Patient Monitoring Systems

Abstract

The present invention provides an IV system for monitoring a patient positioned on the patient's body, the IV system including: 1) a catheter inserted into the patient's venous system; 2) a pressure sensor connected to the catheter that measures a physiological signal indicative of pressure within the patient's venous system; 3) a motion sensor that measures a motion signal; and 4) a processing system that: i) receives the physiological signal from the pressure sensor; ii) receives the motion signal from the motion sensor; iii) processes the motion signal by comparing the motion signal to a predetermined threshold to determine when the patient has a relatively low degree of motion; and iv) processes the physiological signal and determines a physiological parameter when the processing system determines that the motion signal is below the predetermined threshold.

Description

[Technical field]

[0001] (PRIORITY CLAIM AND CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 043,494, filed June 24, 2020, entitled "PATIENT-MONITORING SYSTEM," the entire contents of which are incorporated herein by reference in their entirety.

[0002] FIELD OF THEINVENTION The invention described herein relates to systems for drug and fluid delivery and for patient monitoring in, for example, hospitals and medical clinics. [Background technology]

[0003] Unless a term is expressly defined herein using the phrase "herein "___"" or similar language, there is no intention to limit the meaning of the term other than its plain or ordinary meaning. To the extent that any term is referred to herein in a manner consistent with a single meaning, it is done for clarity only and is not intended to limit such claim term to that single meaning. Finally, unless a claim element is defined by reciting the words "means" and function without reciting any structure, the scope of any claim element is not intended to be construed pursuant to application of 35 USC § 112(f).

[0004] Proper treatment of a hospitalized patient typically requires: 1) the delivery of medications and fluids using an intravenous (herein "IV") catheter and an infusion pump; and, 2) the measurement of vital signs and hemodynamic parameters with a patient monitor. Typically, an IV catheter is inserted into a vein in the patient's hand or arm, and the patient monitor connects to sensors or electrodes worn on (or inserted into) the patient's body.

[0005] Conventional patient monitors typically use torso-worn electrodes to measure electrocardiogram (herein "ECG") and impedance pneumography (herein "IP") waveforms from which heart rate (herein "HR"), heart rate variability (herein "HRV"), and respiration rate (herein "RR") are calculated. Most conventional monitors also typically measure optical signals called photoplethysmogram (herein "PPG") waveforms using sensors that are clipped onto the patient's finger or earlobe. Such sensors can calculate blood oxygen level (herein "SpO2") and pulse rate (herein "PR") from these PPG waveforms. More advanced monitors can also measure blood pressure (herein "BP"), particularly systolic (herein "SYS"), diastolic (herein "DIA"), and average (herein "MAP") BP, typically using a cuff-based technique called oscillometry or a pressure-sensitive catheter inserted into the patient's arterial system called an arterial line. Digital stethoscopes, which can be either portable and body-worn devices, can measure phonocardiogram (herein "PCG") waveforms indicative of heart sounds and murmurs.

[0006] Some patient monitors are fully body-worn. These typically take the form of a patch that measures ECG, HR, HRV, and in some cases RR. Such patches may also include an accelerometer that measures movement (herein "ACC") waveforms. Algorithms can determine the patient's posture, degree of movement, falls, and other relevant parameters from the ACC waveform. Patients typically wear these types of patches in hospitals, or alternatively for ambulatory and home use. The patches are typically worn for relatively short periods of time (e.g., days to weeks). They are typically wireless, usually including technologies such as Bluetooth transceivers, to transmit information over short distances to a secondary "gateway" device, which typically includes a cellular or Wi-Fi radio for transmitting information to a cloud-based system.

[0007] Even more sophisticated patient monitors use invasive sensors called Swan-Ganz or pulmonary artery catheters to measure parameters such as stroke volume (herein "SV"), cardiac output (herein "CO"), and cardiac wedge pressure. To make measurements, these sensors are positioned in the patient's left heart, and they are "wedged" into the small pulmonary vessels 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 body-worn electrodes (typically deployed on the patient's chest, legs, and / or neck) and measure impedance plethysmogram (herein "IPG") and / or bioreactance (herein "BR") waveforms. Analysis of the IPG and BR waveforms yields SV, CO, and thoracic impedance, which are proxies for fluid within the patient's chest (herein "FLUIDS"). It should be noted that IPG and BR waveforms generally have similar shapes and are sensed using similar measurement techniques, and therefore are used interchangeably herein.

