PATIENT MONITORING SYSTEM

Abstract

The invention provides an IV system for patient monitoring, which is placed in the patient's body. The IV system includes: 1) a catheter that is inserted into the patient's venous system; 2) a pressure sensor connected to the catheter that quantifies physiological signals indicating pressure in the patient's venous system; 3) a motion sensor that quantifies motion signals; and 4) a processing system that: i) receives physiological signals from the pressure sensor; ii) receives motion signals from the motion sensor; iii) processes 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) processes the physiological signals to determine a physiological parameter when the processing system determines that the motion signals are below the predetermined threshold value.

Description

PATIENT MONITORING SYSTEM Claim of Priority and Cross-Reference to Related Applications This application claims priority and benefit from U.S. Provisional Patent Application No. 63 / 043,494, entitled PATIENT-MONITORING SYSTEM, filed June 24, 2020, the full content of which is incorporated herein by reference. Field of Invention The invention described herein relates to systems for the administration of drugs and drug fluids, and systems for monitoring patients in, for example, hospitals and medical clinics. Background of the Invention 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 common usage. 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; such claim term is not intended to be limited to that single meaning. Finally, unless a claim element is defined by reciting the word “means” and a function without consideration of any structure, the scope of any claim element is not intended to be interpreted upon application of 35 U.S.C. § 112(f). Appropriate care for hospitalized patients generally requires: 1) administration of medications and fluids using intravenous catheters (hereafter “IV”) and infusion pumps; and 2) monitoring of vital signs and hemodynamic parameters with patient monitors. Typically, IV catheters are inserted into veins in the patient’s hands or arms, and patient monitors are connected to sensors or electrodes worn (or inserted) on the patient’s body. Conventional patient monitors typically quantify 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 commonly clipped to the patient's fingers or earlobes.These sensors can calculate blood oxygen levels (hereafter “SpOz”) and pulse rate (hereafter “PR”) from these PPG waveforms. More advanced monitors can also quantify blood pressure (hereafter “BP”), specifically systolic (hereafter “SYS”), diastolic (hereafter “DIA”), and mean arterial pressure (hereafter “MAP”), typically using cuff-based techniques called oscillometry or pressure-sensitive catheters inserted into a patient’s arterial system called arterial lines. Digital stethoscopes, which can be either wearable or body-worn, can measure phonocardiogram (hereafter “PCG”) waveforms that indicate heart sounds and murmurs. Some patient monitors are entirely body-worn. These typically take the form of patches that measure ECG, HR, HRV, and, in some cases, RR. Such patches may also include accelerometers that quantify 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 or, alternatively, for outpatient and home use. The patches are generally worn for relatively short periods of time (e.g., from a few days to several weeks).They are usually wireless, and they typically include technologies such as Bluetooth® transceivers to transmit information in the short term to a secondary gateway device, which usually includes a cellular radio or Wi-Fi to transmit the information to a cloud-based system. Even the most 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 or pulmonary artery catheter. To quantify these parameters, the sensors are placed in the patient's left 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 (typically deployed on the patient's chest, legs, and / or neck) to measure impedance plethysmogram (IPG) and / or bioreactance (BR) waveforms. Analysis of the IPG and BR waveforms yields SV, CO, and thoracic impedance, which is a surrogate for fluids in the patient's chest (FLUIDS). It is worth noting that IPG and BR waveforms generally have similar shapes and are detected using similar measurement techniques and are therefore used interchangeably herein. Devices that measure SV, CO, and FLUIDS can establish a patient's blood volume, fluid responsiveness, and, in some cases, related metrics such as central venous pressure (hereafter “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 disadvantage and carries a high risk of infection. Additionally, CVP measurements may be slower 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 in peripheral blood vessels can reduce the effect of fluid loss in the central system, temporarily masking blood loss in conventional CVP measurements. This masking can lead to a delay in recognizing and treating the patient's condition, thus worsening outcomes. QnfrQ ίη / ΖΖΠΖ / Ε / ΥΙΛΙ To address these and other deficiencies, a measurement technique called peripheral intravenous waveform analysis (hereinafter “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 comprising pressure transducers that receive signals from internal catheters inserted into a patient’s venous system and are connected via cables to remote electronic components that process signals generated from these catheters (hereinafter “PIVA sensor”).PIVA sensors quantify time-dependent waveforms that indicate peripheral venous pressure (hereafter “PVP”) using existing IV lines, which typically include IV tubing connected to an infusion pump or saline drip. Measurements made with PIVA sensors typically involve a mathematical transformation of the PVP waveforms into the frequency domain, performed remotely using a methodology called Fast Fourier Transform (hereafter “FFT”). Analysis of a frequency-domain spectrum generated with an FFT can provide an RR frequency (hereafter “F0”) and an HR frequency (hereafter “F1”) that indicate the patient’s HR and RR, respectively.A more detailed 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. Further processing of these energies yields an indication of a patient's blood volume status. Such quantifications 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 iodogenic 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. ago. 2018;24(8):525-532; y 4) Hocking et aL, Peripheral i.v. analysis (PIVA) of venous waveforms for volume assessment in patients undergoing hemodialysis.”, Br J Anaesth. dic. 20171; 119(6): 1135-1140. Unfortunately, during commonly used PIVA sensor measurements, the PVP waveforms induced by HR and RR events (typically 5–20 mmHg) are much weaker than their arterial pressure counterparts (typically 60–150 mmHg). This means that the magnitudes of the corresponding signals in the time-dependent PVP waveforms measured by conventional pressure transducers are often very weak (e.g., typically 5–50 dV). Furthermore, PVP waveforms are generally amplified, conditioned, digitized, and ultimately processed by electronic systems located remotely 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 a primary frequency are obscured by 'harmonics'. QnfrQ Ln / Zznz / E / YIAI (i.e., an integer multiple 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. Brief Description of the Invention In light of the above, it would be beneficial to improve a conventional PIVA sensor to overcome the historical problems related to weak and noisy PVP waveforms and inadequate detection of F0 and F1. Such a system could improve how patients are monitored in hospitals and medical clinics.To address these and other deficiencies, an improved and augmented PIVA sensor (hereinafter, the “PIVA sensor”) is described herein, comprising: 1) a circuit board located very close to an internal venous catheter that amplifies, filters, and digitizes PVP waveforms immediately after they are detected by a pressure sensor (e.g., directly on the patient's body); and 2) a physiological sensor worn on the chest (hereinafter, the “patch sensor”) that makes accurate and independent measurements of vital signs, including HR and RR, which can help locate F0 and F1, and then processes these characteristics to determine their corresponding energies. A PIVA sensor according to the invention may include one or both of these improvements.Additionally, according to the invention, iPIVA sensor measurements can be coupled with independent measurements of hemodynamic parameters, such as SV, CO, and FLUIDS (which can be obtained using the patch sensor or a comparable patient monitor), to provide a better understanding of the patient's fluid status. Ultimately, combining these technologies—an iPIVA sensor with a novel signal conditioning circuit board combined with a complementary patch sensor that measures vital signs and hemodynamic parameters—can improve how patients are monitored and resuscitated in hospitals and medical clinics. The iPIVA sensor described herein is designed to operate with a conventional IV system and connects to the patient via an internal catheter; both are standard equipment. The catheter includes a housing, worn near or on the patient's body, typically in their arm or hand, which encloses a signal conditioning circuit board. This circuit board features complex circuitry that amplifies, filters, and digitizes the analog PVP waveforms. The circuit board may also include components for processing and storing the digitized signals, measuring motion (e.g., an accelerometer and / or gyroscope), and transmitting information wirelessly (e.g., a Bluetooth® transmitter).In this way, the circuit board can be integrated with a remote processor (e.g., server, gateway, tablet, smartphone, computer, infusion pump, or some combination of these) that can collectively analyze the PVP waveforms and supplementary information from the patch sensor. The iPIVA sensor described herein simplifies traditional measurements of vital signs and hemodynamic parameters, which can involve multiple devices and take several minutes to perform. The remote processor, which wirelessly couples with both the iPIVA sensor and the patch sensor, can be further integrated with existing hospital infrastructure and reporting systems, such as a hospital electronic health record system (herein Ln / zznz / E / YiAi “EMR”). This system can alert and notify caregivers about changes in a patient's condition, allowing them to intervene. The patch sensor measures vital signs such as heart rate (HR), heart rate variability (HRV), respiratory rate (RR), SpO2, temperature (TEMP), and blood pressure (BP), along with complex hemodynamic parameters such as stroke volume (SV), carbon monoxide (CO), and fluids. Blood pressure (BP) measurement is typically cuffless and calibrated using a cuff-based device, such as an oscillometric one. The patch sensor is generally a wearable device that adheres to a patient's chest and continuously and non-invasively measures the aforementioned parameters. The chest is an ideal location for such measurements in hospitalized patients: it is generally easily accessible, and a sensor placed there is usually discreet, comfortable, and easily removed from the hands (which typically experience relatively large amounts of movement).Because the patch sensor is small and therefore considerably less noticeable and bothersome than several other patient monitoring devices, emotional discomfort from wearing it can be reduced, thereby encouraging long-term compliance, healing, and overall patient well-being. Alternatively, instead of the patch sensor, the system that provides independent measurements of HR, RR, and hemodynamic parameters can be a conventional hemodynamic or vital signs monitor, such as the Starling™ SV patient monitor manufactured by Cheetah Medical, based in Newton Center, MA, USA. The patch sensor may also include a motion-detecting accelerometer and gyroscope, from which it can determine motion-related parameters such as posture, degree of movement, activity level, breathing-induced chest heave, and falls. These parameters could determine, for example, a patient's posture or movement during a hospital stay. The patch sensor can operate additional algorithms that process the motion-related parameters, allowing it to measure vital signs and hemodynamic parameters only when movement is minimized or below a predetermined threshold, thus reducing artifacts. Furthermore, the patch sensor estimates motion-related parameters, such as posture, to improve the accuracy of calculations for vital signs and hemodynamic parameters. Disposable electrodes on the underside of the patch sensor secure it to the patient's body without the need for cumbersome wires. In some models, these electrodes are easily attached to (and detached from) the sensor via magnets, allowing the sensor to easily return to its proper position if removed. The patch sensor is typically lightweight, weighing approximately 20 grams. It is powered by a Li-ion battery that can be recharged with a standard cable or wirelessly. Taking the foregoing into account, in one aspect, the invention provides an IV system for monitoring a patient that is positioned on the patient's body. The IV system includes: 1) a catheter that is inserted into the patient's venous system; 2) a pressure sensor connected to the catheter that quantifies the physiological signals indicating pressure in the patient's venous system; 3) a motion sensor that quantifies the motion signals; and 4) a processing system that: i) receives the physiological signals from the pressure sensor; ii) receives the motion signals from the motion sensor; and iii) processes the signals. QnfrQ Ln / Zznz / E / YIAI of movement by comparing them with a predetermined threshold value to determine when the patient has a relatively low degree of movement; and iv) processes the physiological signals to determine a physiological parameter when the processing system determines that the movement signals are below the predetermined threshold value. In another aspect, the motion sensor is used to measure the patient's posture, as opposed to their movement, and the processing system determines the physiological parameter when the patient is in a predetermined posture. In another aspect, the invention provides an IV system for monitoring a patient that includes: 1) a catheter that is 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; 3) a motion sensor that measures motion signals; and 4) a processing system that only transmits physiological signals, or parameters calculated from these signals, when the motion signals fall below a predetermined threshold. In these modalities, the motion sensor is an accelerometer (e.g., a 3-axis accelerometer) and / or a gyroscope. In these modalities, the processing system calculates a motion vector by analyzing a motion signal corresponding to each axis of the 3-axis accelerometer. The default motion threshold used to determine whether the patient's movement is too severe for accurate measurement typically corresponds to a vector magnitude of 0.1 G. In other modalities, the processing system compares the motion vector to a predefined lookup table to determine the patient's posture. In other modalities, the processing system digitally filters the signals (for example, with a digital high-pass filter) to generate a filtered signal. It then processes the filtered signal to determine the patient's heart and respiratory rates. In some modalities, the processing system further processes the signal components indicating the patient's heart and respiratory rates to determine a physiological parameter (for example, pulmonary artery 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 frequency domain signal before determining the physiological parameter. The transformation method is usually a continuous waveform transform (FFT) or a discrete waveform