described herein, in addition to securing a catheter in place, can further measure blood pressure values ​​while simultaneously detecting IV infiltration. Figure 5 shows how the IVDS 80 system described herein can be incorporated into a hospital setting to measure a patient 11. Here, the IVDS 80 is implemented within a system 10 that features an IV system 19 to characterize IV-related parameters and vital signs of a patient 11 lying in a hospital bed 24. The arm-wear housing 20 within the IVDS 80 encloses the electronic module and PVP conditioning circuit board, which is configured to amplify, filter, and digitize PVP signals. The arm-wear housing 20 terminates with a venous catheter 21 inserted into a vein in the patient's hand or arm. A remote processor 36 (e.g., a tablet or device with comparable functionality) connects to the arm-wear housing 20 via a wireless interface (e.g., Bluetooth®).In some modes, the remote processor 36 can also be connected to the arm-worn housing via wired means (e.g., cable); this can be used, for example, to charge the lithium-ion battery inside the electronic module. During a measurement, the remote processor 36 receives information from the IV system 19 and the IVDS 80, and analyzes it collectively as described in detail herein to monitor the patient. The IV system 19 features a bag 16 containing pharmaceutical compounds and / or fluids (herein, medication 17) for the patient. The bag 16 is connected to an infusion pump 12 via a first tube 14. A standard IV pole 28 supports the bag 16, the infusion pump 12, and the remote processor 36. A display 13 on the front panel of the infusion pump 12 indicates the type of medication being administered to the patient, its flow rate, metering time, etc. The medication 17 flows from the bag 16 through the first tube 14 and into the infusion pump 12. From there, it is properly dosed and flows through a second tube 18, through connector 91, which has a pressure sensor, and finally through the venous catheter 21 and into the patient's venous system 23.The housing 20 worn on the arm connects to connector 91 and is normally attached to the patient's arm or hand, for example, using an adhesive such as medical tape or a disposable electrode. The venous catheter 21 may be a standard venous access device and, therefore, may include a needle, catheter, cannula, or other means for establishing a smooth connection between the catheter 21 and the patient's peripheral venous system 23. The venous access device may be a separate component connected to the venous catheter 21 or may be formed as an integral part of it. In this way, the IV system 19 delivers medication 17 to the patient's venous system 23, while the IVDS 80, which features a pressure measurement system and is described in more detail below, simultaneously measures signals related to the patient's PVP and vital signs. It is important to note that, as described in more detail below, the IVDS 80 is designed to maintain constant, seamless communication with the patient's circulatory system (and in particular the venous system) while deployed near (or directly on) the patient's body. It features electronic systems to measure analog pressure signals within the patient's venous system to generate PVP waveforms, which are then amplified and filtered to optimize their signal-to-noise ratios.An analog-to-digital converter within the arm-worn housing digitizes the analog PVP waveforms before transmitting them over the cable. This minimizes any noise (caused, for example, by cable movement) that would normally affect the transmitted analog signals and ultimately introduce inaccuracies in downstream measured values ​​(e.g., BP, HR, RR, F0, and F1). In particular, this design provides a relatively short conduction path between where the PVP waveforms are first detected and then processed and digitized. Ultimately, this results in signals that are more likely to produce highly accurate wedge pressure values ​​(and, in modalities, pulmonary arterial pressure, and particularly the diastolic component of this pressure, blood volume, and other fluid-related parameters). Figure 6 shows in more detail the arm-worn housing 20, its method of operation, and how its internal components (the electronic module and the PVP conditioning circuit board) function. The housing 20 is designed to rest comfortably near or on the patient while it: 1) allows fluids (and / or medications) from the IV system to flow (as indicated by arrow 25) into the patient's venous system (box 27); 2) measures pressure signals from the patient's venous system with a pressure sensor (box 29); 3) filters / amplifies the pressure signals with circuitry that functions as analog amplifiers and filters (box 31); 4) digitizes the filtered / amplified signals with an analog-to-digital converter (box 33); and 5) transmits the digitized signals using a Bluetooth® transceiver for further processing by the remote processor (arrow 35). 3. PVP Conditioning Circuit Board. Figures 7A and 7B show, respectively, an image and a photograph of the PVP 62 conditioning circuit board inside the arm-mounted housing. Circuit board 62 was fabricated according to an electrical schematic, which is shown in Figure 8 (specifically component 100) and described in more detail below. The circuit board 62 shown in the figure is a 4-layer fiberglass / metal structure that includes metal pads soldered to, among other components, an analog-to-digital converter 68, an accelerometer 75, operational amplifiers 71a-f, and power regulators 72a-b. More specifically, the operational amplifiers 71a-d form high-pass and low-pass analog filters, and the operational amplifiers 71ef and power regulators 72a-b collectively regulate the power levels for the various components on circuit board 62.The accelerometer 75 measures the movement of the circuit board 62 and, in doing so, of any part of the patient's body to which it is attached. The analog-to-digital converter 68 digitizes analog PVP waveforms after they have been filtered and converts them into digital waveforms with a resolution of 16 bits and a maximum digitization rate of 200 Ksamples / second (hereafter Ksps). The PVP conditioning circuit board 62 further includes metal-plated hole assemblies that support a 4-pin connector 69, two 6-pin connectors 77 and 78, and a 3-pin connector 79. More specifically, connector 69 connects directly to the pressure transducer, where it receives a common ground signal and analog PVP waveforms representing the pressure in the patient's venous system. These waveforms are filtered and digitized as described in more detail below. Through connector 79, the circuit board receives power (+5 V, +3.3 V, ground) from an external power source, for example, a battery or power supply located in the arm-worn housing. These power levels may differ in other embodiments of the invention.The digital signals and a corresponding ground from the analog-to-digital converter 68 terminate at connector 78; they exit circuit board 62 at this point, for example, through the wire segment 37 shown in Figure 2C. Connector 77 is used primarily for testing and debugging purposes, and allows the analog PVP signals, once they have passed through analog high-pass and low-pass filters, to be measured with an external device such as an oscilloscope. The PVP 62 conditioning circuit board is generally connected to the electronic module via a serial interface (e.g., SPI, I2C), which includes components for processing, storing, and transmitting data that is digitized by the analog-to-digital converter 68. For example, the electronic module typically includes a microprocessor, a microcontroller, or a similar integrated circuit and may also provide analog and digital circuitry for the IVDS.In the modalities, the microprocessor or microcontroller of the same can operate a computer code to process PVP-AC, PVP-DC, PPG, IMP, PA, and other time-dependent waveforms to determine vital signs (e.g., HR, HRV, RR, BP, SpO2, TEMP), hemodynamic parameters (CO, SV, FLUIDS), PVP waveform components (e.g., F0, F1, and amplitudes and energies associated with them), and associated parameters (e.g., wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary arterial pressure) related to the patient's fluid status.Processing by the microprocessor in this manner, as used herein, means using computer code or a comparable approach to digitally filter (e.g., with a high-pass, low-pass, and / or band-pass filter), transform (e.g., using FFT, CWT, and / or DWT), mathematically manipulate, and generally process and analyze waveforms and parameters and constructs derived therefrom with algorithms known in the art. Examples of such algorithms include those described in the following pending and issued patents, the contents of which are incorporated herein by reference: “NECK-WORN PHYSIOLOGICAL MONITOR,” U.S. Serial No. 14 / 975,646, filed December 18, 2015; “NECKLACE-SHAPED PHYSIOLOGICAL MONITOR,” U.S. Serial No. 14 / 184,616, filed August 21, 2014; and “BODY-WORN SENSOR FOR CHARACTERIZING PATIENTS WITH HEART FAILURE, US Serial No.14 / 145,253, submitted on July 3, 2014. In related configurations, the electronic module may include both flash memory and random access memory to store waveforms and time-dependent numerical values, either before or after processing by the microprocessor. In still other configurations, the circuit board may include Bluetooth® and / or Wi-Fi transceivers to transmit and receive information. iviA / a / ¿u¿ó / uu io ι i The PVP waveforms measured with the system described herein exhibit signal components related to heartbeats and respiratory events, which can vary rapidly over time. Figure 9 shows examples of AC PVP waveforms and how they are amplified and conditioned by the PVP conditioning circuit board 62 in the housing 20 worn on the arm to improve their signal-to-noise ratio. More specifically, PVP waveforms typically have signal levels in the 550 V range, a relatively weak amplitude that can be difficult to process. Such signals have been described previously, for example, in U.S. Patent Application 16 / 023,945 (filed June 29, 2018, and published as U.S. Patent Publication 2019 / 0000326); U.S. Patent