[0008] Devices measuring SV, CO, and FLUIDS can establish related metrics such as a patient's blood volume, fluid responsiveness, and in some cases, central venous pressure (herein "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 disadvantageous and are associated with high infection risks. In addition, CVP measurements can change more slowly 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 on the central system, thereby temporarily masking blood loss in conventional CVP measurements. Such masking can lead to delayed recognition and treatment of patient conditions, thereby worsening outcomes.

[0009] To address these and other shortcomings, a measurement technique referred to as peripheral venous waveform analysis (herein "PIVA") has been developed, as described in U.S. Patent Application No. 14 / 853,504 (filed September 14, 2015, published as U.S. Patent Publication No. 2016 / 0073959) and PCT Application No. PCT / US16 / 16420 (filed February 3, 2016, published as WO 2016 / 126856), the contents of which are incorporated herein by reference. These documents describe a sensor (herein "PIVA sensor") featuring a pressure transducer that receives signals from an indwelling catheter inserted within a patient's venous system and connects through a cable to remote electronics that process the signals generated from the pressure transducer. PIVA sensors typically use existing IV lines (including IV tubing attached to a saline drip machine or infusion pump) to measure a time-dependent waveform indicative of peripheral venous pressure (herein "PVP"). Measurements made with PIVA sensors are typically characterized by a mathematical transformation of the PVP waveform into the frequency domain, performed with a remote computer, using a methodology called a Fast Fourier Transform (herein "FFT"). Analysis of the frequency domain spectrum generated with the FFT can yield the RR frequency (herein "F0") and the HR frequency (herein "F1"), which are indicative of the patient's HR and RR, respectively. More detailed analysis of F0 and F1, for example, the use of computer algorithms to determine the amplitude of these peaks, or alternatively, to integrate the area under the curve centered on the maximum peak amplitude, determines the "energy" of these features. Further processing of these energies provides an indication of the patient's blood volume status.Such measurements are described, for example, in the following references, the contents of which are incorporated herein by reference: 1) Hocking et al., "Peripheral venous waveform analysis for detecting hemorrhage and iatrogenic volume overload in a porcine model." Shock. 2016 Oct;46(4):447-52; 2) Sileshi et al., "Peripheral venous waveform analysis for detecting early hemorrhage: a pilot study." Intensive Care Med. 2015 Jun;41(6):1147-8; 3) Miles et al., "Peripheral intravenous volume analysis (PIVA) for quantitating volume overload in patients hospitalized with acute decompensated heart failure - a pilot study." J Card Fail. 2018 Aug;24(8):525-532; and 4) Hocking et al., "Peripheral iv analysis (PIVA) of venous "waveforms for volume assessment in patients undergoing haemodialysis.", Br J Anaesth. 2017 Dec 1;119(6):1135-1140.

[0010] Unfortunately, during a typical measurement with a PIVA sensor, 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 corresponding signal magnitude in the time-dependent PVP waveform measured by a conventional pressure transducer is often very weak (typically 5-50 V). In addition, the PVP waveform is typically amplified, conditioned, digitized, and finally processed using electronic systems located remotely from the patient. Thus, prior to these steps, analog versions of the waveforms travel through cables, which attenuate them and may add noise (e.g., due to motion). Also, in some cases, the PVP waveform simply lacks the signatures corresponding to F0 and F1. Or, the peaks of one primary frequency are obscured by the "harmonics" (i.e., integer multiples of a given frequency) of the other primary frequency. This can make it difficult or impossible for an automated medical device to accurately determine F0 and F1 and the energies associated with these characteristics. [Prior art documents] [Patent documents]

[0011] [Patent Document 1] US Patent Application Publication No. 2016 / 0073959 [Patent Document 2] International Publication No. 2016 / 126856 Summary of the Invention [Means for solving the problem]

[0012] In view of the foregoing, it would be beneficial to improve conventional PIVA sensors to overcome the conventional problems associated with weak and noisy PVP waveforms and improper detection of F0 and F1. Such a system could improve the way patients are monitored in hospitals and medical clinics. To address these and other deficiencies, described herein is an enhanced and improved PIVA sensor (herein "iPIVA sensor") that features 1) a circuit board located in close proximity to an indwelling venous catheter that amplifies, filters, and digitizes the PVP waveforms immediately after the pressure sensor detects them (e.g., directly on the patient's body), and 2) a chest-worn physiological sensor (herein "patch sensor") that helps locate F0 and F1 and then processes these features to provide accurate independent measurements of vital signs including HR and RR that can determine their corresponding energies. An iPIVA sensor according to the present invention can include one or both of these improvements. In addition, according to the present invention, measurements from an iPIVA sensor can be combined with independent measurements of hemodynamic parameters, such as SV, CO, and FLUIDS (which may be performed with a patch sensor or comparable patient monitor), resulting in an improved understanding of the patient's fluid status. Ultimately, the combination of these technologies, i.e., an iPIVA sensor featuring a novel signal conditioning circuit board combined with a complementary patch sensor that measures both vital signs and hemodynamic parameters, may improve the way patients are monitored and resuscitated in hospitals and medical clinics.