transform. In another aspect, the invention provides a system for monitoring a patient while simultaneously administering IV fluids. The system comprises a housing positioned on the patient's body. The housing includes a catheter that is inserted into the patient's venous system to deliver the IV fluids, and a pressure sensor connected to the housing that measures time-dependent pressure signals indicating pressure in the patient's venous system. The housing also includes a circuit system connected to the pressure sensor that receives the time-dependent signals it generates. The circuit system comprises: i) a differential amplifier that amplifies the pressure signals Ln / zznz / E / YiAi time-dependent to generate an amplified signal; ii) a low-pass filter that filters the amplified signal to generate a filtered signal, and iii) a secondary amplifier system that amplifies the filtered signal to generate a twice-amplified signal. In these configurations, the differential amplifier, low-pass filter, and secondary amplifier can be placed in any order within a circuit that differs from the one described above. In another aspect, the system also includes operating computer code for the processing system, which analyzes the twice-amplified signal to estimate a vital sign (e.g., HR, RR) corresponding to the patient. And in yet another aspect, the system also includes a wireless transmitter that transmits a digital representation of the vital sign to a remote receiver, and a power source that supplies power to the pressure sensor, circuitry, processing system, and wireless transmitter. In these modalities, the IV system includes a housing that completely encloses the circuit system and pressure sensor, and is attached to the catheter. The catheter, for example, can be used in the patient's hand or arm. In these modalities, the differential amplifier has a gain of at least 10X. The low-pass filter typically separates a component of the amplified signal containing heart rate and respiratory rate components. The low-pass filter usually includes circuit components that generate a filter cutoff value between 10 and 30 Hz. In other modalities, the circuit system additionally includes a high-pass filter that receives the twice-amplified signals and, in response, generates a twice-filtered signal. In this case, the high-pass filter usually includes circuit components that generate a filter cutoff value 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 value between 10 and 30 Hz. In other configurations, the circuit system additionally includes a motion sensor, such as an accelerometer or gyroscope. In other configurations, the circuit system additionally includes a wireless transmitter, such as a Bluetooth®, Wi-Fi, or cellular transmitter. In other configurations, the circuit system additionally includes a microprocessor that runs an algorithm to process the twice-amplified signal, or a signal derived from it. In still other configurations, the circuit system additionally includes a flash memory system that stores a digital representation of the twice-amplified signal or a signal derived from it. In another aspect, the invention provides a patient monitoring system that includes a physiological sensor, connected to the patient, which has a bioimpedance and / or bioreactance sensing element that measures a first set of parameters indicating the patient's fluid status. The system also includes an IV system comprising: 1) a catheter that is inserted into the patient's venous system; 2) a pressure sensor that receives fluid from the catheter and, in response, measures a waveform that Ln / zznz / E / YiAi indicates a pressure in the patient's venous system; and 3) a first processing system that receives the waveform and processes it, or new signals derived from it, to estimate a second set of parameters indicating the patient's fluid status. A second processing system then receives the first and second sets of parameters, or a new parameter derived from them, and processes them collectively to estimate a physiological parameter of the patient. In another aspect, the invention provides a similar system, but uses only the physiological sensor in the patient. It includes the bioimpedance and / or bioreactance detection element and the first processing system. In yet another aspect, the invention provides a system for monitoring a patient that includes: 1) a bioimpedance and / or bioreactance detection element connected to the patient that measures a first time-dependent waveform; 2) an IV system inserted into the patient's venous system that has a pressure sensor that measures a second time-dependent waveform; and 3) a processing system that analyzes parameters calculated from the first and second waveforms and processes them collectively to estimate a physiological parameter of the patient. In some modalities, the second processing system is selected from a group consisting of a computer, a tablet, and a mobile phone. This system can operate an algorithm that compares the first set of parameters with the second set of parameters to estimate the physiological parameter. In other modalities, the physiological sensor includes a first wireless transmitter, the IV system includes a second wireless transmitter, and the second processing system includes a third wireless transmitter. Here, the third wireless transmitter can communicate wirelessly with both the first and second wireless transmitters. In other modalities, the first set of parameters indicating the patient's fluid status is selected from a group that includes BP, SpO2, SV, stroke index, CO, cardiac index, thoracic impedance, FLUIDS, intercellular fluids, and extracellular fluids. In 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 determined from these. The second processing system can operate a linear mathematical model to collectively process the first and second sets of parameters. Alternatively, it can operate an artificial intelligence-based algorithm to collectively process the first and second sets of parameters. In these modalities, the physiological parameter estimated by the second processing system indicates the patient's fluid status. For example, the estimated physiological parameter could be the patient's blood volume, pulmonary artery wedge pressure, or pulmonary arterial pressure. In yet another aspect, the invention provides a system for monitoring a patient, comprising: 1) a physiological sensor connected to the patient and having sensing elements that measure a first set of signals indicating the patient's physiology; 2) an IV system comprising: i) a catheter inserted into the patient's venous system; and ii) a pressure sensor that detects fluids in the catheter and, in response, measures a second set of signals indicating a pressure in the patient's venous system; and 3) a processing system that receives the first and second sets of signals and processes them Ln / zznz / E / YiAi collectively, or new signals derived from them, to estimate a physiological parameter that indicates the patient's condition. In another aspect, the invention provides a similar system, only that all the elements—the physiological sensor, the pressure sensor, and the processing system—are used in the patient's body. And in yet another aspect, the invention provides a system for monitoring a patient comprising: 1) a physiological sensor worn on the patient's body with sensing elements that measure heart rate and / or respiratory rate; 2) a catheter that is inserted into the patient's venous system and collects fluid; 3) a pressure sensor connected to the catheter that detects the fluid and, in response, measures the signals indicating pressure in the patient's venous system; and 4) a processing system that receives the heart rate and / or respiratory rate value from the physiological sensor and collectively processes this value and the signals indicating pressure in the patient's venous system, or new signals derived from these, to estimate a physiological parameter indicating the patient's condition. In these modalities, the physiological sensor measures an ECG waveform and then processes this to determine a heart rate (HR) value. The physiological sensor can also measure an intraperitoneal diastolic (IPG) or bradycardia (BR) waveform and then process this to determine an relative risk (RR) value. In these modalities, both HR and RR represent the 'first set of signals', as used herein. In the modalities, the pressure sensor measures a time-dependent pressure waveform that indicates the pressure in the patient's venous system; this represents the 'second set of signals', as used herein. The processing system can then be configured to process the time-dependent waveform with an algorithm (e.g., an algorithm to perform an FFT, a continuous wavelet transform, or a discrete wavelet transform) to generate a frequency-domain spectrum. In one modality, the processing system then collectively processes the HR value and the frequency-domain spectrum to determine a feature in the frequency-domain spectrum corresponding to HR (i.e., F1); it then processes F1 or a parameter estimated from it (e.g., its amplitude or corresponding energy, as described herein) to estimate the physiological parameter that indicates the patient's condition.In one related modality, the processing system collectively processes the RR value and the frequency domain spectrum to determine a feature in the frequency domain spectrum corresponding to RR (i.e., F0); it then processes F0 or a parameter estimated from it (e.g., its amplitude or corresponding energy, as described herein) to estimate the physiological parameter that indicates the patient's condition. In yet another modality, both F0 and F1, or parameters derived from them, are collectively processed to estimate the physiological parameter that indicates the patient's condition. This parameter may be, for example, wedge pressure, central venous pressure, pulmonary arterial pressure, blood volume, fluid volume, or a related value. In another aspect, the invention provides an IV system for patient monitoring, which is placed in the patient's body. The system comprises: 1) a catheter that is inserted into the patient's venous system; 2) a pressure sensor connected to the catheter that measures signals indicating pressure in the patient's venous system; and 3) a processing system that receives the signals from the pressure sensor and processes them to measure a physiological parameter. In another aspect, the invention provides an IV system for patient monitoring, which is placed in the patient's body. The system comprises: 1) a catheter that is inserted into the patient's venous system; 2) a pressure sensor connected to the catheter that measures signals indicating pressure in the patient's venous system; and 3) a processing system that receives the signals from the pressure sensor and processes them to determine the signal components that indicate (or both) the patient's heart rate and respiratory rate. In yet another aspect, the invention provides a patient monitoring system that is positioned on the patient's body. The system comprises: 1) a catheter that is inserted into the patient's venous system and collects fluid; 2) a pressure sensor connected to the catheter that detects the fluid and, in response, measures the signals indicating pressure in the patient's venous system; and 3) a processing system that receives the signals from the pressure sensor and, in response, processes them to determine (or both) the patient's heart rate and respiratory rate. In these modalities, the processing system digitally filters the signals (e.g., with a digital high-pass filter, low-pass filter, and / or band-pass filter) to generate a filtered signal. It then processes the filtered signal to determine the patient's heart rate and respiratory rate. In these modalities, the processing system further processes the signal components indicating the patient's heart rate and respiratory rate to determine a physiological parameter (e.g., F0, F1, energy associated with F0, energy associated with F1, wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary arterial pressure) that indicates the patient's fluid status. In these modes, the processing system transforms the signals in the frequency domain to generate a frequency domain signal. The method for the transformation is usually an FFT, a continuous wavelet transform (CWT), or a discrete wavelet transform (DWT). In some models, the processing system is a microprocessor. The microprocessor typically includes random-access memory that stores a computer program and flash memory that stores a digital representation of the pressure sensor signals. In still other models, the processing system additionally includes a motion sensor, such as an accelerometer or gyroscope. In other models, the processing system additionally includes a wireless transmitter, such as a Bluetooth®, Wi-Fi, or cellular transmitter. In another aspect, the invention provides an IV system that monitors a patient and is placed entirely within the patient's body. The IV system includes: 1) a catheter that is inserted into the patient's venous system; 2) a pressure sensor connected to the catheter that measures signals indicating the pressure in the patient's venous system; and 3) a circuit system that receives the signals from the pressure sensor. The circuit system comprises: i) a differential amplifier that amplifies the signals to generate an amplified signal; ii) a low-pass filter that filters the amplified signal to generate a filtered signal; and iii) a secondary amplifier system that amplifies the filtered signal to generate a twice-amplified signal. In another aspect, the invention provides a similar IV system, also placed entirely within the patient's body, which includes a catheter, pressure sensor, and circuit system similar to those described above. Here, the circuit system comprises: i) an amplifier that amplifies the signals to generate an amplified signal; ii) a filter that filters the amplified signal to generate a filtered signal; iii) a secondary amplifier system that amplifies the filtered signal to generate a twice-amplified signal; and iv) an analog-to-digital converter that digitizes the twice-amplified signal, or a signal derived from it. In these configurations, the amplifiers, filters, and secondary filters described above can be arranged in any order within the circuit system. In yet another aspect, the invention provides a system for monitoring a patient comprising a catheter inserted into the patient's venous system, and a housing placed entirely within the patient's body that includes: 1) a pressure sensor configured to detect catheter fluids and, in response, measure pressure signals; and 2) a circuit system with circuit elements that amplify, filter, and digitize the pressure signals to identify the signal components that indicate the patient's HR and RR. In these modalities, the IV system includes a housing that completely encloses the circuit system and pressure sensor, and attaches to the catheter. The housing, for example, can be worn on the patient's hand or arm. It can be attached to these body parts using a band or adhesive. In these configurations, the differential amplifier has a gain of at least 10X. The low-pass filter typically separates the amplified signal into a first component containing HR and RR-related components, and a second component lacking these components. The low-pass filter usually includes circuit components that generate a filter cutoff value between 10 and 30 Hz. In other configurations, the circuit system additionally includes a high-pass filter that receives the twice-amplified signals and, in response, generates a twice-filtered signal. In this case, the high-pass filter typically includes circuit components that generate a filter cutoff value 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 value between 