Application No. 14 / 853,504 (filed September 14, 2015, and published as U.S. Patent Publication No. 2016 / 0073959); and PCT Application No. PCT / US16 / 16420 (filed February 3, 2016, and published as WO 2016 / 126856). The contents of these pending patent applications are incorporated herein by reference.During a measurement, as described in these documents, a pressure sensor near the patient measures the PVP waveform and generates the corresponding analog signals. These signals typically travel through a relatively long cable and are amplified, filtered, and digitized by a system located some distance from the patient. However, because PVP waveforms are so weak and characterized by a low signal-to-noise ratio, they can be extremely difficult to measure. Therefore, it is advantageous to digitize these signals before they propagate through a long, lossy cable. Figure 8 shows a schematic 100 of the circuit board 62 shown in Figures 7AB. Schematic 100 includes: 1) a first set of circuit elements 102 designed to amplify and filter PVP-AC waveforms; 2) a second set of circuit elements 104 designed to amplify and filter PVP-DC waveforms; and 3) a 16-bit, 200 Ksps analog-to-digital converter 106 to digitize both of the PVP-AC waveforms as PVP-DC. More specifically, the circuit described by schematic 100 is designed to perform the following function in series on incoming PVP waveforms: Incoming PVP waveforms 1) Amplify the signal with a gain of 100X using a zero drift amplifier 2) Differentially amplify the signal with an additional gain of 10X 3) Filter the amplified signals with a 25 Hz, 2-pole low-pass filter This first part of the circuit provides a combined gain of approximately 1000x for the incoming PVP waveforms, amplifying the input signal (typically in the TV range) to a larger signal (in the mV range). The tracking low-pass filter removes any high-frequency noise. Ultimately, these steps facilitate the processing of AC-PVP and DC-PVP waveforms, as described below. In the descriptions provided herein, the term differential amplification refers to a process in which the circuit measures the difference between the positive (PJN in Figure 8) and negative (N_IN in Figure 8) terminals. Specifically, the output of the differential amplifier is a single-ended signal, zeroed at the midpoint of the system voltage. Alternatively, it could be zeroed at 0 V, although a midpoint between the voltage rails generally provides a more accurate and cleaner output signal. iviA / a / ¿u¿ó / uu io ι i The term zero-drift amplifier also refers to an amplifier that: 1) internally corrects for temperature and other forms of low-frequency signal error; 2) has a very high input impedance; and 3) has very low offset voltages. The incoming signal received by a zero-drift amplifier is typically extremely small, meaning it can be subject to interference, gain shifts, or loss of generated current at the amplifier's inputs; the amplifier's zero-drift architecture helps to reduce or eliminate these issues. After processing the input PVP waveforms, the circuit described by schematic 100 is designed to perform the following function in series on the PVP-AC and PVP-DC waveforms: PVP-AC waveforms only 1) Filter the signal with a 0.1 Hz, 2-pole high-pass filter 2) Filter the amplified signal with a 15 Hz, 2-pole low-pass filter 3) Amplify the signal with a gain of 50X Only the PVP-DC signal 1) Filter the signal with a 0.07 Hz, 2-pole low-pass filter 2) Filter the amplified signal with a 0.13 Hz, 2-pole low-pass filter 3) Amplify the signal with a gain of 10X Both PVP-AC and PVP-DC waveforms 1) Digitize the signals with a 16-bit, 200 Ksps Delta-Sigma analog-to-digital converter. With this level of digital signal processing, the 62 circuit board can process PVP waveforms directly in the patient's body and, more specifically, signals associated with IV infiltration, respiratory rate, and heart rate. It performs these functions without having to send signals through an external cable, an approach that can add noise and other signal artifacts and thus negatively affect the measurement of these parameters. As those familiar with the subject matter will appreciate, circuit elements 102, 104, and 106 shown in Figure 8 can have a comparable design that performs the steps described above with a scheme that differs slightly from the one described here. Furthermore, it can include other circuits and integrated components to enhance the measurement of the PVP signals and thus provide additional functionality. For example, circuit board 62 can also include a temperature / humidity sensor, a multi-axis accelerometer, an integrated gyroscope, or other motion-sensing sensors configured to detect a motion signal associated with the patient (e.g., movement of the patient's arm, wrist, or hand). In certain modalities, for example, the motion signal can be processed in tandem with the PVP waveform and used as an adaptive filter to remove motion components.Alternatively, a motion signal measured by one of these components can be processed and compared to a predetermined threshold value: if the signal exceeds the predetermined threshold value, it may indicate that the patient is moving too much for an accurate measurement; if the signal is below the predetermined threshold value, it may indicate that the patient is stable and an accurate measurement can be made. Such circuit elements 102, 104, and 106 are normally manufactured on a small fiberglass circuit board, such as the one shown in Figure 7, characterized by dimensions designed to fit inside a small connector (e.g., component 91 in Figure 1). Figures 9A–C illustrate how circuit board 62 and associated circuit elements 102, as shown in Figures 7A, 7B, and 8, respectively, amplify and generally enhance the analog versions of the PVP-AC waveform. More specifically, Figure 9A shows a time-dependent graph of the PVP-AC waveform measured at location 130 within circuit elements 102 corresponding to an initial analog amplification and filtering stage. As is clear from the figure, the signal-to-noise ratio of the PVP-AC waveform at this point is relatively weak, making it difficult (if not impossible) to detect any features corresponding to actual physiological components, such as a heartbeat or a breath-induced pulse.In contrast, after passing through three additional amplification / filtering stages: 1) differential amplification with an additional 10X gain; 2) filtering with a 2-pole 25 Hz low-pass filter, followed by a 2-pole 0.1 Hz high-pass filter, and then a 2-pole 15 Hz low-pass filter; 3) amplification with 50X gain—the signal is considerably improved. Figure 9B shows the time-dependent waveform measured further down the amplifier chain of the circuit at a second location 132: it exhibits a relatively high signal-to-noise ratio and clear pulses induced by the heartbeat (i.e., it shows a well-defined signal in the time domain corresponding to heart rate). Such a waveform, when processed in the frequency domain as described above, would produce clear features that enhance the IVDS's ability to detect events related to intravenous infiltration. It is important to note that, as described above, the analog signal processing shown in Figures 9A-C and the digitization of the PVP waveform are ideally performed as close as possible to the signal source, i.e., in the housing worn on the arm. This configuration minimizes the noise and attenuation caused by the signal propagating through a long, lossy cable (which is also susceptible to movement) to a remote amplification / filter circuit. Ultimately, this approach produces a time-dependent waveform with the highest possible signal-to-noise ratio, maximizing the accuracy with which IV infiltration and vital signs can be determined. 4. Blood Pressure Measurement Even after processing with the PVP conditioning circuit board, the measured PVP waveforms may exhibit low signal-to-noise ratios, thus hindering the extraction of the individual heartbeat-induced pulses required to estimate arterial blood pressure using the algorithm described herein. Regarding Figures 10A and 10B, in typical applications, heartbeat-induced pulses in time-dependent waveforms (e.g., PPG and IMP waveforms) are typically identified using algorithms that identify periodic peaks. However, such peaks can be difficult to find when the signal-to-noise ratio of the waveform is low, as shown in Figure 10A. In this case, the algorithm identifies multiple peaks (indicated by open circles) for each heartbeat-induced pulse. iviA / a / ¿u¿ó / uu io ι i Most of these are incorrect, since only a single peak should be identified for each pulse induced by the heartbeat. Figure 10B shows the results of an alternative pulse algorithm described in the following reference, the content of which is incorporated herein by reference: Scholkmann F, Boss J, Wolf M.; “An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals,” Algorithms. 