[0013] The iPIVA sensor described herein is designed to work with conventional IV systems and connects to the patient using an indwelling catheter, both of which are standard equipment. The catheter is worn adjacent to or on the patient's body, typically on the patient's arm or hand, and includes a housing that encloses a signal conditioning circuit board that features complex circuitry that amplifies, filters, and digitizes the analog PVP waveform. The circuit board may also include components for processing and storing the digitized signal, measuring motion (e.g., accelerometer and / or gyroscope), and wirelessly transmitting information (e.g., Bluetooth transmitter). In this manner, the circuit board can be integrated with a remote processor (e.g., a server, gateway, tablet, smartphone, computer, infusion pump, or some combination thereof) that can collectively analyze the PVP waveform and complementary information from the patch sensor.

[0014] The iPIVA sensor described herein simplifies traditional measurements of vital signs and hemodynamic parameters that may involve multiple devices and take minutes to accomplish. A remote processor wirelessly coupled to both the iPIVA sensor and the patch sensor can additionally integrate with existing hospital infrastructure and notification systems, such as hospital electronic medical record (herein "EMR") systems. Such systems can warn and alert caregivers of changes in the patient's condition, thereby enabling them to intervene.

[0015] Patch sensors measure vital signs such as HR, HRV, RR, SpO2, TEMP, and BP, along with complex hemodynamic parameters such as SV, CO, and FLUIDS. BP measurements are typically cuff-less and calibrated using cuff-based devices such as those based on oscillometry. Patch sensors are typically body-worn devices that adhere to the patient's chest and measure the parameters mentioned above continuously and non-invasively. The chest is an ideal location when such measurements are made on hospital-based patients, as it is usually easily accessible and sensors placed therein are typically unobtrusive, comfortable, and removed from the hand (which typically undergoes a relatively large amount of movement). Because patch sensors are small and therefore significantly less obtrusive and obtrusive than various other patient monitoring devices, the emotional discomfort of wearing one can be reduced, thereby promoting long-term compliance, healing, and overall patient health.

[0016] Alternatively, a system that provides independent measurements of HR, RR, and hemodynamic parameters in place of patch sensors is the Starling HR Sensor, manufactured by Cheetah Medical, based in Newton Center, MA, USA. TM It may be a conventional vital signs or hemodynamic monitor, such as an SV patient monitor.

[0017] The patch sensor may also include a motion-detecting accelerometer and gyroscope, which may determine motion-related parameters such as posture, degree of motion, activity level, respiratory-induced chest lift, and falls. Such parameters may determine, for example, the posture or movement of a patient during hospitalization. The patch sensor may operate additional algorithms that process the motion-related parameters, allowing the patch sensor to measure vital signs and hemodynamic parameters only when motion is minimized or below a predefined threshold, thereby reducing artifacts. Additionally, the patch sensor estimates motion-related parameters such as posture to improve the accuracy of calculations related to vital signs and hemodynamic parameters.

[0018] Disposable electrodes on the bottom of the patch sensor secure it to the patient's body without requiring cumbersome cables. In embodiments, such electrodes easily connect (and disconnect) to the sensor using magnets, thus allowing the sensor to easily and quickly return to the proper position when it is removed. The patch sensor is typically lightweight, weighing about 20 grams. It is powered using a Li-ion battery that can be recharged using a conventional cable or using a wireless mechanism.

[0019] Given the above, in one aspect, the present invention provides an IV system for monitoring a patient positioned on the patient's body, the IV system including: 1) a catheter inserted into the patient's venous system, 2) a pressure sensor connected to the catheter that measures a physiological signal indicative of pressure within the patient's venous system, 3) a motion sensor that measures a motion signal, and 4) a processing system that i) receives the physiological signals from the pressure sensor, ii) receives the motion signals from the motion sensor, iii) processes the motion signals by comparing them to predetermined thresholds to determine when the patient has a relatively low degree of motion, and iv) processes the physiological signals and determines a physiological parameter when the processing system determines that the motion signal is below the predetermined threshold.