10 and 30 Hz. In other configurations, the circuit system additionally includes a motion sensor, such as an accelerometer or gyroscope. In other configurations, the circuit system additionally includes a wireless transmitter, such as a Bluetooth®, Wi-Fi, or cellular transmitter. In other configurations, the circuit system additionally includes a microprocessor that operates an algorithm to process the twice-amplified signal, or a signal derived from it. In still other configurations, the circuit system additionally includes a flash memory system that stores a digital representation of the twice-amplified signal or a signal derived from it. onfrQ Ln / zznz / E / YiAi QnfrQ Ln / Zznz / E / YIAI The advantages of the invention should be evident from the following detailed description, and from the claims. Brief Description of the Figures Figure 1 is a drawing of the system of the invention featuring a patch sensor and a PIVA sensor; Figure 2A is a schematic drawing showing how the iPIVA sensor of Figure 1 is attached to a patient; Figure 2B is a mechanical drawing of a worn arm housing that houses a circuit board used in the PIVA sensor; Figure 2C is an image of the circuit board enclosed by the housing worn by the arm shown in Figure 2B; Figures 2D and 2E are, respectively, an image and a photograph of the circuit board indicated by the image shown in Figure 2C; Figure 3 is an electrical schematic diagram of a circuit board from Figures 2D and 2E featuring circuits for filtering, amplifying, and digitizing AC-PVP and DC-PVP waveforms; Figure 4A is a time-dependent graph of a first PVP-AC waveform measured after a first amplifier stage described by the electrical schematic in Figure 3; Figure 4B is a time-dependent graph of a second PVP-AC waveform measured after a second amplifier / filter stage described by the electrical schematic in Figure 3; Figure 4C is an electrical schematic of a circuit board, taken from the electrical schematic in Figure 3, which features a circuit for processing PVP-AC waveforms; Figure 5 is a frequency-dependent logarithmic graph of the quantized AC-PVP and DC-PVP signals using the circuit board of Figure 2E compared to the ideal theoretical responses of the filters and amplifiers described by the electrical schematics of Figure 3 and fabricated on the circuit board of Figure 2E; Figure 6A is a time-dependent graph of a quantized PVP-AC waveform of a patient over a 30-minute period with the system according to the invention; Figures 6B, 6C, and 6D are time-dependent graphs of the PVP-AC waveforms (i.e., waveform fragments) taken from the graph in Figure 6A and starting at time periods of, respectively, 420, 780, and 1310 seconds; Figures 6E, 6F, and 6G are frequency domain spectra representing the FFTs of the waveform fragments shown, respectively, in Figures 6B, 6C, and 6D; Figure 7 is a mechanical drawing of a physiological sensor (PIVA) from Figure 1; Figures 8A-8E are time-dependent graphs of the ECG, PPG, IPG / BR, PCG, and PVP-AC waveforms quantified simultaneously by the patch sensor and the iPIVA sensor of Figure 1; Figures 9A, 9B and 9C are mechanical drawings of, respectively, a lower surface, an upper surface and a detailed view of a physiological iPIVA sensor according to the invention; Figures 10A, 10B, and 10C are, respectively, a schematic drawing of a patient using a modality of a physiological iPIVA sensor according to the invention, a time-dependent graph of a PPG waveform quantized with the physiological iPIVA sensor of Figure 10A, and a time-dependent graph of a PVP-AC waveform quantized with the physiological iPIVA sensor of Figure 10A; Figures 11A, 11B, and 11C are, respectively, a schematic drawing of a patient using a modality of a physiological iPIVA sensor according to the invention, a time-dependent graph of a PPG waveform quantized with the physiological iPIVA sensor of Figure 11A, and a time-dependent graph of a PVP-AC waveform quantized with the physiological iPIVA sensor of Figure 11A; Figures 12A, 12B, 12C and 12D are, respectively, a schematic drawing of a patient using a modality of a physiological iPIVA sensor according to the invention, a time-dependent graph of a PPG waveform quantized with the physiological iPIVA sensor of Figure 12A, a time-dependent graph of a PCG waveform quantized with the physiological iPIVA sensor of Figure 12A and a time-dependent graph of a PVP-AC waveform quantized with the physiological iPIVA sensor of Figure 12A; Figures 13A, 13B, 13C, 13D and 13E are, respectively, a schematic drawing of a patient using a modality of a physiological iPIVA sensor according to the invention, a time-dependent graph of an ECG waveform quantified with the physiological iPIVA sensor of Figure 13A, a time-dependent graph of a PPG waveform quantified with the physiological iPIVA sensor of Figure 13A, a time-dependent graph of an IPG / BR waveform quantified with the physiological iPIVA sensor of Figure 13A and a time-dependent graph of a PVP-AC waveform quantified with the physiological iPIVA sensor of Figure 13A; Figures 14A, 14B, 14C, 14D, 14E, and 14F are, respectively, a schematic drawing of a patient using a modality of an iPIVA physiological sensor according to the invention, a time-dependent graph of an ECG waveform quantified with the iPIVA physiological sensor of Figure 14A, a time-dependent graph of a PPG waveform quantified with the iPIVA physiological sensor of Figure 14A, a time-dependent graph of an IPG / BR waveform quantified with the iPIVA physiological sensor of Figure 14A, a time-dependent graph of a PCG waveform quantified with the iPIVA physiological sensor of Figure 14A, and a time-dependent graph of a PVP-AC waveform quantified with the iPIVA physiological sensor of Figure 14A; Figure 15A is a flowchart showing an algorithm used by the system in Figure 1 that processes signals from both the iPIVA sensor and the patch to monitor a patient; Figure 15B is a time-dependent graph of the ECG, PPG, and IPG / BR waveforms shown in Figures 8A, 8B, and 8C, respectively; Figure 15C is a time-dependent graph of a PVP-AC waveform (referred to in the flowchart of Figure 15A as 'PVP-AC timej' quantized with the PIVA sensor; Figure 15D is a time-dependent plot of a waveform fragment (referred to in the flowchart of Figure 15A as 'PVP-AC time,segment') taken from the time-dependent plot of the PVP-AC waveform in Figure 15C; and, Figure 15E is a frequency domain spectrum (called 'PVP'). Ln / zznz / E / YiAi ACfrequency,segment,bird') showing an ensemble average of DWT of the time-domain waveform fragments indicated in Figure 15C. Detailed Description of the Invention 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 only as an example; 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. iPIVA sensor With reference to Figure 1, a system 10 featuring an IV system 19 incorporating an iPIVA sensor 15, working in conjunction with an iPIVA physiological sensor 70, characterizes the vital signs and hemodynamic parameters of a patient 11 lying in a hospital bed 24. The iPIVA sensor 15 includes an arm-worn housing 20 enclosing a fiberglass circuit board (shown in Figures 2B and 2D, and described in detail below) configured to amplify, filter, and digitize PVP signals. The arm-worn 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 computer or tablet device with comparable functionality) connects to the arm-worn housing 20 via a cable 22, and to the iPIVA physiological sensor 70 via a wireless interface (e.g., Bluetooth®).In certain modes, the remote processor 36 can be connected to the arm-worn housing 20 and the iPIVA physiological sensor 70 via wired (e.g., cable) or wireless (e.g., Bluetooth®) means. During a measurement, it receives PVP signals from the iPIVA sensor 15 and vital signs and hemodynamic parameters from the iPIVA physiological sensor 70, and analyzes them collectively as described in detail below to monitor the patient. Both the iPIVA 15 sensor and the iPIVA 70 physiological sensor are tightly coupled and integrated within the IV system 19. The combination of these components, along with the collective analysis of the information they measure (e.g., by the remote processor), is the focus of the invention described herein. More specifically, during a measurement, the iPIVA 70 physiological sensor measures the patient's vital signs (e.g., HR, HRV, RR, BP, SpO2, TEMP) and hemodynamic parameters (SV, CO, FLUIDS), while the iPIVA 15 sensor measures PVP waveforms, which, upon processing, produce F0 and F1. Digital versions of these datasets are then sent to the remote processor 36 for further processing.For example, in certain modalities, the remote processor 36 analyzes the digitized PVP waveforms and performs frequency domain transformation techniques, such as FFT, CWT, and DWT, to provide a frequency domain spectrum. It then uses the HR and RR values ​​from the iPIVA 70 physiological sensor to detect F0 and F1 from the frequency domain spectrum and determines the associated energies of these features to estimate a parameter indicating a patient's fluid status (e.g., wedge pressure). In certain modalities, the energies associated with F0 and F1, along with measurements from the iPIVA physiological sensor, can be used to estimate other parameters related to the patient's fluid status, such as pulmonary artery pressure and blood volume, as described in more detail below with reference to Figure 15A.The remote processor may also include an internal wireless transmitter (e.g., a Bluetooth® or Wi-Fi transmitter) that sends information via an antenna 57 to the hospital's EMR system, as indicated by icon 39. It may also generate audible and / or visual alarms and alerts when the physiological parameters measured by the iPIVA 15 sensor and the iPIVA 70 physiological sensor indicate a trend in the patient's condition above or below certain predetermined thresholds, indicating that the patient is decompensating. The IV system 19 features a bag 16 containing pharmaceutical compounds and / or fluid (hereinafter “medicine” 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 medicine administered to the patient, its flow rate, metering time, etc. The medicine 17 passes from the bag 16 through the first tube 14 and into the infusion pump 12. From there, it is appropriately dosed and passes through a second tube 18, through a connector 58 and a cable segment 42, into the wear-in arm housing 20, and finally through the venous catheter 21 and into the patient's venous system 23.The housing worn by the arm 20 is usually 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 the medication 17 to the patient's venous system 23, while the iPIVA sensor 15 and the iPIVA physiological sensor 70, which has a pressure measurement system and is described in more detail below, simultaneously measure signals related to the patient's PVP, vital signs, and hemodynamic parameters. It is important to note, and as described in more detail below, that the housing used in arm 20 is designed to maintain constant fluid contact with the patient's circulatory system (and particularly the venous system) while deployed near (or directly) the patient's body. It incorporates 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 housing worn by the arm digitizes the analog PVP waveforms before transmitting them via cable, thereby minimizing any noise (caused, for example, by cable movement) that would normally affect the transmitted analog signals and ultimately introduce inaccuracies in the F0 and F1 values ​​(and their associated energies) measured downstream.Notably, this design provides a relatively short conduction path between where the PVP waveforms are first detected and then... QnfrQ Ln / Zznz / E / YIAI process and digitize; ultimately, this results in signals that are more likely to produce highly accurate values ​​of wedge pressure (and in modalities pulmonary arterial pressure (and particularly the diastolic component in this pressure), blood volume and other fluid-related parameters). Figures 2A-D show in more detail the housing worn by arm 20, its method of operation, and various components included therein.Housing 20 is designed to rest comfortably near or over the patient while: 1) allowing fluids (and / or medications) from the IV system to flow (as indicated by arrow 25 in Figure 2A) into the patient's venous system (box 27 in Figure 2A); 2) measuring pressure signals from the patient's venous system with a pressure sensor (box 29 in Figure 2A); 3) filtering / amplifying the pressure signals with a small-scale printed circuit board featuring circuitry that functions as analog amplifiers and filters (box 31 in Figure 2A); 4) digitizing the filtered / amplified signals with an analog-to-digital converter (box 33 in Figure 2A); and 5) transmitting the digitized signals using a serial protocol (e.g., SPI, I2C) for further processing by the remote processor (arrow 35 in Figure 2A). Figures 2B and 2C show, respectively, a mechanical drawing of the housing worn by arm 20 enclosing circuit board 62 according to the invention, and a photograph of the housing worn by arm 20 connected to the second tube 18 (which receives the drug from the IV system) and cable 22 (which transmits signals to the remote processor). Specifically, circuit board 62 supports a collection of integrated circuits (hereinafter “ICs”) and discrete electrical components that, while working together, perform the functions shown schematically in Figure 2A; they are deployed on circuit board 62 according to an electrical scheme shown in Figure 3 and described in more detail below.The circuit board 62 connects via a back panel 64 at the distal end of the housing to a short cable segment 37 terminated with a multi-pin connector (not shown in the figure) and enclosed by an overmolding 54, which mates with a corresponding connector (also not shown in the figure) enclosed by a similar overmolding 56. The overmolding 56 connects to cable 22, which in turn connects to the remote processor 36. This mechanism allows cable 22 to be easily detached from the housing worn by the arm 20, for example, if the patient is moved or connected to a new infusion system. Cable 22 has individual electrical connectors that supply power (5V, 3.3V, GND) to the circuit board and additionally transmit digitized PVP waveforms via a serial protocol (e.g., SPI, I2C) to the remote processor 36 for follow-up processing, as described in more detail below.In other configurations, circuit board 62 may include an internal wireless transceiver (e.g., Wi-Fi, Bluetooth™, or cellular transceiver) so that it can communicate wirelessly with remote systems, such as the remote processor, infusion pump, and hospital EMR. It may also include an accelerometer to estimate the movement of the arm-worn housing 20, flash memory and RAM for storing information, a high-end microprocessor to analyze PVP waveforms and other signals, a battery, and additional circuitry and sensors to measure TEMPORAL and physiological waveforms (e.g., QnfrQ Ln / Zznz / E / YIAI example, PPG, ECG, IPG and BR) from which vital signs (PR, HR, HRV, SpO2, RR, BP) and hemodynamic parameters (FLUID, SV, CO) are calculated. In general, circuit board 62 is designed to amplify and condition PVP signals along with other physiological signals with an approach comparable to that deployed in conventional vital signs monitors, such as those described in U.S. Patents 10,314,496 and 10,188,349, the content of which is incorporated herein by reference. With reference to Figure 2B, the housing worn on the arm 20 features a connector 60 surrounded by a flange 50 that connects to an internal venous catheter (not shown in the figure) which, during a measurement, is inserted into the patient's venous system. The catheter is typically housed in a coupled plastic component (also not shown in the figure) that is secured to the flange 50 and forms a watertight seal using a rubber gasket 66. The circuit board 62 is held firmly in place within the housing worn by the arm with a set of plastic ribs 59. It connects to the cable 22 with the short cable segment 37, which is typically only a few centimeters long. Figures 2D and 2E show, respectively, an image and a photograph of circuit board 62 within the arm-worn housing. Circuit board 62 was fabricated according to an electrical schematic, which is shown in Figure 3 (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, operational amplifiers 71a-d form analog high-pass and low-pass filters, and operational amplifiers 71e-f 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, 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 16-bit resolution and a maximum digitization rate of 200 Ksamples / second (hereinafter “Ksps”). 