2012; 5(4):588-603. In this approach, each point in the time-dependent, pulse-containing waveform is compared to its neighbors. The algorithm iteratively increases the size of a time-dependent window while testing for a peak. It tracks the locations that pass the test for each window, and the width of the window sizes can be optimized based on the signal period (e.g., the pulse frequency). The algorithm confirms true peaks if they exist across all window sizes.Figure 10B shows the results of this pulse algorithm, referred to herein as the IVDS pulse algorithm, when applied to the same PVP waveform shown in Figure 10A. In contrast to the conventional algorithm used to process the waveform in Figure 10A, the IVDS pulse algorithm correctly and uniquely identifies each pulse induced by the heartbeat, as shown by the open circles in Figure 10B. Ideally, due to the typically low signal-to-noise ratio of PVP waveforms, the IVDS described herein uses the IVDS pulse algorithm as described in the reference mentioned above and demonstrated with the data shown in Figure 10B. Typically, this algorithm is implemented using computer code such as C or C++ on a microprocessor within the IVDS electronic module. Figures 11A-D show time-dependent PVP and arterial PA waveforms measured and processed with the IVDS and, in doing so, demonstrate the following key points: Point 1: The IVDS pulse algorithm can effectively process when PVP and arterial PA waveforms depend on time to identify pulses. Point 2: There is a strong agreement between the changes in time-dependent arterial and PVP waveforms, as measured and processed with the system described herein. Point 3: A patient's respiratory events modulate PVP waveforms in a significantly more pronounced manner compared to arterial PA waveforms. With regard to Point 1, the graphs in Figures 11A and 11C show, respectively, time-dependent PVP and arterial PA waveforms processed with the IVDS pulse algorithm. The open circles near the tops of each waveform show heartbeat-induced pulses identified by the algorithm. Figures 11B and 11D, showing portions of the waveforms indicated by dashed circles 170 and 172, respectively, display both the waveforms and the pulses in greater detail.As is clear from these data, the IVDS pulse algorithm successfully identifies heartbeat-induced pulses in both arterial PA and PVP waveforms; this is particularly challenging for the PVP waveforms shown in Figures 11C and 11D, since signals originating from the subject's venous system have considerably less defined heartbeat-induced pulses compared to those originating from the subject's arterial system. Regarding Points 2 and 3, comparing the graphs shown in Figures 11A and 11B with those in 11C and 11D indicates a high degree of agreement between the time-dependent pulse waveforms of pulmonary vascular resistance (PVP) and arterial blood pressure (ABP). However, the PVP waveforms are significantly more affected by the subject's respiration. This is clearly shown in dashed boxes 173 and 174 in Figures 11B and 11D, respectively. In Figure 11B, which shows the ABP waveform, the overall pressure is only slightly modulated by respiration. Therefore, the ratio between pulses induced by heartbeats (indicated by the 'o' marks) and modulation by respiration is large. Conversely, in Figure 11D, which shows the PVP waveform, the overall pressure is strongly modulated by respiration and the pulses induced by heartbeats are relatively weak.This means that the proportion of pulses induced by heartbeats (indicated by the 'x' markers) and respiratory modulation is small. Even with respiratory modulation, there is strong agreement between the two waveforms, indicating that an algorithm that digitally removes artifacts due to respiration can improve the agreement and thus proportionally improve the accuracy of the blood pressure calculated from the pulse waveform. Figures 12A-E further illustrate these points. Each figure shows two graphs corresponding to different porcine subjects participating in a clinical study: 1) a time-dependent arterial blood pressure waveform measured over a relatively short time segment, along with the corresponding pulses generated using the IVDS pulse algorithm, shown with 'o' markers (upper graph); and 2) a time-dependent pulse pressure waveform measured over the same time segment with the corresponding pulses generated using the IVDS pulse algorithm, shown with 'x' markers (lower graph). Note that for these graphs, the x-axis (Time) is in samples, with a sampling rate of 50 samples / second. The data in these figures corroborate the three points mentioned above: in all cases, the IVDS pulse algorithm is effective in locating cardiac pulses, particularly in the relatively difficult PVP waveforms. There is a strong correlation between changes in arterial blood pressure and PVP waveforms. Furthermore, in all cases, both waveforms are consistently modulated by the subject's respiration, with the modulation being significantly more pronounced and resulting in relatively large changes in the PVP waveforms. Importantly, the concordance between the two waveforms persists even during periods when respiration-induced modulation is not present. For example, in Figures 12A and 12D, subjects exhibit somewhat prolonged periods of time when no respiration is present (in both figures, approximately 1.125-1.135x105 samples, or 20 seconds), but there is still agreement between the pressure variations in the two signals. While not linked to any particular theory, the relatively large modulation present in PVP waveforms compared to arterial PA waveforms, as shown in Figures 11 and 12, may be due to the established theory that the distensibility of a vein is 10 to 20 times greater than that of an artery (see, for example, “Cardiovascular Physiology Concepts,” by Richard E. Klabunde, Ph.D., https: / / www.cvDhvsioloqy.com / ). Referring to Figure 13A, distensibility is the ability of a blood vessel wall to passively expand and contract with changes in pressure. Typically, veins can accommodate large changes in blood volume with only a small change in pressure, meaning they have greater distensibility. The greater distensibility of veins is largely due to venous collapse, which occurs at pressures below 10 mmHg.At higher pressures and volumes, venous distensibility (the slope of the distensibility curve) is similar to arterial distensibility. There is no single distensibility curve for a blood vessel. For example, as shown in Figure 13B, contraction of vascular smooth muscle, which increases vascular tone, reduces vascular distensibility (dashed lines in the figure) and shifts the volume-pressure relationship downward. Conversely, smooth muscle relaxation increases distensibility and shifts the distensibility curve upward. This is particularly important in the venous vasculature for the regulation of venous pressure and cardiac preload. Smooth muscle contraction in arteries reduces their distensibility, thereby decreasing arterial blood volume and increasing blood pressure within the arterial system. Distensibility, as described above, represents the static distensibility generated by expanding a blood vessel by a known volume and measuring the pressure change at rest. Normally, the distensibility of a blood vessel (whether artery or vein) also depends on the rate at which the volume change occurs; that is, there is a dynamic component to distensibility. This is indicated in Figures 11 and 12 by the impact of respiration on arterial and venous pressure waveforms: respiratory events affect vascular distensibility in both arteries and veins, but due to the relatively low pressure within veins, respiration has a more pronounced impact on blood pressure in them. When respiration-induced modulation is removed from both the PVP and arterial BP waveforms, for example, using a digital filtering technique, the agreement between the two signals increases. For example, Figures 14A and 14B are time-dependent graphs of the pulses of these two waveforms (as opposed to the full-resolution waveforms that include all data points in addition to the pulses, as shown in Figures 11 and 12). Figure 14A shows the arterial BP pulses, indicated by 'O' markers, while Figure 14B shows the PVP pulses, indicated by 'x' markers. In all cases, the pulses were generated using the IVDS pulse algorithm, as described above. Both Figures 14A and 14B include a dark solid line indicating pressure variations where the respiratory artifact is digitally filtered out. Here, the filter used was a digital bandpass filter, with the filter limits consistent with the frequency at which breathing normally occurs (e.g., approximately 3 to 20 breaths / minute). As is clear from the figure, the solid line generally passes through the breath-modulated pulses and significantly illustrates the strong agreement in pressure variations for these signals when the breathing-related components are removed. In some applications, the filter used to remove breathing components may be something other than a bandpass filter. Other candidate filters include a waveform-based filter (e.g., CWT or DWT), an adaptive filter where breathing is measured using another technique (e.g., from the IMP waveform) and then used within a separate filter for PVP waveforms, a frequency-domain-based filter (e.g., one applied after the time-domain waveform is converted to a frequency-domain waveform using an FFT), or a simple smoothing algorithm. Other comparable digital filtering or digital signal processing techniques for removing or reducing signal artifacts due to breathing modulation are within the scope of the invention. The pulses in the PVP waveforms correspond to systolic pressure within the vein and typically have pressure values ​​in the range of 10 to 30 mmHg, while those of arterial BP correspond directly to systolic pressure and are relatively higher, for example, typically in the range of 70 to 150 mmHg. Furthermore, there does not appear to be a universal relationship between venous and arterial pressures that applies to all patients. This means that calibration is necessary to estimate arterial BP from PVP waveforms. Referring to Figure 15, a system for 'calibrating' a PVP waveform so that it can be used to estimate arterial BP values ​​(SIS, MAP, and DIA) features the IVDS 80 according to the invention attached to a patient's arm 23, as described in detail with reference to Figure 1. During the calibration period, which normally takes place at the beginning of a measurement, a blood pressure cuff 181 that performs an oscillometric BP measurement is connected to the patient's brachial region (e.g., biceps). The blood pressure cuff 181 includes a flexible cuff 180 that wraps around the biceps; it has an inflatable air chamber and is generally made of a nylon-like material with Velcro® patches used to temporarily secure it.A control module 182 controls the blood pressure cuff 181 and features a circuit board containing a microprocessor, a wireless Bluetooth® transceiver, a pressure sensor, a power circuit, and analog / digital signal conditioning electronics; an electronic pump; and a battery. To initiate a measurement, a physician (or indeed the patient 11) presses an on / off button 184 on the blood pressure cuff 181. This activates the pump within the control module 182, which inflates the bag inside the cuff, collects pressure signals from the patient's biceps, and generally performs a