[0020] In another aspect, the motion sensor is used to measure the patient's posture as opposed to the patient's movement, and the processing system determines the physiological parameters when the patient is in a given posture.

[0021] In another aspect, the present invention provides an IV system for monitoring a patient including: 1) a catheter inserted into a patient's venous system; 2) a pressure sensor connected to the catheter that measures a physiological signal indicative of pressure within the patient's venous system; 3) a motion sensor that measures a motion signal; and 4) a processing system that transmits the physiological signals or a parameter calculated from these signals only when the motion signal is below a predetermined threshold.

[0022] In an embodiment, the motion sensor is an accelerometer (e.g., a three-axis accelerometer) and / or a gyroscope. In an embodiment, the processing system calculates a motion vector by analyzing the motion signals corresponding to each axis of the three-axis accelerometer. A predetermined motion threshold used to determine whether the patient is moving too violently to make an accurate measurement typically corresponds to a vector magnitude of 0.1 G. In another embodiment, the processing system compares the motion vector to a predetermined look-up table to determine the patient's posture.

[0023] In other embodiments, the processing system digitally filters the signal (e.g., with a digital high pass filter) to generate a filtered signal. It then processes the filtered signal to determine the patient's heart rate / respiration rate. In embodiments, the processing system additionally processes signal components indicative of the patient's heart rate and respiration rate, and determines physiological parameters indicative of the patient's fluid status (e.g., wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary artery pressure).

[0024] In an embodiment, the processing system transforms the signal into a frequency domain to generate a frequency domain signal prior to determining the physiological parameter. The method for transformation is typically FFT, continuous wavelet transform, or discrete wavelet transform.

[0025] In another aspect, the invention provides a system for monitoring a patient and simultaneously delivering IV fluid to the patient. The system features a housing positioned on the patient's body. The housing includes a catheter inserted into the patient's venous system to deliver IV fluid, and a pressure sensor connected to the housing to measure a time-dependent pressure signal indicative of pressure within the patient's venous system. The housing also includes a circuit system connected to the pressure sensor to receive the time-dependent signal generated by the pressure sensor. The circuit system features i) a differential amplifier that amplifies the time-dependent pressure signal 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.

[0026] In embodiments, the differential amplifier, low pass filter, and secondary amplifier can be positioned in any order in a circuit different from that described above.

[0027] In another aspect, the system additionally includes a processing system that runs computer code that analyzes the twice amplified signal and estimates vital signs (e.g., HR, RR) corresponding to the patient. In yet another aspect, the system additionally includes a wireless transmitter that transmits a digital representation of the vital signs to a remote receiver, and a power source that provides power to the pressure sensor, the circuit system, the processing system, and the wireless transmitter.

[0028] In an embodiment, the IV system includes a housing that completely encloses the circuit system and pressure sensor and is attached to a catheter, which can be worn, for example, on the patient's hand or arm.

[0029] In an embodiment, the differential amplifier is characterized by a gain of at least 10 times. The low pass filter typically separates signal components, including heart rate and respiratory rate components, from the amplified signal. The low pass filter typically includes circuit components that generate a filter cutoff of 10 to 30 Hz. In another embodiment, the circuit system additionally includes a high pass filter that receives the twice amplified signal and responsively generates a twice filtered signal. In this case, the high pass filter typically includes circuit components that generate a filter cutoff of 0.01 to 1 Hz.

[0030] In an embodiment, the circuit system additionally includes a second-order low pass filter that receives the twice amplified signal and, in response, generates a thrice filtered signal, where the second-order low pass filter typically includes circuit components that generate a filter cutoff of 10 to 30 Hz.

[0031] In another embodiment, the circuit system additionally includes a motion sensor, such as an accelerometer or gyroscope. In another embodiment, the circuit system additionally includes a wireless transmitter, such as a Bluetooth, Wi-Fi, or cellular transmitter. In another embodiment, the circuit system additionally includes a microprocessor that operates an algorithm for processing the twice amplified signal or a signal derived therefrom. And in yet another embodiment, the circuit system additionally includes a flash memory system that stores a digital representation of the twice amplified signal or a signal derived therefrom.

[0032] In another aspect, the invention provides a system for monitoring a patient including a physiological sensor featuring a bioimpedance and / or bioreactance sensing element connected to the patient and measuring a first set of parameters indicative of the patient's fluid status. The system also includes an IV system featuring 1) a catheter inserted into the patient's venous system, 2) a pressure sensor receiving fluid from the catheter and responsively measuring a waveform indicative of pressure within the patient's venous system, and 3) a first processing system receiving the waveform and processing it or a new signal derived therefrom to estimate a second set of parameters indicative of the patient's fluid status. A second processing system then receives the first and second sets of parameters, or new parameters derived therefrom, and collectively processes them to estimate physiological parameters from the patient.