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 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 (+5V, +3.3V, and 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 are terminated 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 in particular 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. QnfrQ ίη / ΖΖΠΖ / Ε / ΥΙΛΙ In the various configurations, circuit board 62 additionally includes components for processing, storing, and transmitting data that is digitized by the analog-to-digital converter 68. For example, circuit board 62 may include a microprocessor, microcontroller, or similar integrated circuit, and may additionally provide analog and digital circuitry for the physiological sensor iPIVA.In the modalities, the microprocessor or microcontroller in this can operate the computer code to process PVP-AC, PVP-DC, ECG, PCG, PPG, IPG, BP and other time-dependent waveforms from both the iPIVA sensor and the iPIVA physiological sensor 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 the amplitudes and energies associated with these) 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.Microprocessor processing 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 field. Examples of such algorithms include those described in the following related and issued patents, the contents of which are incorporated herein by reference: “NECK-WORN PHYSIOLOGICAL MONITOR,” USSN 14 / 975,646, filed December 18, 2015; “NECKLACESHAPED PHYSIOLOGICAL MONITOR,” USSN 14 / 184,616, filed August 21, 2014; and “BODY WEAR SENSOR FOR CHARACTERIZING PATIENTS WITH HEART FAILURE, USSN 14 / 145,253, filed July 3, 2014. In related configurations, the circuit board 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. Referring again to Figure 1 and Figures 2A-2E, during a measurement with the iPIVA sensor, the venous catheter delivers the medication 17, measured by the infusion pump 12, through the second tube 18 and into the patient's venous system 23. The second tube 18 is terminated with a connector 58 that connects to the arm-worn housing via a short cable segment 42. This allows the arm-worn housing to be easily detached (i.e., separated) from the IV system 19. In this modality, the second tube 18 can be temporarily pinched with a small plastic piece 60 to occlude fluid flow into and out of the patient. In related modalities, the arm-worn housing 20 may include a power source (such as an internal battery), a processor, and an integrated wireless transmitter.In this way, the iPIVA 15 sensor can function as a wearable device, for example, for an outpatient: it can measure PVP waveforms, process them to determine the energies associated with F0 and F1, and then transmit the digitized versions of these components to a remote device. Such a system could also be effectively coupled with the iPIVA 70 physiological sensor, which is also a wearable hemodynamic and vital signs monitor that is wireless and battery-operated, and can therefore measure the vital signs and hemodynamic parameters of the outpatient.This means that, working together according to the modality mentioned above, the IPIVA sensor and the physiological iPIVA sensors can function as an effective and unique device for patients confined to hospital beds, as well as those being transferred to different areas of the hospital and ultimately moving from the hospital to home. The PVP waveforms measured with the system described herein exhibit signal components related to heartbeats and respiratory events that can vary rapidly over time. These signal components are referred to herein as 'PVP-AC' waveforms, where 'AC' is a term normally used to describe alternating current, but is used herein to describe a signal component that changes rapidly over time as the signal evolves. Figures 6A-D show examples of PVP-AC waveforms and how they are amplified and conditioned by circuit board 62 in the housing worn by arm 20 to improve their signal-to-noise ratio.Similarly, the low-frequency components of PVP waveforms that are relatively stable and unchanging over time are herein referred to as PVP-DC waveforms, where the term DC is normally used to describe direct current, but is used herein to describe signals that do not change rapidly over time. More specifically, PVP waveforms typically have signal levels in the 5-50 V range, a relatively weak amplitude that can be difficult to process. Such signals have been described previously (e.g., 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 Ser. 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 content of these pending patent applications has been previously incorporated herein by reference.In a conventional PIVA measurement, as described in these documents, PVP waveforms are measured using a pressure sensor proximal to the patient that generates analog signals. These signals typically travel through a relatively long cable and are amplified, filtered, and digitized by a system located remotely from the patient. Furthermore, conventional PIVA sensors, such as those described above, commonly include frequency-domain transformation of the PVP waveforms (typically using, for example, an FFT) and then attempt to identify F0 (indicating a frequency related to RR) and F1 (indicating a frequency related to HR) without any secondary determination of these parameters. The energies associated with F0 and F1 are then analyzed to estimate other metrics (e.g., wedge pressure, pulmonary artery pressure) related to the patient's fluid status.However, because PVP waveforms are so weak and characterized by low signal-to-noise ratios, they can be extremely difficult to measure. Furthermore, when transformed into the frequency domain, the signal components related to F0, F1, and their respective harmonics (i.e., frequencies corresponding to integer multiples of F0 and F1) can overlap, making them difficult to analyze. Ln / zznz / E / YiAi to explicitly delineate and measure. These and other factors can ultimately complicate the determination of parameters derived from energies associated with F0 and F1, for example, the patient's fluid status. The present invention attempts to remedy these deficiencies in the measurement of PVP waveforms and, ultimately, the energies associated with F0 and F1, by: 1) amplifying, filtering, digitizing, and, in some cases, processing PVP waveforms immediately after they are detected by the pressure transducer (as opposed to the first analog signal passing through a long, noise-inducing cable) to improve their signal-to-noise ratio and create a digital representation of these that is immune to cable-induced noise; 2) simultaneously and independently quantifying HR and RR with an external iPIVA physiological sensor, which is tightly integrated with the iPIVA sensor; and 3) collectively processing the amplified / filtered / digitized PVP waveforms with HR and RR measurements from the iPIVA physiological sensor to better determine the energies associated with F0 and F1.In addition, other measurements from the iPIVA physiological sensor, such as BP, SV, CO, and FLUIDS, are combined with iPIVA sensor measurements to better determine the patient's fluid status, thereby improving their care within a hospital. Figure 3 shows a schematic 100 of the circuit board 62 described in Figures 2A-C. 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 for digitizing both the PVP-AC and PVP-DC waveforms. More specifically, the circuit described by schematic 100 is designed to perform the following function in series on the incoming PVP waveforms: Incoming PVP waveforms 1) Amplify the signal with a gain of 100X using a zero deviation amplifier 2) Differentially amplify the signal with an additional gain of 10X 3) Filter the amplified signals with a 2-pole 25 Hz low-pass filter This first part of the circuit provides approximately 1000x of combined gain for the incoming PVP waveforms, thus amplifying the input signal (typically in the V range) to a larger signal (in the mV range). The tracking low-pass filter removes any high-frequency noise. Ultimately, these stages facilitate the processing of AC and DC PVP waveforms, as described below. In the descriptions provided herein, the term 'differential amplification' refers to a process where the circuit measures the difference between positive (P_IN in Figure 3) and negative (N_IN in Figure 3) terminals. Specifically, the output of the differential amplifier is a single-ended signal, zeroed at the system's midpoint voltage. Alternatively, it could be zeroed at 0 V, although a center point between the voltage rails generally provides a more accurate and cleaner output signal. Likewise, the term zero-drift amplifier refers to an amplifier that: 1) internally corrects for temperature and other forms of low-frequency signal error; 2) has an impedance of Ln / zznz / E / YiAi very high input; and 3) has very low offset voltages. The incoming signal received by a zero-deviation amplifier is usually extremely small, meaning it can be subject to interference, gain changes, or the amplifier inputs draining the generated current; the amplifier's zero-deviation architecture helps to reduce or eliminate this. 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 signal with a 2-pole, 15 Hz low-pass filter 3) Amplify the signal with a gain of 50X PVP-DC signal only 1) Filter the signal with a 2-pole, 0.07 Hz low-pass filter 2) Filter the signal with a 2-pole, 0.13 Hz low-pass filter 3) Amplify the signal with a gain of 10X AC-PVP and DC-PVP 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 respiratory rate (F0) and heart rate (F1). 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 impact the measurement of F0, F1, and their associated energies, as described above. As noted by experts in the field, circuit elements 102, 104, and 106 shown in Figure 3 can have a comparable design that achieves the stages described above with a scheme that differs slightly from the one shown in Figure 3. Furthermore, it can include other integrated circuits and components to enhance the measurement of F0, F1, and their associated energies, thus providing 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 conjunction with the PVP waveform and used as an adaptive filter to remove motion components.Furthermore, a motion signal measured by one of these components can be processed and compared to a pre-existing threshold value: if the signal exceeds the predetermined threshold value, it may indicate that the patient is moving too much to make an accurate measurement; if the signal is less than the predetermined threshold value, it may indicate that the patient is stable and an accurate measurement can be made. These circuit elements 102, 104, and 106 are generally manufactured on a small fiberglass circuit board, such as the one shown in Figure 2E, characterized by dimensions designed to fit within the arm-worn housing shown in Figures 2B and οηΐτα Ln / zznz / E / YiAi 2C. Figures 4A–C illustrate how circuit board 62 and associated circuit elements 102, as shown in Figures 2A–C and 3, respectively, amplify and generally enhance the analog versions of the PVP-AC waveform. More specifically, Figure 4A shows a time-dependent graph of the PVP-AC waveform measured at location 130 within circuit elements 102 corresponding to an initial analog filtering and amplification stage. As is evident 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 pulse induced by heart rate or respiration.Conversely, after passing through three additional amplification / filtering stages—1) a differential amplifier with an additional gain of 10X; 2) a filter with a 2-pole 25Hz low-pass filter, followed by a 2-pole 0.1Hz high-pass filter, and then a 2-pole 15Hz low-pass filter; and 3) an amplifier with a gain of 50X—the signal is greatly improved. Figure 4B 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 heartbeat-induced pulses (i.e., it shows a well-defined time-domain signal corresponding to HR). Such a waveform, when processed in the frequency domain as described above, would produce clear features corresponding to F1, thus improving the measurement of F0, F1, and their associated energies. It is important to note that, as described above, the analog signal processing shown in Figures 4A–C and the digitization of the PVP waveform are ideally performed as close as possible to the signal source, i.e., in the arm-worn housing shown in Figures 2A–D. 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 filtering / amplification circuit. Ultimately, this approach produces a time-dependent waveform with the highest possible signal-to-noise ratio, thus maximizing the accuracy with which F0, F1, and their associated energies can ultimately be determined. Figure 5 shows the results of an actual experiment designed to validate the effectiveness of the circuit board shown in Figure 2E for isolating and amplifying AC-PVP and DC-PVP signals. For the experiment, a function generator and a signal reduction circuit were combined to generate analog sinusoidal input waveforms representing AC-PVP and DC-PVP signals similar to patient measurements. Like the actual versions of these signals, the input waveforms had frequencies ranging from 0.5 to 100 Hz and amplitudes in the 20 V range. In the experiment, the waveforms were passed through a circuit board similar to the one shown in Figure 2E, where they were filtered and amplified according to the parameters described above (and also shown in Figure 3), and then digitized using an analog-to-digital converter (component 106 shown in Figure 3).The digitized waveforms were stored in memory, and then the peak-to-peak voltages were calculated from the digitized signals. Finally, these values ​​were compared with the ideal frequency-dependent theoretical gain for the AC-PVP and DC-PVP signals. Ln / zznz / E / YiAi as determined by a circuit program / simulator. As shown in Figure 5, the measured peak-to-peak voltage outputs for the AC-PVP and DC-PVP signals are indicated by solid lines (with triangle signal markers for AC-PVP signals and square signal markers for DC-PVP signals) and the left y-axis of the graph. The ideal theoretical gain response of the circuit board is indicated by dashed lines and the right y-axis of the graph. The x-axis indicates logarithms of frequencies corresponding to the input sinusoidal waveforms. Figure 5 shows a strong agreement between the ideal theoretical gain of the circuit board and the measured peak-to-peak voltages of the sinusoidal waveforms after amplification and filtering. This agreement persists from approximately 0.5–50 Hz. This indicates that the circuit board shown in Figure 2E is functioning as expected, effectively filtering and amplifying the AC-PVP and DC-PVP signals. Once measured as described above, a processor analyzes the PVP waveforms to determine F0, F1, and their associated energies. Figures 6A–G show commonly used time-dependent PVP-AC waveforms measured from a hospitalized patient using an IV system similar to that shown in Figure 1. More specifically, Figure 6A shows the waveform measured over a period of approximately 30 minutes. Boxes 110a, 110b, and 110c indicate 1-minute 