standard blood pressure measurement using oscillometry. This produces initial SIS, DIA, and MAP values. In addition, the pressure sensor within the blood pressure cuff 181 measures a time-dependent pressure waveform that indicates the pressure applied to the patient's brachial artery by the flexible cuff 180.Once measured, these parameters (SIS, DIA, and AP values, along with a time-dependent pressure waveform) are wirelessly transmitted via the Bluetooth® transceiver within the blood pressure cuff 181 to a paired Bluetooth® transceiver within the electronic module 94 attached by the housing 20 worn on the arm. More specifically, the microprocessor in the electronic module 94 receives and processes these parameters, along with other time-dependent waveforms measured by the IVDS 80, to determine a patient-specific calibration, as described in more detail below. The Bluetooth® communication between the 181 blood pressure cuff and the 94 electronic module in the IVDS 80, as indicated by arrow 188 in the figure, is a bidirectional connection: as described above, the 181 blood pressure cuff sends SIS, DIA, and MAP values ​​and a time-dependent pressure waveform to the IVDS 80, and this system processes this information to generate a patient-specific calibration, and can also send information (such as an acknowledgment, an error code, or an instruction to start a new calibration measurement) to the 181 blood pressure cuff. Patient-specific calibration is generally determined by collectively analyzing the time-dependent pressure waveform from the blood pressure cuff 181, along with time-dependent waveforms collected by the IVDS 80, e.g., IMP, temperature, PPG, and motion waveforms, and time-dependent AC-PVP and DC-PVP waveforms measured by the PVP conditioning circuit board 95. Similar techniques have been described in the following U.S. Patents, the contents of which are incorporated herein by reference: Banet et al., Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure, U.S. Patent 10,722,131; Banet et al., Handheld physiological sensor, U.S. Patent 10,206,600; McCombie et al., System for calibrating a PTT-based blood pressure measurement using arm height, US Patent 8,672,854; Banet et al., Cuffless system for measuring blood pressure, 7,179,228; y Banet et al., Blood-pressure monitoring device featuring a calibratlon-based analysis, 7,004,907. More specifically, to determine patient-specific calibration, multiple PVP and arterial BP values ​​can be collected and analyzed to determine patient-specific slopes, which relate changes in PVP to changes in SIS, DIA, and MAP. Patient-specific slopes can also be determined using predefined values ​​from a clinical study and then combining these measurements with biometric parameters (e.g., age, sex, height, weight) collected during the clinical study. In still other modalities, the patient-specific slope can be determined by detecting the change in PVP (measured with the PVP conditioning circuit board 95) with the change in pressure applied to the brachial artery (measured with the control module 182 within the blood pressure cuff 181).Here, blood pressure can be estimated from the variable pressure applied by the blood pressure cuff 181, and then correlated with the variable PVP measured during cuff inflation. This relationship can be used to estimate patient-specific calibration. Other calibration approaches, such as empirical methods based on the patient's biometric parameters, and as described in the patents mentioned above, are also within the scope of the invention. Once a measurement is complete, the IVDS 80 can wirelessly transmit numerical values ​​via a Bluetooth® interface, as indicated by arrow 189, to an external display, such as an infusion pump 192. This type of communication, for example, enables a closed-loop system where the infusion pump 192 administers fluids to the patient to affect their blood pressure, blood volume, and other physiological parameters, and the IVDS 80 determines whether the fluids are administered into the patient's venous system or infiltrate the underlying tissue, and also how the patient responds to the administered fluids. In other modes, the IVDS 80 sends information via a similar wireless interface to another remote display, such as a mobile phone, computer, tablet, television, hospital EMR, or other comparable display device. Figure 16 shows how a patient's arm height can influence the PVP waveform and, in particular, change both the signal baseline (which is readily apparent from the large changes in Figure 16) and the magnitude of each heartbeat-induced impedance pulse (a feature present upon close inspection of the data, but less evident in Figure 16). The graph in Figure 16 shows time-dependent PVP and motion waveforms (taken from the z-axis of the accelerometers) measured at four different arm positions, as shown in graphs 200a–d. During the first 60 seconds, the patient's arm points straight down, as shown in graph 200a, and the PVP waveform has an initial baseline of around 20 mmHg.During the next 60 seconds, the patient raises their arm approximately 45°, as shown in Figure 200b, causing the baseline of the PVP waveform to decrease by approximately 20 mmHg. This trend continues as the patient raises their arm to 90° (as shown in Figure 200c), and finally to 135° (as shown in Figure 200d). Figure 16 also shows how the motion signal measured by the accelerometer (in this case, along the z-axis) changes proportionally with arm height, indicating that this signal can be processed to estimate the actual arm height. The change in PVP signals with arm height and the ability to automatically characterize relative arm height with an accelerometer are important for several reasons. First, because both PVP and blood pressure change with a change in arm height in a continuous and well-defined manner, a process involving a systematic variation of arm height can be used to calibrate a PVP-based blood pressure measurement, as described above. Second, because PVP signals (both baseline and heartbeat-induced pulses) vary with arm height, an accurate blood pressure measurement based on them must account for arm height, as measured with an accelerometer. For the IVDS, the calculation of arm height from an accelerometer signal is preferably performed by generating a series of lookup tables beforehand, which have separate entries for both parameters, as is typical in a clinical trial involving subjects with different demographic characteristics (e.g., height, weight, BMI, sex, age). The lookup tables are preferably coded into the IVDS software during manufacturing. During an actual measurement, the accelerometer signals are measured and compared to the appropriate lookup table to estimate arm height. An algorithm based on the results shown in Figure 14 (respiratory modulation removal via digital filtering), Figure 15 (calibration with a cuff-based system), and Figure 16 (upper arm height measurement) can be used to estimate PVP arterial blood pressure. Figure 17 shows a flowchart outlining the main steps of the algorithm. The algorithm begins (step 270) with the measurement of PVP waveforms using an IVDS such as the one shown in Figures 1 and 15. Such a system, for example, would be deployed in a hospitalized or surgical patient connected to a conventional IV system. After the IVDS measures the PVP waveforms, it processes them with a rhythm selector, such as the IVDS pulse algorithm described earlier with reference to Figure 10, to determine a collection of points (i.e., 'vectors') of SIS / DIA values ​​(step 271).Using embedded computer code operating within the IVDS, the algorithm then filters SIS / DIA value vectors to remove respiration modulation using one of the digital signal processing techniques mentioned above, such as bandpass filtering, adaptive filtering, waveform filtering (e.g., CWT or DWT), or simple multipoint smoothing (step 272). Once filtered, the IVDS uses its internal multiaxis accelerometer to estimate changes in the vertical distance between the subject and the IV system, according to the approach described with respect to Figure 16 (step 276). The changes in vertical distance are then processed by the IVDS to adjust the SIS / DIA value vectors to represent these changes in vertical distance between the patient and the IV system (step 273).Once this is complete, the IVDS initiates a calibration measurement, as described above with reference to Figure 15, instructing the blood pressure cuff to measure SIS and DIA values ​​and a time-dependent pressure waveform (step 278). The algorithm uses these cuff-based system values ​​to effectively calibrate the measurement—that is, to determine the initial SIS and DIA values ​​and generate the patient-specific calibration (step 274). With this calibration and the PVP waveforms, the IVDS can estimate the follow-up SIS / DIA value (step 275). Figures 18 and 19 show the results of processing PVP data from five different porcine subjects using a version of the algorithm shown in Figure 17. The graphs in Figures 18A–E show time-dependent SIS values ​​taken from PVP (i.e., estimated SIS) and arterial BP waveforms (actual SIS). In each case, the agreement between estimated and actual SIS is good, even during periods of large and rapid blood pressure changes. Figure 19 shows a graph indicating the agreement between estimated and actual SIS values, taken from Figures 18A–E. Data points were selected every 30 minutes to generate this graph. From the pooled paired values ​​used to generate the graph, the overall bias was calculated to be 0.81 mmHg, and the standard deviation was 3.93 mmHg. The r value, indicating correlation, was 0.98, indicating excellent agreement, and the slope of the data points was 0.96, indicating a value close to unity and the overall absence of any systematic variation. Taken together, these data indicate the effectiveness of the blood pressure measurement described herein. 