[0033] In another aspect, the invention provides a similar system in which only a physiological sensor is mounted on the patient, which includes a bio-impedance and / or bio-reactance sensing element and a first processing system.

[0034] In yet another aspect, the present invention provides a system for monitoring a patient including: 1) a bioimpedance and / or bioreactance sensing element connected to the patient and measuring a first time-dependent waveform; 2) an IV system inserted into the patient's venous system characterized by a pressure sensor that measures a second time-dependent waveform; and 3) a processing system that analyzes parameters calculated from both the first and second waveforms and collectively processes them to estimate a physiological parameter from the patient.

[0035] In an embodiment, the second processing system is selected from the group consisting of a computer, a tablet computer, and a mobile phone. The system is capable of running an algorithm that compares the first set of parameters to the second set of parameters and estimates the physiological parameters. In another embodiment, 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, where the third wireless transmitter is capable of wirelessly communicating with both the first and second wireless transmitters.

[0036] In another embodiment, the first set of parameters indicative of the patient's fluid status is selected from the group including BP, SpO2, SV, stroke index, CO, cardiac index, thoracic impedance, FLUIDS, intercellular fluid, and extracellular fluid, In another embodiment, the second set of parameters is selected from the group including F0, F1, energies associated with F0 and F1, mathematical combinations of F0 and F1, and parameters determined therefrom.

[0037] The second processing system may operate a linear mathematical model for collectively processing the first and second sets of parameters. Alternatively, it may operate an artificial intelligence based algorithm for collectively processing the first and second sets of parameters.

[0038] In an embodiment, the physiological parameter estimated by the second processing system is indicative of the patient's fluid status. For example, the estimated physiological parameter may be one of the patient's blood volume, wedge pressure, and pulmonary artery pressure.

[0039] In another aspect, the present invention provides a system for monitoring a patient including: 1) a physiological sensor characterized by a sensing element connected to the patient and measuring a first set of signals indicative of the patient's physiology; 2) an IV system characterized by i) a catheter inserted into the patient's venous system and ii) a pressure sensor that senses fluid from the catheter and, in response, measures a second set of signals indicative of pressure within the patient's venous system; and 3) a processing system that receives the first and second sets of signals and collectively processes them, or new signals derived therefrom, to estimate physiological parameters indicative of the patient's status.

[0040] In another aspect, the present invention provides a similar system in which all elements, ie, only the physiological sensor, the pressure sensor, and the processing system, are worn on the patient's body.

[0041] In yet another aspect, the present invention provides a system for monitoring a patient comprising: 1) a physiological sensor worn on a patient's body with sensing elements that measure heart rate and / or respiration 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 senses the fluid and, in response, measures a signal indicative of pressure in the patient's venous system; and 4) a processing system that receives heart rate and / or respiration rate values ​​from the physiological sensor and collectively processes the values ​​and the signal indicative of pressure in the patient's venous system, or a new signal derived therefrom, to estimate a physiological parameter indicative of the patient's status.

[0042] In embodiments, the physiological sensor measures an ECG waveform, which is then processed to determine a value of HR. The physiological sensor may also measure an IPG or BR waveform, which is then processed to determine a value of RR. In these embodiments, both HR and RR refer to a "first set of signals" as used herein.

[0043] In an embodiment, the pressure sensor measures a time-dependent pressure waveform indicative of the pressure in the patient's venous system, which represents a "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 for performing an FFT, a continuous wavelet transform, or a discrete wavelet transform) to generate a frequency domain spectrum. In one embodiment, the processing system then collectively processes the value of HR and the frequency domain spectrum to determine a feature in the frequency domain spectrum corresponding to HR (i.e., F1), which then processes F1 or a parameter estimated therefrom (e.g., its amplitude or corresponding energy, as described herein) to estimate a physiological parameter indicative of the patient's status. In a related embodiment, the processing system collectively processes the value of RR and the frequency domain spectrum to determine a feature in the frequency domain spectrum corresponding to RR (i.e., F0), which then processes F0 or a parameter estimated therefrom (e.g., its amplitude or corresponding energy, as described herein) to estimate a physiological parameter indicative of the patient's status. In yet another embodiment, both F0 and F1, or parameters derived therefrom, are collectively processed to estimate a physiological parameter indicative of the patient's status, which may be, for example, wedge pressure, central venous pressure, pulmonary artery pressure, blood volume, fluid volume, or related values.