'waveform fragments' selected to illustrate both the challenges of conventional PIVA sensors and how the invention described herein is designed to overcome these challenges. Figure 6B shows a 1-minute time-dependent waveform fragment (i.e., w(t)) and its first time-dependent derivative (i.e., dw(t) / dt) selected over 420–480 seconds from the PVP-AC waveform in Figure 6A, as noted in Inset 110A. The waveform fragment and its derivative exhibit a series of pulses induced by heartbeats. Here, the derivative effectively serves as a high-pass filter, removing low-frequency components of the signal, such as those due to respiration, and amplifying high-frequency signals, such as those due to heartbeats. Figure 6E shows the FFT of the raw, subderivatived waveform fragment shown in Figure 6B. The peaks in the figures are labeled to indicate F1 (corresponding to 70 beats / min) and the 2X and 3X harmonics of F1. While the signal components associated with F1 are readily apparent in Figures 6B and 6E, those associated with F0 (i.e., respiration) are absent. The patient is clearly alive and likely breathing during this 1-minute period; therefore, the lack of a respiration-related signal could be due to a number of factors, such as movement with the catheter, the low signal associated with F0, motion-induced noise, shallow breathing, and so on. Indeed, a peak corresponding to F0 might be present in Figure 6E, but simply too weak to detect without some prior knowledge of the patient's true respiratory rate. However, an independent measurement of the patient's respiratory rate, for example, with the physiological iPIVA sensor shown in Figure 1, would facilitate the explicit and independent determination of F0.A beat selection algorithm that processes the transformed PVP waveforms could then perform a 'search' in the frequency domain for F0, focusing this search. QnfrQ ίη / ZZΖΠZ / E / YΙΛΙ around the respiratory rate as determined by the patch sensor. This, in turn, could allow the determination of both F0, F1, and their associated energies. Alternatively, an adaptive filter could be implemented in the software, where the filter is specifically designed to amplify the signal components centered around RR, as measured by the physiological iPIVA sensor. Figure 6C shows a second 1-minute waveform fragment selected for 780,840 seconds from the time-dependent PVP-AC waveform in Figure 6A, as indicated in Inset 110B. In this fragment, the signal components due to both F0 (respiratory rate) and F1 (heart rate) are more evident compared to those shown in Figures 6B and 6E. More specifically, the pulses induced by heartbeats are clearly evident in the time domain (Figure 6C), resulting in a well-defined F1 peak (corresponding to a heart rate of 72 beats / min) along with the corresponding 2X and 3X harmonics in the frequency domain (Figure 6F). Furthermore, the respiratory component for this fragment is better defined than that shown in Figures 6B and 6E.Breath-induced ripples are clear in the time domain, resulting in a fairly well-defined F0 peak in the frequency domain, corresponding to 17 breaths / min. As with the case described above, prior knowledge of cardiac and respiratory events as determined by the patch sensor means that an algorithm informed with the corresponding HR and RR values ​​will likely be more successful in detecting the relevant peaks in the frequency domain. Ultimately, this will improve the iPIVA sensor and any measurements it performs. A clear example of this is shown in a third 1-minute waveform fragment selected during 1310–1370 seconds of the PVP-AC waveform shown in Figure 6A, as noted in Box 110C. Here, the signal components due to both F0 (i.e., RR) and F1 (i.e., HR) are more evident compared to those described in the previous cases. The undulations presumably corresponding to HR and RR are clear in the time domain (Figure 6D), resulting in well-defined F0 and F1 peaks in the frequency domain (Figure 6G). However, since the respiratory component in this fragment is so pronounced, the F1 peak (measured at 64 beats / min) could actually correspond to a 4X harmonic of the respiratory event (4 × 17 breaths / min = 68 breaths / min).In other words, it is not clear from a simple inspection of the spectrum in Figure 6G whether the peak near 1 Hz (i.e., 60 beats / min) is due to F1 or the 4X harmonic of F0. As before, an independent heart rate measurement with the patch sensor would resolve this issue, as this could be used to inform the determination of F1. The characteristics associated with F0 and F1 (e.g., their amplitude or energy) can be processed in various ways to estimate fluid-related parameters, such as locking 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; οηΐτα Ln / zznz / E / YiAi Ln / zznz / E / YiAi 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 Ago;24(8):525532; y 4) Hocking et al., Peripheral i.v. analysis (PIVA) of venous waveforms for volume assessment in patients undergoing hemodialysis., BrJ Anaesth. 1 de diciembre de 2017;119(6):1135-1140. Parameters such as wedge pressure, as determined by an iPIVA sensor and a physiological iPIVA sensor working together as described herein, generally indicate a patient's fluid status and are therefore useful for managing patient care and resuscitation. These parameters can be helpful for certain conditions that can be treated with fluid administration (e.g., sepsis) or those treated with fluid removal (e.g., heart failure). Sepsis, in particular, is usually treated in an intensive care unit with IV fluids and antibiotics, which are typically administered as soon as the condition is detected. Fluids are usually replaced to maintain blood pressure. Indeed, appropriately treating patients with fluid-related illnesses such as sepsis can mean the difference between life and death.The risk of death from sepsis is up to 30%, from severe sepsis up to 50%, and from septic shock up to 80%. Estimates suggest that sepsis affects millions of people annually; in the developed world, approximately 0.2 to 3 people per 1,000 are affected by sepsis each year, resulting in about one million cases per year in the United States. Physiological sensor PIVA The physiological sensor iPIVA measurements that directly relate to a patient's fluid status, such as BP, FLUIDS, SV, and CO, can complement a parameter like lockout pressure and aid in the management of a patient suffering from a condition such as sepsis. Sensors that measure such parameters typically implement bioimpedance and bioreactance measurements, operating hardware systems and algorithms similar to those described in the following pending patent applications, the contents of which are incorporated herein by reference: U.S. Patent Application No. 62 / 845,097 (filed May 8, 2019) and U.S. Patent Application No. 16 / 044,386 (filed July 24, 2018). In general, and with reference again to Figure 1, a physiological iPIVA 70 sensor according to the invention typically comprises a central processing unit 83 integrated into a flexible, arm-worn sheath 82 that attaches to the patient's arm. In modalities such as those described in Figures 10-14, the arm-worn sheath 82 may include reflective or transmissive optical sensors and one or more disposable electrodes (not shown in Figure 1) for measuring time-dependent physiological waveforms, such as those shown in Figures 8 and 10-14, and described in more detail below.In modalities such as those shown in Figures 1 and 12-14, the arm-worn sheath 82 and the central processing unit contained therein are connected via a cable 81 to a secondary sensor 80, which can be worn on the patient's shoulder (as shown in Figures 1 and 13A), chest (as shown in Figure 14A), or arm (as shown in Figure 12A). In the shoulder-worn modality, the secondary sensor 80 includes a pair of electrodes; these are typically adhesive electrodes containing hydrogel that adhere the secondary sensor 80 to the patient's skin while simultaneously measuring bioelectrical signals that, with processing and when combined with a similar pair of electrodes (e.g., those in the arm-worn sheath 82), produce ECG, IPG, and BR waveforms.In the chest-worn mode, the secondary sensor may also include a digital microphone that measures PCG waveforms from the underlying heart valves in the patient's chest, along with the pair of electrodes that function as described above. Finally, in the brachial-worn mode, the arm-worn sheath also includes the digital microphone that measures PCG waveforms from the patient's underlying brachial artery and a pair of electrodes that function as described above. The central processing unit 83 features a microprocessor that operates algorithms to process waveforms, ultimately providing parameters such as HR, HRV, RR, BP, SpO2, TEMP, SV, CO, and FLUIDS. Once a measurement is complete, both the iPIVA sensor 15 and the iPIVA physiological sensor transmit information (via wired and / or wireless means) to the remote processor 36, which includes a microprocessor and a display component 38. Algorithms operating through computer code running on the microprocessor in the remote processor 36 process signals from both the patch sensor 30 and the iPIVA sensor 15 to determine the patient's vital signs and fluid status.For example, as described above, one modality of the algorithm can use HR and RR values ​​determined independently by the iPIVA physiological sensor (e.g., from impedance and ECG waveforms) to inform a 'search' for the F0 and F1 values ​​(corresponding, respectively, to RR and HR) measured by the iPIVA sensor. The algorithm then determines the corresponding energies of F0 and F1 and finally processes these energies to determine the patient's fluid status. Such an algorithm is illustrated by the flowchart shown in Figure 15A. Here, the search may involve using a beat selection algorithm to process the frequency-domain spectrum (generated using one of the methodologies described above) of a PVP waveform. Another algorithm modality can collectively process the parameters measured by the iPIVA VA 15 sensor (e.g., locking pressure and blood volume, which may be correlated with energies associated with F0, F1, or some combination thereof) with those measured by the iPIVA 70 physiological sensor (e.g., BP, SpO2, FLUIDS, SV, and CO) to determine the patient's fluid status and effectively inform fluid delivery during resuscitation (e.g., during periods of sepsis and / or fluid overload). In general, by using information from both the iPIVA 15 and iPIVA 70 physiological sensors, a clinician can better manage the patient by characterizing life-threatening conditions and helping to guide resuscitation. As a more specific example, in certain modalities, the BP and SpO2 values ​​measured by the physiological iPIVA sensor can be combined with the volume status determined from the iPIVA sensor to Ln / zznz / E / YiAi estimates 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 deliver during resuscitation. Similarly, SV, CO, BP, and SpO2 measured by the iPIVA physiological sensor, along with the F0 and F1 energy ratio measured by the iPIVA sensor, each indicate the 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 additional 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 toward the central organs, thus increasing the circulatory volume available to the heart (generally called cardiac preload) by about 150–300 milliliters, depending on the amount of venous reserve. If the parameters mentioned above or the index measured by the iPIVA and patch sensors increase, this may indicate that the 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 index measured by the iPIVA and patch sensors. In some modalities, simple linear computational methods, combined with results from clinical studies, can be used to develop models that collectively process the data generated by the iPIVA sensor and the iPIVA physiological sensor. In other modalities, more sophisticated computational models, such as those involving artificial intelligence and / or machine learning, can be used for collective processing. Figure 7 shows a specific modality of a physiological sensor iPIVA 70 according to the invention. Said patch 70 can be integrated with a sensor described above to perform two functions: 1) independently quantifying parameters such as HR and RR to better facilitate the quantification of F0, F1 and their associated energies; and 2) additionally measuring parameters such as BP, FLUIDS, SV and CO that complement the parameters quantified with the iPIVA 15 sensor, such as lock pressure, pulmonary artery pressure, blood volume and fluid status to assist in patient management. The iPIVA 70 physiological sensor measures a patient's ECG, PPG, PCG, IPG, and BR waveforms and calculates vital signs (HR, HRV, SpO2, RR, BP, TEMP) and hemodynamic parameters (FLUIDS, SV, and CO) as described in detail below. Once this information is determined, the patch sensor 30 wirelessly transmits it to a remote monitor for analysis with iPIVA sensor data to characterize the patient. The PIVA 70 physiological sensor shown in Figure 7 has two main components: 1) a central processing unit 83 worn near the patient's wrist; and 2) a secondary sensor 80 worn near the patient's left shoulder. A flexible cable containing wire 81 connects the central processing unit 83 and the secondary sensor 80. The central processing unit includes an optical sensor on its underside (shown in more detail in Figure 9) that measures the waveform. QnfrQ ίη / ΖΖΠΖ / Ε / ΥΙΛΙ PPG of the patient's arm using a reflective mode geometry. The electrode cables (two 90a, 90B on the central processing unit, two 107a, 107B on the secondary sensor) are each connected to single-use adhesive electrodes (not shown in the figure) and help to fix the iPIVA 70 physiological sensor (and particularly the optical sensor) to the patient. The central electronic / sensing module 130 features two 'halves' 139A, 139B, each housing the electronic and sensing components described in more detail below, which are separated by a first flexible rubber gasket 138. The flexible circuitry within the sensor 30 is generally made of Kapton® with embedded electrical traces connecting fiberglass circuit boards (also within the sensor) inside the two halves 139A, 139B of the central electronic / sensing module 130, thereby allowing the sensor to flex and conform to the patient's chest. Electrode cables 141, 142, 147, and 148 connect to a single-use electrode (not shown in the figure) and form two pairs of cables. One cable, 141, 147, in each pair injects electrical energy to quantify the IPG and BR waveforms, while the other cables, 142 and 148, in each pair detect bioelectrical signals. These signals are then processed by the electronics in the central detection / electronics module 130 to determine the ECG, IPG, and BR waveforms. Electrode cables 143 and 145 also connect to a single-use electrode (also not shown in the figure), but they do not perform an electrical function (i.e., they do not measure bioelectrical signals) and only serve to secure the patch sensor 30 to the patient. IPG and BR measurements are performed when current injection electrodes 141 and 147 inject high-frequency (e.g., 100 kHz), low-amperage (e.g., 4 mA) current into the patient's chest. In the various modalities, the injected current can be sequentially adjusted to provide a frequency range (e.g., 5–1000 kHz). In particular, low-frequency measurements (e.g., 5 kHz) generally do not penetrate cell walls within the patient's body and are therefore particularly sensitive to fluids located outside these walls, i.e., extracellular fluids. Electrodes 142 and 148 detect a voltage that indicates the impedance encountered by the injected current. This voltage passes through a series of electrical circuits with analog filters and differential amplifiers. These filter and amplify selected components of the ECG, IPG, and BR