5. Measurement of Movement and Posture with the IVDS The same accelerometer used in the IVDS to estimate arm height can also detect a patient's movement and posture, for example, during a hospital stay. More importantly, it can be used to characterize periods of movement that might otherwise make the measurements described herein (IV infiltration and PVP-based PA) difficult or impossible due to motion-related artifacts. In short, the accelerometer can detect movement, which is useful in itself for characterizing a patient, while also indicating periods when the patient is relatively immobile and, ideally, a measurement can be taken. Figure 20, for example, shows time-dependent (from the accelerometer's z-axis) waveforms of PVP, IMP, temperature, and motion measured during the following events: arm flexion, twitching, arm elevation and lowering (45° and 90°), supine-to-sitting and sitting-to-supine transitions, walking, and the standing-to-supine transition. The dashed lines in the figure delineate each event as a function of time. Figure 19 indicates that each waveform is affected by motion to some extent. The IMP waveform, in particular, is composed of relatively weak signals and is most profoundly affected by motion; in particular activities involving large arm movements, such as walking, they impart a significant amount of noise to the waveform. In preferred modes, the microprocessor located in the IVDS electronic module operates an algorithm that continuously processes the signals from the accelerometer's three axes. By comparing this data with a predetermined lookup table or, alternatively, with first-principles models, the algorithm determines: 1) the type of movement the patient is experiencing; and 2) whether the movement is intense enough to affect PVP-based blood pressure measurements, as well as measurements of other vital signs, as described below. The IVDS reports a set of values ​​when the movement is such that the algorithm determines a measurement can be taken. In other measurements, using accelerometer data, the IVDS can determine events that are about to occur, such as a patient moving in a hospital bed and preparing to get out of bed. In these and other cases, the IVDS can wirelessly transmit an alarm or alert to a remote display, for example, an infusion pump, as shown in Figure 15. 6. Measurement of other vital signs and physiological parameters with the IVDS The same sensors described herein used to detect IV infiltration, in particular the IMP, temperature, and PVP conditioning circuit board used to process PVP signals, can perform a dual function and also measure waveforms produced by other vital signs, such as HR, HRV, RR, and TEMP. Furthermore, the IVDS can include a reflective optical system (typically arranged within the breathable, flexible polymer base (component 89) in Figure 1) that can be used to characterize IV infiltration using time-dependent changes in an optical signal. This same optical signal can simultaneously provide PR and SpO2 values.These measurements, when combined with the PVP-based BP measurement described herein, mean that the IVDS can potentially measure all five vital signs (HR, RR, TEMP, SpO2, and BP) that are typically used to characterize a patient. The electrodes (i.e., components 83 in Figure 1) detect signals that are used for bioimpedance (or, alternatively, bioreactance) measurement of the IVDS, producing a time-dependent IMP waveform that includes features related to heart rate (HR) and heart rate (FR). Here, one pair of electrodes on the polymer base of the IVDS injects a high-frequency (e.g., 20–100 kHz), low-amplitude (e.g., 10–1000 Ω) current into the patient's body. The current injected by the two electrodes is 180° out of phase. The other pair of electrodes measures a voltage that, with further processing, indicates the resistance (or impedance) encountered by the injected current. The voltage is related to the resistance (or impedance) through Ohm's Law.Typically, a bioimpedance circuit within the electronic module measures IMP waveforms, which are separated into an AC waveform exhibiting relatively high frequency characteristics (generally called Z(t)), and a DC waveform exhibiting relatively low frequency characteristics (typically called Z0). This technique for measuring Z(t) and Z0 is described in detail in the following pending patent applications, the contents of which are incorporated herein by reference: NECK-WORN PHYSIOLOGICAL MONITOR, U.S. Serial No. 62 / 049,279, filed September 11, 2014; NECKLACE-SHAPED PHYSIOLOGICAL MONITOR, U.S. Serial No. 14 / 184,616, filed February 19, 2014; and BODY-WORN SENSOR FOR CHARACTERIZING PATIENTS WITH HEART FAILURE, US Series No. 14 / 145,253, filed December 31, 2013, and PHYSIOLOGICAL MONITORING SYSTEM FEATURING FLOORMAT AND WIRED HANDHELD SENSOR. The physiological processes within a patient's arm modulate the iZ(t) and Z0 waveforms detected by the IVDS bioimpedance measurement system. Therefore, processing these waveforms can generate parameters that correspond to the physiological processes. For example, respiratory effort (i.e., breathing) affects Z(t) to impart a series of low-frequency oscillations (typically 5–30 oscillations / minute) to the waveform. The IVDS electronic module processes these oscillations to determine respiratory rate (RR). Blood is a good electrical conductor, and therefore, blood flow in the patient's arm manifests as cardiac pulses induced by the heartbeat in the iZ(t) waveform. These can be processed using established techniques to determine heart rate (HR) and heart rate variability (HRV). The physiological fluids in the arm also conduct the injected current. They can accumulate in this region (much like fluids accumulate to detect IV infiltration, although on a much slower timescale) and affect the impedance within the electrode's conduction pathway in a low-frequency manner (i.e., by changing slowly); therefore, they can be detected by processing the Z0 waveform. Typically, the Z0 waveform has an average value of approximately 10–50 ohms, where 10 ohms indicates a relatively low impedance and therefore high fluid content (e.g., the patient is wet), and 50 ohms indicates a relatively high impedance and therefore low fluid content (e.g., the patient is dry). Time-dependent changes in the average Z0 value can indicate that the patient's fluid level is increasing or decreasing.An increase in fluid level, for example, may indicate the onset of congestive heart failure or kidney failure. To measure optical signals, the IVDS may include a light source, for example, a dual-emitting LED operating in either a transmissive or reflective mode geometry, which generates red and infrared optical wavelengths in the I = 660 nm and l = 908 nm regions, and a photodetector (for example, a photodiode). These components measure PPG waveforms using both red and infrared radiation, as commonly known in the art, from the patient's arm or one of their fingers (for example, the thumb) near the IV site. The electronic module processes the waveforms to determine SpO2. Such measurement is described in more detail in the following pending patent applications, the contents of which are incorporated herein by reference: NECK-WORN PHYSIOLOGICAL MONITOR, U.S. Serial No. 62 / 049,279, filed September 11, 2014; NECKLACE-SHAPED PHYSIOLOGICAL MONITOR, US Series No.14 / 184,616, filed February 19, 2014; and BODY-WORN SENSOR FOR CHARACTERIZING PATIENTS WITH HEART FAILURE, U.S. Serial No. 14 / 145,253, filed December 31, 2013. In general, and as explained in greater detail in these incorporated references, during an SpO2 measurement, the digital system alternately illuminates the red and infrared LEDs within the dual-emitting LED. This process generates two distinct PPG waveforms. Using both digital and analog filters, the digital system extracts the AC and DC components from the red (RED(AC) and RED(DC)) and infrared (IR(AC) and IR(DC)) PPG waveforms, which the digital system then processes to determine SpO2, as described in the patent applications cited above.To enhance the optical signal, the IVDS may include a thin-film heating element, such as a Kapton® film with integrated electrical conductors arranged, for example, in a serpentine pattern. Typically, the temperature of the heating element is regulated in a closed-loop manner to between 41 and 42 °C, which has minimal effect on the underlying tissue and is considered safe by the U.S. Food and Drug Administration (FDA). Such an optical system and thin-film heating element is described in the following patent application, the contents of which are incorporated herein by reference: PATCH-BASED PHYSIOLOGICAL SENSOR U.S. Serial No. 16 / 044386, filed July 24, 2018. Figures 21A and 21B show graphs indicating IMP and PPG waveforms measured with a version of the IVDS shown in Figure 1 from a subject participating in a clinical study. Similar results were obtained from 13 other subjects participating in the study. Here, the IVDS was applied to each subject's arm proximal to a conventional IV site. Subjects were then instructed to breathe at a normal rate, then hold their breath, then breathe rapidly, and then hold their breath once more. Figure 21A shows an IMP waveform measured during this process. As is clear from the data, relatively small heartbeat-induced pulses are present throughout the measurement period. These are due to blood flow near the IV site.Furthermore (and somewhat surprisingly), the impedance signals measured from the arm were highly sensitive to respiratory rate. From these data, along with those collected from other subjects, heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) values ​​could be calculated with reasonable accuracy. It is important to note that the electrodes and circuitry used for these measurements are the same as those used to detect IV infiltration, described in detail above. Similarly, the optical sensor in the IVDS measured PPG waveforms using both RED and IR radiation. Typically, the waveform measured with IR radiation had a relatively high signal-to-noise ratio. From the PPG waveforms, PR and SpO2 values ​​were calculated, as described above. As with the electrodes described above, the optical system used for these measurements is the same as that used to detect IV infiltration, as described above. Furthermore, the PVP waveform can be processed to determine heart rate (HR), respiratory rate (RR), and other hemodynamic parameters. These measurements can be used to compensate for or enhance those obtained from IMP and PPG waveforms, as described with reference to Figure 21. For example, calculating the FFT of the PVP waveform produces a frequency-domain spectrum with peaks corresponding to HR (F1) and RR (F0). The features associated with F0 and F1 (e.g., their amplitude or energy) can be processed in various ways to estimate fluid-related parameters, such as pulmonary artery wedge pressure and / or pulmonary arterial pressure. Further energy processing then yields the appropriate fluid-related parameters. Examples of such processing are described in the following references, the content of which is incorporated herein by reference: 1) Hocking et al., “Peripheral venous waveform analysis for detecting hemorrhage and iatrogenic volume overload in a porcine model.”, Shock. 