[0044] In another aspect, the invention provides an IV system for monitoring a patient that is positioned on the patient's body, the system featuring 1) a catheter inserted into the patient's venous system, 2) a pressure sensor connected to the catheter that measures a signal indicative of pressure within the patient's venous system, and 3) a processing system that receives and responsively processes the signals from the pressure sensor to measure a physiological parameter.

[0045] In another aspect, the invention provides an IV system for monitoring a patient that is positioned on the patient's body, the system featuring 1) a catheter inserted into the patient's venous system, 2) a pressure sensor connected to the catheter that measures a signal indicative of pressure within the patient's venous system, and 3) a processing system that receives and processes the signals from the pressure sensor to determine signal components indicative of one (or both) of the patient's heart rate and respiratory rate.

[0046] In yet another aspect, the invention provides a system for monitoring a patient positioned on the patient's body, the system featuring 1) a catheter inserted into the patient's venous system to collect fluid, 2) a pressure sensor connected to the catheter to sense the fluid and responsively measure a signal indicative of pressure within the patient's venous system, and 3) a processing system that receives and responsively processes the signals from the pressure sensor to determine one (or both) of the patient's heart rate and respiratory rate.

[0047] In an embodiment, the processing system digitally filters the signal (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 / respiration rate. In an embodiment, the processing system additionally processes signal components indicative of the patient's heart rate and respiration rate, and determines physiological parameters indicative of the patient's fluid status (e.g., F0, F1, energy associated with F0, energy associated with F1, wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary artery pressure).

[0048] In an embodiment, the processing system transforms the signal into a frequency domain to generate a frequency domain signal. The method for the transformation is typically an FFT, a continuous wavelet transform (herein "CWT"), or a discrete wavelet transform (herein "DWT").

[0049] In an embodiment, the processing system is a microprocessor. The microprocessor typically includes a random access memory that stores a computer program and a flash memory that stores a digital representation of the signal from the pressure sensor. In yet another embodiment, the processing system additionally includes a motion sensor, such as an accelerometer or gyroscope. In another embodiment, the processing system additionally includes a wireless transmitter, such as a Bluetooth, Wi-Fi, or cellular transmitter.

[0050] In another aspect, the present invention provides an IV system for monitoring a patient and positioned generally on the patient's body. The IV system includes: 1) a catheter inserted into the patient's venous system; 2) a pressure sensor connected to the catheter and measuring a signal indicative of pressure within the patient's venous system; and 3) a circuit system receiving the signal from the pressure sensor. The circuit system features: i) a differential amplifier for amplifying the signal and generating an amplified signal; ii) a low pass filter for filtering the amplified signal and generating a filtered signal; and iii) a secondary amplifier system for amplifying the filtered signal and generating a twice amplified signal.

[0051] In another aspect, the invention provides a similar IV system, also positioned in its entirety on a patient's body, including a catheter, a pressure sensor, and a circuit system similar to those described above, characterized by i) an amplifier for amplifying a signal and generating an amplified signal, ii) a filter for filtering the amplified signal and generating a filtered signal, iii) a secondary amplifier system for amplifying the filtered signal and generating a twice amplified signal, and iv) an analog-to-digital converter for digitizing the twice amplified signal or a signal derived therefrom.

[0052] In an embodiment, the amplifiers, filters, and second-order filters described above can be arranged in any order within the circuit system.

[0053] In yet another aspect, the present invention provides a system for monitoring a patient, characterized by a catheter for insertion into a patient's venous system and a housing for its entire positioning on the patient's body that encloses 1) a pressure sensor configured to sense and respond to fluid from the catheter and measure a pressure signal, and 2) a circuit system with circuit elements that amplify, filter, and digitize the pressure signal and identify signal components indicative of the patient's HR and RR.

[0054] In an embodiment, the IV system includes a housing that completely encapsulates the circuit system and pressure sensor and is attached to the catheter. The housing can be worn, for example, on the patient's hand or arm. For example, it can be attached to these body parts using a band or adhesive.

[0055] In an embodiment, the differential amplifier is characterized by a gain of at least 10 times. The low pass filter typically separates the amplified signal into a first amplified signal component that includes components related to both HR and RR and a second amplified signal component that lacks these components. The low pass filter typically includes a circuit component that generates a filter cutoff of 10 to 30 Hz. In another embodiment, the circuit system additionally includes a high pass filter that receives the twice amplified signal and, in response, generates a twice filtered signal. In this case, the high pass filter typically includes a circuit component that generates a filter cutoff of 0.01 to 1 Hz.