waveforms, respectively. The IPG and BR waveforms have low-frequency (DC) and high-frequency (AC) components that are further filtered and processed, as described in more detail below and in the references cited herein, to measure different impedance waveforms. The IPG waveform is sensitive to phase and amplitude changes imparted to the injected current by capacitive changes (e.g., those induced by respiratory events) and conductive changes (e.g., those induced by changes in fluids and blood flow).The BR waveform is primarily sensitive to phase changes imparted in the injected current induced by these same components. The use of a cable 134 to connect the central sensing / electronics module 130 and the optical sensor 136 allows the electrode cables (141, 142 on the central sensing / electronics module 130; 147, 148 on the secondary battery 157) to be separated by a relatively large distance when the patch sensor 30 is attached to a patient's chest. For example, the secondary battery 157 can be attached near the QnfrQ Ln / Zznz / E / YIAI left shoulder of the patient. This separation between electrode leads 141, 142, 147, 148 generally improves the signal-to-noise ratios of the ECG, IPG, and BR waveforms measured by patch sensor 30, since these waveforms are determined from the difference in bioelectrical signals collected by the single-use electrodes, which generally increases with electrode separation. Ultimately, electrode lead separation improves the accuracy of any physiological parameters detected from these waveforms, such as HR, HRV, RR, BP, SV, CO, and FLUIDS. The Acoustic Module 146 features a solid-state acoustic microphone, typically a thin piezoelectric disc surrounded by foam substrates. The foam substrates make contact with the patient's chest during measurement, coupling the patient's heart sounds to the piezoelectric disc, which then measures the patient's heart sounds. A plastic housing encloses the entire Acoustic Module 146. Heart sounds are the 'lub / dub' sounds typically heard from the heart with a stethoscope. They indicate when the underlying mitral and tricuspid valves close (here referred to as "S1," or the 'lub' sound) and the aortic and pulmonic valves close (here referred to as "S2," or the 'dub' sound). (Note: No detectable sounds are generated when the valves open.) With signal processing, heart sounds produce a PCG waveform that is used in conjunction with other signals to determine blood pressure, as described in more detail below. In other modalities, multiple solid-state acoustic microphones are used to provide redundancy and better detect S1, S2, heart murmurs, and other sounds from the patient's heart. The optical sensor 136 features an optical system 160 comprising a photodetector array 162, arranged in a circular pattern, surrounding an LED 161 that emits radiation in the red and infrared spectral regions. During a measurement, the red and infrared radiation emitted sequentially from the LED 161 is reflected by the underlying tissue in the patient's chest and detected by the photodetector array 162. The detected radiation is modulated by the blood flowing through the capillary beds in the underlying tissue. Processing the reflected radiation with the electronics in the central detection / electronics module 130 results in PPG waveforms corresponding to the red and infrared radiation, which are used to determine blood pressure (BP) and SpO2, as described below. The outer surface of the optical sensor 136 is covered by a heating element featuring a thin Kapton® film 165 with integrated electrical conductors arranged, for example, in a serpentine pattern. Other conductor patterns may also be used. The Kapton® film 165 has cut-out portions that allow the radiation emitted by the LED 161 and detected by the photodetectors 162 to pass through after it is reflected off the patient's skin. A tab 167 in the thin Kapton® film 165 is folded so that it can be connected to the circuit board inside the patch sensor 30. During use, the software running on the patch sensor 30 controls the power management circuitry on the circuit board to apply a voltage to the conductors integrated within the thin Kapton® film 165, thereby passing electrical power through them.The resistance of the embedded conductors causes the Kapton® 165 film to heat up and gradually warm the underlying tissue. The applied heat increases perfusion (i.e., blood flow) to the tissue, which in turn improves the signal-to-noise ratio of the PPG waveform. A temperature sensor located on or near the Kapton® film is integrated with the power management circuitry, allowing the software to operate in a closed loop to carefully monitor and adjust the applied temperature. Here, 'closed-loop' means that the software analyzes the amplitudes of the heartbeat-induced pulses in the PPG waveforms and, if necessary, increases the voltage applied to the Kapton® 165 film to raise its temperature and maximize the heartbeat-induced pulses in the PPG waveforms.Typically, the temperature is regulated to a level between 41-42°C, which has a minimal effect on the underlying tissue and is considered safe by the United States Food and Drug Administration (FDA). The patch sensor 30 also typically includes a three-axis digital accelerometer and a temperature / humidity sensor (not specifically identified in the figure) to measure, respectively, three time-dependent motion waveforms (along the x, yyz axes), humidity values, and TEMPERATURE. The Patch 30 sensor typically samples time-dependent waveforms at relatively high frequencies (e.g., 250 Hz). An internal microprocessor running the firmware processes the waveforms with computational algorithms to generate vital signs and hemodynamic parameters at a rate of approximately one per minute. Examples of algorithms are described in the following pending and issued patents, the contents of which are incorporated herein by reference: “NECK-WORN PHYSIOLOGICAL MONITOR,” USSN 14 / 975,646, filed December 18, 2015; “NECKLACE-SHAPED PHYSIOLOGICAL MONITOR,” USSN 14 / 184,616, filed August 21, 2014. and “BODY-WORN SENSOB FOB CHABACTEBIZING PATIENTS WITH HEART FAILURE”, USSN 14 / 145,253, filed July 3, 2014. The patch sensor 30 shown in Figure 7 is designed to maximize comfort and reduce cable clutter when deployed on a patient, while simultaneously optimizing the ECG, IPG, BR, PPG, and PCG waveforms it measures to determine physiological parameters such as HR, HRV, BP, SpO2, RR, TEMP, FLUIDS, SV, and CO. The flexible rubber gasket 138 allows the sensor 30 to flex on a patient's chest, thus improving comfort for both male and female patients. An additional benefit of its chest-worn configuration is the reduction of motion artifacts, which can distort waveforms and lead to inaccurate reporting of vital signs and hemodynamic parameters.This is due, in part, to the fact that, during daily activities, the thorax generally moves less than the hands and fingers, and the subsequent reduction of the artifact ultimately improves the accuracy of the patient's measured parameters. Measurement of time-dependent physiological waveforms and calculation of vital signs and hemodynamic parameters The patch sensor described above determines vital signs (HR, RR, SpO2, TEMP) and Ln / zznz / E / YiAi hemodynamic parameters (FLUIDS, SV, CO) are generated by collective processing of time-dependent ECG, IRG, BR, PPG, PCG, and ACC waveforms, as shown in Figures 8A–E (note: The BR and IPG waveforms have a similar morphology and, therefore, for simplicity, only the IPG waveform is shown in Figure 8D). The ECG, IPG, BR, PPG, and PCG waveforms are generally characterized by a heartbeat-induced pulse; these are indicated in the figure by the dashed lines 170a and 170B. The temporal separation of the pulses is inversely related to the heart rate, as shown in Figure 8A. Some of the waveforms, most notably the IPG and BR waveforms, are strongly affected by respiratory events. This is because this event changes the capacitance and, therefore, the impedance in the patient's chest.In particular, Figure 8C shows undulations indicated by the dashed lines 180a, 180b with a spacing inversely related to RR. The values ​​corresponding to these vital signs—HR and RR—can be used to inform a beat selection algorithm used to locate F0 and F1 in the frequency domain spectrum, as described in detail above. During a measurement, the embedded firmware operating in the patch sensor processes the pulses in these waveforms, as described above, with pulse selection algorithms to determine the reliable markers corresponding to the characteristics of each pulse; these markers are then processed with additional algorithms, described herein, to determine vital signs and hemodynamic parameters. For example, Figure 8A shows an ECG waveform measured by the patch sensor described herein. It includes a heartbeat-induced QRS complex that informally marks the beginning of each cardiac cycle. Compared to other physiological waveforms, ECG waveforms generally have relatively good signal-to-noise ratios and are easy to analyze with beat-selection algorithms; therefore, they are often used to measure heart rate, and the QRS complexes serve as fiducial makers for analyzing some of the more complex waveforms described below. Figure 8B shows a PPG waveform, which is measured by the optical sensor and indicates volumetric changes in the underlying capillaries caused by heartbeat-induced blood flow.As is known in the field, the AC and DC components of PPG waveforms quantified with red (J ~ 660 nm) and infrared (U ~ 940 nm) optical radiation can be processed collectively to determine SpOz values. The IPG waveform includes AC and CC components: the CC component indicates the amount of fluid in the chest by measuring the reference electrical impedance; the average Z0 value is used to determine LOS fluids, as mentioned previously. The AC component, shown in Figure 8C, tracks blood flow in the thoracic vasculature and represents the pulsatile components of the IPG waveform. The time-dependent derivative of the AC component includes a well-defined peak that indicates the maximum acceleration of blood flow in the thoracic vasculature. Both the AC and CC components can be processed together with a parameter called left ventricular ejection time (hereinafter “LVET”) and an equation called the Sramek-Bernstein equation (or an equivalent equation) to determine SV.LVET indicates the time separation between the opening and closing of the aortic valves; as is known in the field, it can be determined directly from the time-dependent derivative of the AC component, or alternatively, it can be estimated from the HR value using a standard regression equation called Weissler regression, or from the time separation of the S1 and S1 peaks in the PCG waveform. CO is the mathematical product of SV and HR. The PCG waveform shown in Figure 8D includes two features corresponding to each heartbeat: S1 (indicating closure of the underlying mitral and tricuspid valves) and S2 (indicating closure of the aortic and pulmonic valves). The amplitude, timing, and frequency-domain spectra of S1 and S2 are known to be sensitive to blood pressure. Figure 8E shows a motion waveform measured along a single axis by the accelerometer. Motion waveforms are typically measured along the x, y, and z axes and can be used to characterize the degree and type of patient movement and posture. Blood pressure (BP)-related parameters can be determined by analyzing the time difference between features in different waveforms. For example, algorithms operating in the firmware of the patch sensor can calculate the time intervals between the QRS complex and reliable markers in each of the other waveforms. One such interval is the time between a pulse foot in the PPG waveform (Figure 8B) and the QRS complex (Figure 8A), referred to as pulse arrival time (hereafter “PAT”). PAT is inversely related to BP and systemic vascular resistance. Similarly, vascular transit time (hereafter “VTT”) is the time difference between reliable markers in waveforms other than ECG, for example, points S1 or S2 in a pulse in the PCG waveform (Figure 8D) and the foot of the PPG waveform (Figure 8B).Or the peak of a pulse in the waveform (Figure 8C) and the foot of the PPG waveform (Figure 8B). In general, any set of time-dependent fiducial elements determined from waveforms other than the ECG can be used to determine VTT. Collectively, PAT, VTT, and other time-dependent parameters extracted from pulses in the four physiological waveforms are called 'systolic time intervals' and are generally inversely related to PA. In general, blood pressure (BP) measurement methods based on systolic time intervals indicate changes in BP; they require calibration of a cuff-based system (e.g., manual auscultation or automated oscillometry) to determine absolute BP values. Typically, such calibration methods provide baseline BP values ​​and patient-specific BP to TBP / TVT ratios. During a non-cuff measurement, TBP / TVT values ​​are measured quasi-continuously and then combined with the BP and TBP / TVT values ​​determined during calibration to produce quasi-continuous BP values. Such calibrations typically involve measuring the patient multiple times (e.g., 2–4) with a cuff-based BP monitor employing oscillometry, while simultaneously collecting TBP and TVT values ​​as described above. Each cuff-based measurement results in separate BP values.Calibrations generally last approximately 1 day before they need to be repeated. In some modalities, one of the cuff-based blood pressure measurements coincides with a challenge event that alters the patient's blood pressure, such as tightening a grip, changing posture, or raising the legs. This introduces variation into the calibration measurements, thereby improving the sensitivity of post-calibration measurements to changes in blood pressure. In other modalities, a 'universal calibration' (e.g., a single calibration for all patients) can be used for the BP measurement. In still other modalities, the BP measurement is left uncalibrated, and only the relative BP measurements are calculated. Alternative patch sensors The patch sensor described herein may have a form factor that differs from that shown in Figure 7. For example, Figures 9A-B show, respectively, top and bottom images of such an alternative modality. Like the patch sensor described in Figure 7, the patch sensor 230 shown in Figures 9A-B has two main components: a central sensing / electronics module 252 worn near the center of the patient's chest and having a reflective optical sensor 274, and a secondary module 254 that connects to the central sensing / electronics module 252 with a thin cable 258. The central sensing / electronics module 252 has electrode cables 250a-d that incorporate circular magnets 251a and 250b which, during a measurement, connect to magnetically active, coupled posts on single-use electrodes (not shown in the figure).Single-use electrodes secure the central sensing / electronics module 252 to the patient's chest. Additionally, electrode cable 250a serves as a 'sensing' electrode to detect bioelectrical signals which, after processing, produce the ECG, IPG, and BR waveforms as described above. Similarly, electrode cable 250B serves as a drive electrode to inject high-frequency, low-amperage current into the patient's chest for IPG and BR measurements. Electrode cables 250c-d, along with magnets 251cd, serve no electrical function and are simply used to further secure the sensing / electronics module 252 to the patient's chest. To complete the ECG, IPG, and BR measurements, the secondary module 254 includes a one-way electrode 256a and the corresponding magnet 257a, as well as a one-way drive electrode 256b and the corresponding magnet 257b.They form electrode pairs with the sensing electrode cable 250a and the driving electrode cable 250b. As before, the IPG and BR waveforms