2016 Oct;46(4):447-52; 2) Sileshi et al., “Peripheral venous waveform analysis for detecting early hemorrhage: a pilot study.”, Intensive Care Med. 2015 Jun;41 (6):1147-8; 3) Miles et al., “Peripheral intravenous volume analysis (PIVA) for quantitating volume overload in patients hospitalized with acute decompensated heart failure - a pilot study ”, J Card Fail. 2018 Aug;24(8):525-532; y 4) Hocking et al., “Peripheral i.v. analysis (PIVA) of venous waveforms for volume assessment in patients undergoing haemodialysis ”, Br J Anaesth. 2017 Dec 1 ;119(6):1135-1140. In other modalities, the IVDS can collectively process hemodynamic parameters measured in the PVP waveform (e.g., wedge pressure and blood volume, which may be correlated with energies associated with F0, F1, or some combination thereof) with those measured by other sensors within the IVDS (e.g., BP, SpO2) to determine the patient's fluid status and effectively inform fluid administration during resuscitation (e.g., during periods of sepsis and / or fluid overload). Overall, by using information from both the PVP waveform and the IVDS, a clinician can better manage the patient by characterizing life-threatening conditions and helping to guide resuscitation. iviA / a / ¿u¿ó / uu io ι i As a more specific example, in IVDS modalities, the blood pressure (BP) and SpO2 values ​​measured by the IVDS can be combined with the volume status determined from the PVP waveform to estimate a patient's blood flow and perfusion. Knowledge of these parameters, in turn, can inform the estimation of the amount of fluid a clinician needs to administer after resuscitation. Similarly, the BP and SpO2 measured by the IVDS, along with the F0 / F1 energy ratio measured from the PVP waveform, each indicate a patient's perfusion level. These can also be combined into a mathematical index to better estimate this condition. These parameters or the index can then be measured while the patient undergoes a technique called passive leg raise, which is a test to assess the need for further fluid resuscitation in a critically ill person.Passive leg elevation involves raising a patient's legs (usually without their active participation), which allows gravity to draw blood from the legs to the central organs, thus increasing the circulatory volume available to the heart (commonly referred to as cardiac preload) by approximately 150–300 milliliters, depending on the amount of venous reservoir. If the parameters mentioned above or the index measured by the IVDS increase, this may indicate that leg elevation is effectively increasing perfusion to the patient's central organs, suggesting they will respond to fluids. Clinicians can perform a similar test by administering a fluid bolus to the patient via an IV system and then monitoring for increases or decreases in the parameters or the index measured by the IVDS. In some modalities, simple linear computational methods, combined with the results of clinical studies, can be used to develop models that collectively process the data generated by the IVDS. In other modalities, more sophisticated computational models, such as those involving artificial intelligence and / or machine learning, can be used for collective processing. 7. Other modalities In other modalities, time- and frequency-domain analyses of IMP, PPG, PVP, and motion waveforms can be used to distinguish respiratory events such as coughing and wheezing, and to measure respiratory tidal volumes. Specifically, respiratory tidal volumes are determined by integrating the area under a respiratory pulse on an IMP or BR waveform (as shown in Figure 21A) and then comparing it to a predetermined calibration. Such events can be combined with IVDS data to help predict patient decompensation. In other modalities, IVDS can use variations of the algorithms described above to determine vital signs and hemodynamic parameters.For example, to improve the signal-to-noise ratio of pulses within the IMP and PPG waveforms, the embedded firmware operating the patch sensor can employ a signal processing technique called pulse stacking. With pulse stacking, for instance, an average pulse is calculated from multiple (e.g., seven) consecutive pulses of the IMP waveform, which are delineated and then averaged together. The AC component derivative of the IMP waveform is then calculated over a window of seven samples as an ensemble average and used as described above. iviA / a / ¿u¿ó / uu io ι i Other embodiments are within the scope of the invention. For example, other components of the signals measured by the sensors within the IVDS can be analyzed, and in particular those used to measure PVP waveforms, to evaluate the patient. In some modalities, for example, arterial pulse pressure (PP) can be calculated from SIS and DIA as described above, and then analyzed to estimate changes in the patient's volume status, since a lower blood volume can reduce arterial pulse pressure and a higher blood volume can raise it. Furthermore, the venous system stores 60–70% of the blood volume and serves as a volume reservoir; it is a highly compliant, low-pressure system that can adapt to large volume changes with minimal pressure changes. Recent studies have shown that the amplitude and shape of the PVP waveform are sensitive to changes in intravascular volume.Changes in intravascular volume status in both humans and pigs caused changes in the PVP waveform before changes in BP, HR, and pulmonary artery diastolic pressure, suggesting that the PVP waveform is more sensitive to changes in intravascular volume than standard vital signs. The peak ventricular pressure (PVP) waveform of a venous segment during a given cardiac cycle is a direct result of the changes in blood volume occurring within that vein segment and the segment's distensibility. The distensibility of the vein segment is expected to be constant during a given cardiac cycle, and the corresponding distensibility values ​​over the duration of the cardiac cycle are determined by the inflow and outflow of blood to a given vein segment. Therefore, the change in PVP of a vein segment during a given cardiac cycle is a result of the change in blood volume within the vein segment that occurs during that cycle (i.e., the net effect on volume change resulting from blood flow into and out of the vein segment).Based on anatomical considerations and the results of cited studies based on physiological models, the changes in PVP waveforms detected in a peripheral vein segment are due to net changes in the segment's blood volume over the course of each cardiac cycle. Since the cyclic change in blood volume (and the corresponding cyclic change in pressure) in a vein segment results from the heart-induced cyclic change in flow to and from the vein segment, the change in blood volume in a vein segment results from the interaction of inflow pressure, outflow pressure, and intraluminal pressure. Therefore, analysis of these parameters of the pulmonary vascular resistance (PVR) waveform, as measured with IVDS, can provide information about a patient's hemodynamic status. When downstream resistance to venous return increases (for example, during atrial contraction or when the tricuspid valve closes), outflow pressure will rise. This causes a reduction (and eventual cessation once the valve of the proximal vein segment closes) in blood flow from a given vein segment into the adjacent downstream vein segment. Simultaneously, blood flow from the adjacent upstream segment into the vein segment will continue, but it too will decrease (and eventually cease once the valve of the proximal vein segment closes). ML / a / ZUZ J / UUl OI 1 distal vein segment). The net effect of these two actions will increase the blood volume within the vein segment (where the PVP sensor is located), distending its walls outward and increasing the intraluminal pressure (corresponding to the upward movement of the PVP waveform). The maximum intraluminal pressure within the vein segment will occur just before the point where that pressure is greater than the outlet pressure. In contrast, when downstream resistance to venous return decreases (for example, during atrial relaxation or when the tricuspid valve opens), outflow pressure will decrease. This causes an increase (and eventual cessation once the valve in the proximal vein segment closes) in blood flow leaving a given vein segment into the adjacent downstream vein segment. Simultaneously, blood flow leaving the upstream segment adjacent to the vein segment will begin to increase (and eventually cease once the valve in the distal vein segment closes). The net effect of these two actions will decrease the blood volume within the vein segment (where the PVP sensor is located), allowing its walls to recoil and the intraluminal pressure to decrease (corresponding to the downward stroke of the PVP waveform).The nadir of the intraluminal pressure of the vein segment will occur just before the point where the intraluminal pressure is less than the outlet pressure. In summary, the PVP waveform measured from a vein segment depends largely on: i) the right heart cycle, which alters atrial volume and, therefore, atrial pressure, which in turn determines venous return (i.e., venous outflow for a given peripheral vein segment); ii) the blood flow leaving the upstream vein segment adjacent