[0056] In an embodiment, the circuit system additionally includes a second-order low pass filter that receives the twice amplified signal and, in response, generates a thrice filtered signal, where the second-order low pass filter typically includes circuit components that generate a filter cutoff of 10 to 30 Hz.

[0057] In another embodiment, the circuit system additionally includes a motion sensor, such as an accelerometer or gyroscope. In another embodiment, the circuit system additionally includes a wireless transmitter, such as a Bluetooth, Wi-Fi, or cellular transmitter. In another embodiment, the circuit system additionally includes a microprocessor that operates an algorithm to process the twice amplified signal or a signal derived therefrom. In yet another embodiment, the circuit system additionally includes a flash memory system that stores a digital representation of the twice amplified signal or a signal derived therefrom.

[0058] Advantages of the present invention will become apparent from the following detailed description and claims. The present invention provides, for example, the following: (Item 1) 1. An intravenous ("IV") system for monitoring a patient, the IV system being positioned on a body of the patient, the system comprising: a catheter configured to be inserted into the patient's venous system; a pressure sensor connected to the catheter and configured to measure a physiological signal indicative of pressure within the patient's venous system; a motion sensor configured to measure a motion signal; Processing system and Equipped with The processing system includes: i) receiving the physiological signal from the pressure sensor; ii) receiving the motion signal from the motion sensor; and iii) processing the movement signals by comparing the movement signals with predetermined thresholds to determine when the patient has a relatively low degree of movement; iv) processing the physiological signal to determine a physiological parameter when the processing system determines that the motion signal is below the predetermined threshold; A system configured to: (Item 2) 2. The system of claim 1, wherein the motion sensor is one of an accelerometer and a gyroscope. (Item 3) 3. The system of claim 2, wherein the motion sensor is a three-axis accelerometer. (Item 4) 4. The system of claim 3, wherein the processing system is configured to calculate a motion vector by analyzing motion signals corresponding to each axis of the three-axis accelerometer. (Item 5) 2. The system of claim 1, wherein the predetermined threshold for movement corresponds to a vector magnitude of 0.1 G. (Item 6) Item 10. The system of item 1, wherein the processing system is further configured to digitally filter the physiological signal to generate a filtered signal. (Item 7) 7. The system of claim 6, wherein the processing system is configured to digitally filter the physiological signal with a high pass filter to generate a filtered signal. (Item 8) 8. The system of claim 7, wherein the processing system is further configured to process the filtered signal to determine signal components indicative of a heart rate and a respiratory rate of the patient. (Item 9) Item 1 , the system being configured to transform the physiological signal into a frequency domain to generate a frequency domain signal. (Item 10) 10. The system of claim 9, wherein the processing system is configured to transform the physiological signal into the frequency domain using an FFT to generate a frequency domain signal. (Item 11) 10. The system of claim 9, wherein the processing system is configured to transform the physiological signal into the frequency domain using a wavelet transform to generate a frequency domain signal. (Item 12) Item 12. The system of item 11, wherein the processing system is configured to transform the physiological signal into the frequency domain using one of a continuous and a discrete wavelet transform to generate a frequency domain signal. (Item 13) 1. An IV system for monitoring a patient, the IV system being positioned on a body of the patient, the system comprising: a catheter configured to be inserted into the patient's venous system; a pressure sensor connected to the catheter and configured to measure a physiological signal indicative of pressure within the patient's venous system; a motion sensor configured to measure a motion signal; Processing system and Equipped with The processing system includes: i) receiving the physiological signal from the pressure sensor; ii) receiving the motion signal from the motion sensor; and iii) processing the movement signals by comparing them with a mathematical model to determine a posture of the patient; iv) processing the physiological signals to determine physiological parameters when the processing system determines that the patient has a predetermined posture; A system configured to: (Item 14) Item 14. The system of item 13, wherein the motion sensor is one of an accelerometer and a gyroscope. (Item 15) Item 15. The system of item 14, wherein the motion sensor is a three-axis accelerometer. (Item 16) Item 16. The system of item 15, wherein the processing system is configured to calculate a motion vector by analyzing motion signals corresponding to each axis of the three-axis accelerometer. (Item 17) Item 14. The system of item 13, wherein the processing system is further configured to compare the motion vectors to a predetermined lookup table to determine a posture of the patient. (Item 18) Item 14. The system of item 13, wherein the processing system is further configured to transform the physiological signal into a frequency domain to generate a frequency domain signal. (Item 19) 20. The system of claim 18, wherein the processing system is configured to transform the physiological signal into the frequency domain using an FFT to generate a frequency domain signal. (Item 20) 20. The system of claim 18, wherein the processing system is configured to transform the physiological signal into the frequency domain using a wavelet transform to generate a frequency domain signal. [Brief description of the drawings]

[0059] [Figure 1] FIG. 1 is a diagram of a system of the present invention featuring both patch sensors and iPIVA sensors.