can be measured at multiple frequencies ranging from approximately 5-1000KHz. The patch sensor 230 shown in Figures 9A and 9B, like the one shown in Figure 7, includes a reflective optical sensor 274 featuring an LED 272 that emits red and infrared wavelengths. A circular array of photodetectors 270 surrounds the LED 272. A thin Kapton® film 273 with embedded electrical traces surrounds the photodetectors 270 and LED 272, and generates heat when a voltage is applied; this gently heats the skin to 41°C–42°C using a closed-loop system, thereby increasing perfusion and amplifying the corresponding PPG waveforms. The patch sensor 230 includes a thermally conductive metal post 264 that connects to a temperature sensor (not shown in the figure) and the patient's skin during measurement. This allows the patch sensor 230 to measure skin temperature. It is powered by a rechargeable Li-ion battery that can be charged via a small-scale USB port 261, or alternatively with a QnfrQ Ln / Zznz / E / YIAI integrated transformer that performs wireless charging. A simple on / off switch 260 is activated on sensor 230. Sensor 230 lacks an acoustic sensor, which means it cannot measure S1 and S2, as described above. In other configurations, the patch sensor 230 may have different form factors and may include additional sensors. For example, the secondary module 254 may include an acoustic sensor, similar to the acoustic sensor (component 146) shown in Figure 7. The reflective optical sensor 274, like the optical sensor shown in Figure 7 (component 136), may include other non-circular configurations of photodetectors and LEDs. For example, in some configurations, the photodetectors may be arranged in a linear, square, or rectangular array. Figures 10-14 show alternative patch sensor configurations according to the invention, along with time-dependent graphs of the measured waveforms. In these cases, the numbered components of each patch sensor have the same function as those described in Figure 1. For example, Figure 10A shows a patch sensor configuration 70 worn on the wrist of a patient 11. Figures 10B and 10C show, respectively, the PPG and PVP-AC waveforms measured by the patch sensor. Here, the arm-worn wrap 82 includes a reflective optical sensor that measures the PPG waveform from the patient's wrist. Figure 11A shows a similar patch sensor configuration 70, except that the optical sensor 210 is worn as a band around the thumb of the patient 11 and is connected to the central processing unit 83 via a thin cable 112.For this mode, the PPG and PVP-AC waveforms measured by the sensor are shown, respectively, in Figures 11B and 11C. Figure 12A shows a two-part patch sensor 70 featuring an acoustic sensor 114 embedded in a band 113 wrapped around the patient's antecubital fossa. The acoustic sensor 114 is connected to the central processing unit 83 via a thin cable 181 and measures PCG waveforms from acoustic sounds generated by the blood pulse through the underlying brachial artery. In this mode, as shown in Figure 10A, the optical sensor is reflective and measures PPG waveforms from the patient's wrist. The corresponding time-dependent PPG, PCG, and PVP-AC waveforms for this mode are shown in Figures 12B–12D, respectively. Figure 13A shows another two-part patch sensor 70 according to the invention. Here, a secondary sensor containing electrodes 80 is positioned near the patient's shoulder 11 and is connected to the central processing unit 83 via a cable 181. The secondary sensor containing electrodes 80 enables the measurement of ECG and IPG / BR waveforms along the patient's brachial artery using a methodology similar to that described above. Figures 13B-13E show, respectively, the ECG, PPG, ICG / BR, and PVP-AC waveforms measured with this modality of the invention. Figure 14A shows yet another modality of the patch sensor 70. Like Figure 13A, this modality also includes a secondary sensor containing electrodes 85. Only in this case, the secondary sensor 85 includes both electrodes and a phonocardiogram sensor that measures PPG waveforms from the patient's underlying heart 11. The ECG, PPG, IPG / BR, PCG, and PVP-AC waveforms The time-dependent QnfrQ Ln / Zznz / E / YIAI values ​​measured by patch sensor 70 are shown, respectively, in Figures 14B-14F. Algorithms for processing signals from the PIVA and Patch sensors Figure 15A shows a flowchart 300 indicating the steps used by an algorithm that processes signals from both the iPIVA and the patch sensors described herein to determine a parameter (e.g., locking pressure, pulmonary artery pressure, blood volume, fluid status) related to a patient's fluid status. Figures 15B-E show graphs corresponding to different steps listed in flowchart 300. The algorithm begins by explicitly determining the HR / RR parameters with the patch sensor (step 320), as described above. As shown in Figure 10B (taken directly from Figures 8A-C), for such measurements the patch sensor typically measures ECG, PPG, and / or IPG / BR waveforms and processes them as described above to determine HR and RR. The algorithm then collects the PVP waveforms in the time domain using the iPIVA sensor to generate PVPACtime (step 322). At this stage, the algorithm may additionally include filtering algorithms (e.g., bandpass filtering) or other signal processing techniques (e.g., an adaptive filter or averaging technique; use of an accelerometer or acoustic sensor to account for pump-induced motion and noise) to reduce or eliminate artifacts attributed to the pump.Typically, the signals are collected over a period of at least several minutes. The algorithm then segments PVP-AC time into shorter time intervals (e.g., similar to the waveform fragments shown in Figures 6A–D) that are classified as PVP-AC time segments (step 324). An example of PVP-AC time is shown in Figure 10C, with PVP-AC time segments indicated by the time regions of the waveforms between the dashed lines 340 in the figure. Figure 15D shows a time-dependent plot of PVP-AC time segments corresponding to the segment indicated by the shaded circle 342; it has features that indicate both heartbeats and respiratory events. Once the algorithm generates PVP-AC time segments, each segment is transformed into the frequency domain (using, for example, an FFT, CWT, or DWT segment) to generate individual frequency domain segments classified as PVP-AC frequency segments (step 326). The algorithm then takes an ensemble average of the collection of PVP-AC frequency, segments to form PVPAC frequency, segments, bird (step 328). Once the frequency, segments, bird of PVPAC is determined, the algorithm uses HR / RR values ​​determined independently by the patch sensor (step 330) during step 320 to inform a peak selection algorithm that identifies values ​​and energies corresponding to F0 and F1 (step 332). More specifically, the algorithm uses the patch sensor's HR / RR values ​​as truth and then incorporates them into a filter that prevents the algorithm from selecting erroneous peaks in the frequency domain.Alternatively, during stage 330, the HR / RR values ​​determined from the patch sensor can be used in an adaptive filter or comparable mathematical filter to remove erroneous peaks and other features (associated, for example, with motion or noise) from the frequency domain spectrum, thus facilitating the detection of F0 and F1. Ln / zznz / E / YiAi Figure 15E shows graphs of F0 (top graph) and F1 (bottom graph), which in this case were generated using a discrete wavelet transform. As is evident from the graphs, the signal-to-noise ratio of F0 and F1 determined using this approach is high, making it relatively easy to process the parameters associated with these fiducial markers. Once F0 and F1 are selected, their frequency is determined from the peak maximum, and their energy is determined from their peak amplitude or, alternatively, by integrating an area under the curve centered around the peak maximum amplitude (step 332). The algorithm then processes the parameters corresponding to F0 and F1, or a combination thereof, to determine a parameter related to the patient's fluid state (step 334). A clinician can then use this parameter to treat the patient. The algorithm indicated by step 334 in Figure 15A can take several forms. For example, it can be a simple linear regression equation that converts the F0 and F1-related parameters measured with iPIVA (e.g., magnitude, mean, variability, phase, upward slope, or downward slope) into parameters related to the patient's fluid status (weighing pressure, blood volume, pulmonary artery pressure). Here, the linear regression constants (slope, y-intercept) are usually determined beforehand with a clinical trial that simultaneously measures: 1) iPIVA with the system described herein; and 2) parameters related to the patient's fluid status with a reference device such as a pulmonary artery catheter.Once these data are measured, the slope and y-intercept of the linear regression can be determined through data processing. This information is then used in future iPIVA measurements to determine parameters related to the patient's fluid status. The linear regression constants can be grouped according to biometric parameters associated with the patient, such as weight, gender, or vital signs (e.g., HR, BP). In related modalities, the linear regression can be replaced with a more complex mathematical function, such as a polynomial, exponential, or nonlinear equation. The parameters of this equation are predetermined using the approach described above and then used to convert iPIVA values ​​into parameters related to the patient's fluid status. Alternatively, a machine learning approach can be used to develop a model that converts the F0 and F1 parameters measured with iPIVA into those related to the patient's fluid status. One such machine learning approach is called a support vector machine (hereafter “SVM”). The approach here is similar to that used with linear regression: data obtained from a clinical trial are used to build the SVM, which is then used in the future to convert iPIVA parameters into things like cardiac wedge pressure. Other computational models that can be used in similar applications include Gaussian kernel functions, the booster set, and the bagging set. Other alternative modalities In embodiments of the invention, the algorithms operating on the iPIVA sensor can use the following steps to identify features associated with RR (i.e., F0) and HR (i.e., F1): STEP 1) Collect a PVP waveform in the time domain and select the desired section οηΐτα Ln / zznz / E / YiAi to process. STEP 2) Divide the desired section of the PVP waveform into 36-second segments and take a CWT from each segment. STEP 3) Identify a possible F0 value for the CWT of each segment as the median of frequencies associated with the highest energy between 0 and 0.5 Hz. Then calculate the median F0 value for 5 consecutive segments; this becomes the working estimate of F0 for the following steps. STEP 4) Identify the median energies in the 2nd, 3rd, and 4th harmonics of F0, as determined in STEP 3. If the energy of the fourth harmonic is the highest of the three, the fourth harmonic frequency becomes a candidate for F1. STEP 5) Detect all local maxima with frequencies higher than the 4th harmonic of F0. For each maximum, count the number of other maxima with frequencies that are within 10% of a multiple of that maximum's frequency. The maximum with the highest number of multiples is the final F1 for this segment. However, if multiple peaks have the same number of multiples, or if there is only one peak, or if there are no peaks, proceed to STEP 6 below. STEP 6) Find the frequency that is higher than the 4th harmonic of F0 and has the largest corresponding energy (i.e., the integrated area under the peak). This becomes a new candidate for F1. If there is also a candidate F1 from STEP 4, compare the energy of the two candidate F1s and choose the candidate F1 with the highest associated energy. If there is no candidate F1 from STEP 4, the new candidate F1 is calculated as described in this STEP, and it is the final F1 for this segment. STEP 7) The median F1 of the 5 segments above becomes the working estimate of F1. In some modalities, variations of this approach (e.g., using an FFT or DWT instead of a CWT) can be used with the stages listed above to determine the F0 and F1 values. In other embodiments of the invention, the amplitude of heart sounds S1 or S2 (or both) can be used to predict blood pressure (BP). This parameter generally increases linearly with the amplitude of the heart sound. In these embodiments, a universal calibration describing this linear relationship can be used to convert the heart sound amplitude into a BP value. The algorithm for determining BP can also be based on a technique using machine learning or artificial intelligence, for example, a technique using a system-based virtual machine (SVM). Calibration for blood pressure (BP) measurement, for example, can be determined from data collected in a clinical trial with a large number of subjects. Here, numerical coefficients describing the relationship between BP and heart sound amplitude are determined by adjusting the data collected during the trial. These coefficients and a linear algorithm are then coded into the sensor for use during an actual measurement. Alternatively, a patient-specific calibration can be determined by measuring reference blood pressure values ​​and corresponding heart sound amplitudes during a calibration measurement, which precedes an actual measurement. The calibration measurement data can then be adjusted as described above to determine the patient-specific calibration, which is subsequently used to convert heart sounds into BP values. ίη / ζζηζ / Ε / γίΛΐ Time-domain and frequency-domain analyses of IPG, BR, and PCG 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 IPG or BR waveform (as shown in Figure 8C) and then comparing it to a predetermined calibration. Such events can be combined with iPIVA sensor data to help predict patient decompensation. In other modalities, the invention 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 IPG, PCG, and PPG waveforms, the embedded firmware operating the patch sensor can employ a signal processing technique called beatstacking. With beatstacking, for instance, an average pulse is calculated from multiple (e.g., seven) consecutive pulses of the IPG waveform, which are delineated by analyzing the corresponding QRS complexes in the ECG waveform, and then averaged together. The derivative of the AC component of the IPG waveform is then calculated over a window of seven samples as an ensemble average and subsequently used as described above. In other modalities, a sensitive accelerometer can be used instead of the acoustic sensor (for example, in the patch sensor shown in Figures 9A-B) to measure small-scale seismic chest movements driven by the patient's underlying beating heart. Such waveforms are called seismocardiograms (SCGs) and can be used instead of (or in conjunction with) PCG waveforms to measure S1 and S2 heart sounds. In other modalities, PIVA and iPIVA signals can be used to estimate conditions such as IV infiltration, extravasation, and IV occlusion. Here, changes in the time- and frequency-domain PVP waveforms can indicate these conditions. For example, a gradual increase in PVP combined with a gradual decrease in F0 and F1 may indicate that an IV catheter is slipping out of the patient's vein and into the surrounding tissue. Alternatively, a rapid increase in PVP along with a rapid decrease in F0 and F1 may indicate that the IV catheter is occluded. In other modalities, these signals can be used to monitor IV pump performance (e.g., flow rate) or whether the IV system is in a free-flow state. These and other embodiments of the invention are considered to be within the scope of the following claims.