to the downstream vein segment (i.e., venous inflow for a given peripheral vein segment); and iii) the distensibility of the venous wall in that vein segment, which can be affected by changes in venous tone. All of these factors combined define the amplitude and shape of the PVP waveform. Hypovolemia (e.g., blood loss, dehydration) has been shown to reduce the amplitude of PVP waveforms. Potential mechanisms for these findings include low arterial blood flow and the reduced blood pressure feeding the capillaries, which can lead to lower venous flow and pressure, resulting in slower and / or reduced venous filling. This, in turn, leads to a more gradual upward slope and / or a lower peak venous pressure. Initially, hypovolemia may decrease venous inflow (upstream) pressure more than venous outflow (downstream) pressure. This can lead to a more gradual downward slope of the PVP waveform due to a reduced pressure gradient for blood flow out of the vein segment. Vasoconstriction in response to hypovolemia could exacerbate this effect if the vasoconstriction affects arteries more than veins. Lower venous inflow pressure (upstream) can also lead to a more gradual upward slope of PVP if the slower rate of venous filling does not allow the segment to reach maximum potential intraluminal pressure / distension before the right atrium relaxes or the tricuspid valve opens, allowing the downstream veins to begin emptying. iviA / a / ¿u¿ó / uu io ι i As blood flows down from the peripheral venous compartment to the central venous compartment, the reduced downstream venous pressures can decrease the outflow pressure, thus reducing the maximum pressure change that can be achieved in the peripheral venous segment. Even without a change in absolute blood volume, decreased vasomotor tone mimics hypovolemia, with some hemodynamic changes similar to those of absolute hypovolemia (e.g., reduced central pressures due to the reduced stressed circulating volume generated by venous return, reduced mean arterial pressure, and potentially reduced cardiac output, which can lead to reduced venous inflow pressure and reduced venous intraluminal pressure). Lower venous tone can also lead to a more gradual upward and downward stroke of the venous pressure waveform, as more volume is required to increase pressure in the venous segment when the vessel diameter increases. Similarly, increased venous tone can lead to the opposite effects—a more pronounced upward and downward stroke of the venous pressure waveform. In summary, the shape and amplitude of the PVP waveform primarily reflect changes in the volume of the vein segment (where the PVP sensor is located) resulting from the interaction of inflow and outflow due to changes in downstream or central venous pressure / volume driven by the cyclical contraction-relaxation of the right side of the heart. The measured PVP waveform likely reflects the effective intravascular volume (the stressed volume, or the volume contributing to venous return and cardiac output) more accurately than the absolute blood volume. Other embodiments are within the scope of the invention. For example, signal processing techniques outside of (or in addition to) those described above can process PVP waveforms to isolate and improve the signal-to-noise ratio of the PVPAC and PVP-DC signal components, and in particular the PVP-AC components. One such signal processing technique is called wavelet decomposition and is related to the wavelet transform-based technique mentioned above. Wavelet decomposition algorithms approximate the PVP-AC signal with a collection of wavelets, each of which occurs at a different frequency (and usually octaves apart). The algorithm selects only wavelets of certain well-defined frequencies that are theoretically present in the desired signal and then recombines them to approximate the PVP-AC signal.Wavelet decomposition can often produce reconstructed AC PVP signals that accurately reflect cardiac and respiratory pulses, surpassing conventional signal processing techniques such as infinite impulse response (IIR) filters commonly used in bandpass and lowpass filters. Furthermore, wavelet decomposition is typically particularly effective at isolating AC PVP pulses when pressure fluctuations due to pump activity (i.e., pump noise) are present, exhibiting similar frequency components to AC PVP signals. In other modes, aimed at further increasing the signal-to-noise ratio of the signals With PVP-AC, the tubing used to connect the venous catheter to the pressure transducer can be optimized. For example, the hardness (e.g., stiffness) of the typically medical-grade tubing used in venous catheters is approximately 50-55 Shore A. Increasing this by approximately 25%, to match the hardness of tubing used for conventional arterial lines, increases the conductivity of the high-frequency PVP-AC pulses so they propagate effectively in the tubing with minimal loss and are more easily detected. In related modalities, the fluid column within the tubing can be pressurized (e.g., using an external, pressurized IV bag filled with saline solution connected to the tubing) to further increase the tubing's conductivity of the PVP-AC signals. One of the purposes of analyzing pulmonary vascular resistance (PVR) signals is to estimate a patient's volume status and, more specifically, how the patient will respond to fluids. More specifically, it can be helpful to determine where the patient falls on the Frank-Starling curve, which plots stroke volume (i.e., flow) against preload (i.e., blood volume). A patient who is relatively low on the curve will likely respond favorably to fluids, meaning their stroke volume may increase with increased volume, which is in turn facilitated by the increased fluids. Conversely, a patient who is relatively high on the curve may show only a small increase in flow when volume is increased. As such, an increase in volume can lead the patient to a detrimental congestive state, such as congestive heart failure. To this end, analysis of PVP-AC signals can generate a metric indicating how responsive the patient will be to infused fluids. This can include, for example, analyzing the cardiac and respiratory components of the PVP-AC signals, where the signals are first processed by wavelet decomposition as described above, and then the resulting signals are processed using an FFT or 11R filter-based approach to assess the relative magnitudes of both the cardiac and respiratory components. Typically, for example, a patient will respond to fluids (e.g., their SV will subsequently increase) when the magnitude of the cardiac component is relatively small compared to the respiratory component. By using such data (usually collected during a clinical study), one embodiment of the invention can provide a simple index indicating the patient's responsiveness to fluids.Such an index, for example, can be numerical (e.g., on a scale of 1 to 10), colorimetric (e.g., using 'red' to indicate a patient who needs fluids; 'green' to indicate a patient who is not in need of fluids), or something equivalent. In still other modalities, the index or other suitable metric for estimating the patient's fluid volume and / or responsiveness can be based on the mean value of the PVP signal (hereinafter, mean PVP), which is comparable to PVP-DC. Mean PVP indicates the average pressure of the PVP signal. It has the advantage of always being present in the patient and is relatively easy to process, mainly because it lacks oscillatory components related to the patient's cardiac or respiratory actions. Clinical work with the systems described herein indicates that mean PVP tracks a patient's responsiveness to fluids when assessed, for example, with clinical lower body negative pressure (LBNP) protocols.LBNP is an experimental maneuver that serves as a surrogate for hemorrhage. During LBNP, a subject's lower extremities are exposed to a systematically changing vacuum. This process draws fluids from the subject's torso in a manner similar to hemorrhage. When the vacuum is released, blood and other fluids return to the subject's torso; this is analogous to transfusing blood back into a patient. Using the systems described herein, a striking result of LBNP maneuvers applied to healthy subjects was that the mean PVP, along with the cardiac component of PVP-AC, systematically increased with increasing LBNP vacuum, and then rapidly returned to normal values ​​once the vacuum was released.Therefore, an index that includes the mean PVP by itself, or alternatively combined with components extracted from PVP-AC, can be used according to the invention to provide an index indicating the patient's responsiveness to fluids. In yet another aspect of the invention, a signal quality index (hereinafter, SQI) can be used with the parameters described above (e.g., PVP-AC and the signal components therein; mean PVP) to generate a comparable index. SQI is a metric that typically indicates the prevalence of a cardiac component in the PVP-AC signal: a low SQI indicates low amounts of a cardiac component, while a high SQI indicates high amounts of a cardiac component. Therefore, low SQI values ​​generally indicate that a patient needs fluids, while high SQI values ​​generally indicate a patient with adequate fluids. In still other embodiments of the invention, the PVP monitoring components described herein can be coupled with other sensors worn by the patient. For example, the patient can wear a dressing or adhesive wrap that holds the venous catheter in place and simultaneously monitors the degree to which IV fluids or medications are leaking out of the vein and into the third space near the venipuncture site. The signals measured by the dressing can be used to further process the PVP-AC signals, as described herein. Conversely, the presence of PVP-AC signals indicates that a venous catheter is correctly positioned in a patient's vein and can therefore be used with signals generated by the dressing to determine whether fluids and / or medications administered to the patient are leaking into their third space. These and other embodiments of the invention are considered to be within the scope of the following claims.

Claims

1. A system for determining a patient's arterial blood pressure value, comprising: a catheter configured to be inserted into the patient's venous system; a pressure sensor connected to the catheter and configured to measure physiological signals indicating a pressure in the patient's venous system; and a processing system configured to: i) receive the physiological signals from the pressure sensor; and ii) process the physiological signals with an algorithm to determine the arterial blood pressure value.

2. The system according to claim 1, wherein the processing system is further configured to operate an algorithm that filters the respiratory components of the physiological signals to determine the arterial blood pressure value.

3. The system according to claim 2, wherein the algorithm is further configured to operate a bandpass filter to filter the respiratory components of the physiological signals.

4. The system according to claim 2, wherein the algorithm is further configured to operate a wavelet-based filter to filter the respiratory components of the physiological signals.

5. The system according to claim 1, wherein the processing system is enclosed by an enclosure that is configured to attach directly to the patient.

6. The system according to claim 1, wherein the processing system further comprises a motion detection sensor.

7. The system according to claim 6, wherein the motion detection sensor is an accelerometer and a gyroscope.

8. The system according to claim 6, wherein the processing system is further configured to receive signals from the motion detection sensor and process them to determine the degree of patient movement.

9. The system according to claim 8, wherein the processing system is further configured to collectively process the degree of patient movement and physiological signals to determine the value of arterial blood pressure.

10. The system according to claim 6, wherein the processing system is further configured to receive signals from the motion detection sensor and process them to determine a relative height associated with a body part associated with the patient.

11. The system according to claim 10, wherein the body part is the patient's arm.

12. The system according to claim 10, wherein the processing system is further configured to collectively process the relative height associated with the body part associated with the patient and the physiological signals to determine the arterial blood pressure value.

13. The system according to claim 1, wherein the processing system is further configured to receive a blood pressure calibration value from an external source.

14. The system according to claim 13, wherein the processing system is further configured to process the blood pressure calibration value with physiological signals to determine the arterial blood pressure value.

15. The system according to claim 14, wherein the external source is a blood pressure cuff and an arterial catheter.

16. The system according to claim 14, wherein the processing system is further configured to process a patient-specific ratio of venous blood pressure to arterial blood pressure, together with the blood pressure calibration value and physiological signals, to determine the arterial blood pressure value.

17. The system according to claim 16, wherein the processing system is further configured to process physiological signals to determine the patient-specific relationship between venous blood pressure and arterial blood pressure.

18. The system according to claim 16, wherein the processing system is further configured to process biometric information corresponding to the patient to determine the patient-specific relationship between venous blood pressure and arterial blood pressure.

19. A system for determining a patient's arterial blood pressure value, comprising: a catheter configured to be inserted into the patient's venous system; a pressure sensor connected to the catheter and configured to measure physiological signals indicating pressure in the patient's venous system; a motion sensor configured to measure motion signals; and a processing system configured to: i) receive physiological signals from the pressure sensor; ii) receive motion signals from the motion sensor; iii) process the motion signals by comparing them to a predetermined threshold value to determine when the patient has a relatively low degree of movement; and iv) process the physiological signals to determine the arterial blood pressure value.

20. A system for determining a patient's arterial blood pressure value, comprising: a catheter configured for insertion into the patient's venous system; a pressure sensor connected to the catheter and configured to measure physiological signals indicating pressure in the patient's venous system; a motion sensor configured to measure motion signals; and a processing system configured to: i) receive physiological signals from the pressure sensor; ii) receive motion signals from the motion sensor; iii) process the motion signals to determine a relative height between a body part associated with the patient and an infusion system; and iv) process the physiological signals and the relative height to determine the arterial blood pressure value.

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