Claims

CLAIMS 1. An intravenous (“IV”) system for monitoring a patient and placed in the patient’s body, comprising: a catheter configured to be inserted into the patient’s venous system; a pressure sensor connected to the catheter and configured to quantify physiological signals indicating pressure in the patient’s venous system; a motion sensor configured to quantify 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 a physiological parameter when the processing system determines that the motion signals are below the predetermined threshold value.

2. The system according to claim 1, wherein the motion sensor is one of an accelerometer and a gyroscope.

3. The system according to claim 2, wherein the motion sensor is a 3-axis accelerometer.

4. The system according to claim 3, wherein the processing system is configured to calculate a motion vector by analyzing a motion signal corresponding to each axis of the 3-axis accelerometer.

5. The system according to claim 1, wherein the predetermined threshold value for movement corresponds to a vector quantity of 0.1 G.

6. The system according to claim 1, wherein the processing system is further configured to digitally filter the physiological signals to generate a filtered signal.

7. The system according to claim 6, wherein the processing system is configured to digitally filter the physiological signals with a high-pass filter to generate a filtered signal.

8. The system according to claim 7, wherein the processing system is further configured to process the filtered signal to determine the signal components that indicate the patient's heart rate and respiratory rate.

9. The system according to claim 1, wherein the processing system is further configured to transform the physiological signals in the frequency domain to generate a frequency domain signal.

10. The system according to claim 9, wherein the processing system is configured to transform physiological signals in the frequency domain using an FFT to generate a frequency domain signal.

11. The system according to claim 9, wherein the processing system is configured to transform physiological signals in the frequency domain using a wavelet transform to generate a frequency domain signal.

12. The system according to claim 11, wherein the processing system is configured to transform physiological signals in the frequency domain using a continuous and discrete wavelet transform to generate a frequency domain signal.

13. An IV system for monitoring a patient and placed on the patient's body, comprising: a catheter configured to be inserted into the patient's venous system; a pressure sensor connected to the catheter and configured to quantify physiological signals indicating pressure in the patient's venous system; a motion sensor configured to quantify 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 with a mathematical model to determine the patient's posture; and iv) process the physiological signals to determine a physiological parameter when the processing system determines that the patient is in a predetermined posture.

14. The system according to claim 13, wherein the motion sensor is one of an accelerometer and a gyroscope.

15. The system according to claim 14, wherein the motion sensor is a 3-axis accelerometer.

16. The system according to claim 15, wherein the processing system is configured to calculate a motion vector by analyzing a motion signal corresponding to each axis of the 3-axis accelerometer.

17. The system according to claim 13, wherein the processing system is further configured to compare the motion vector with a predetermined lookup table to determine the patient's posture.

18. The system according to claim 13, wherein the processing system is further configured to transform the physiological signals in the frequency domain to generate a frequency domain signal.

19. The system according to claim 18, wherein the processing system is configured to transform physiological signals in the frequency domain using an FFT to generate a frequency domain signal.

20. The system according to claim 18, wherein the processing system is configured to transform physiological signals in the frequency domain using a wavelet transform to generate a frequency domain signal.

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