[0060] [Figure 2A] FIG. 2A is a schematic drawing showing how the iPIVA sensor of FIG. 1 is attached to a patient.

[0061] [Figure 2B] FIG. 2B is a mechanical drawing of the wrist-worn housing that encloses the circuit board used in the iPIVA sensor.

[0062] [Figure 2C] FIG. 2C is an image of the circuit board enclosed by the wrist-worn housing shown in FIG. 2B.

[0063] [Figure 2D] 2D and 2E are an image and a photograph, respectively, of the circuit board illustrated by the image shown in FIG. 2C. [Figure 2E] 2D and 2E are an image and a photograph, respectively, of the circuit board illustrated by the image shown in FIG. 2C.

[0064] [Diagram 3] FIG. 3 is an electrical schematic diagram of the circuit board of FIGS. 2D and 2E featuring circuitry for filtering, amplifying, and digitizing the PVP-AC and PVP-DC waveforms.

[0065] [Figure 4]Figure 4A is a time dependent plot of a first PVP-AC waveform measured after the first amplifier stage described by the electrical schematic of Figure 3. Figure 4B is a time dependent plot of a second PVP-AC waveform measured after the second amplifier / filter stage described by the electrical schematic of Figure 3. Figure 4C is an electrical schematic of a circuit board derived from the electrical schematic of Figure 3 featuring circuitry for processing the PVP-AC waveform.

[0066] [Diagram 5] FIG. 5 is a frequency dependent plot of the logarithm of the PVP-AC and PVP-DC signals measured using the circuit board of FIG. 2E compared to the theoretical ideal response of the filter and amplifier described by the electrical schematic of FIG. 3 and fabricated on the circuit board of FIG. 2E.

[0067] [Figure 6-1] Figure 6A is a time-dependent plot of a PVP-AC waveform measured from a patient over a 30 minute period using a system in accordance with the present invention. Figures 6B, 6C, and 6D are time-dependent plots of the PVP-AC waveform (i.e., waveform snippets) derived from the plot of Figure 6A and beginning at periods of 420, 780, and 1,310 seconds, respectively.

[0068] [Figure 6-2] 6E, 6F, and 6G are frequency domain spectra representing the FFTs of the waveform snippets shown in FIGS. 6B, 6C, and 6D, respectively.

[0069] [Figure 7] FIG. 7 is a mechanical drawing of the iPIVA physiological sensor of FIG.

[0070] [Figure 8] 8A-8E are time-dependent plots of ECG, PPG, IPG / BR, PCG, and PVP-AC waveforms simultaneously measured by the patch sensor and the iPIVA sensor of FIG. 1.

[0071] [Figure 9] 9A, 9B, and 9C are bottom, top, and exploded mechanical drawings, respectively, of an iPIVA physiological sensor in accordance with the present invention.

[0072] [Figure 10] Figures 10A, 10B, and 10C are, respectively, a schematic drawing of a patient wearing an embodiment of an iPIVA physiological sensor according to the present invention, a time-dependent plot of a PPG waveform measured using the iPIVA physiological sensor of Figure 10A, and a time-dependent plot of a PVP-AC waveform measured using the iPIVA physiological sensor of Figure 10A.

[0073] [Figure 11] Figures 11A, 11B, and 11C are, respectively, a schematic drawing of a patient wearing an embodiment of an iPIVA physiological sensor according to the present invention, a time-dependent plot of a PPG waveform measured using the iPIVA physiological sensor of Figure 11A, and a time-dependent plot of a PVP-AC waveform measured using the iPIVA physiological sensor of Figure 11A.

[0074] [Figure 12] Figures 12A, 12B, 12C, and 12D are, respectively, a schematic drawing of a patient wearing an embodiment of an iPIVA physiological sensor according to the present invention, a time-dependent plot of a PPG waveform measured using the iPIVA physiological sensor of Figure 12A, a time-dependent plot of a PCG waveform measured using the iPIVA physiological sensor of Figure 12A, and a time-dependent plot of a PVP-AC waveform measured using the iPIVA physiological sensor of Figure 12A.

[0075] [Figure 13]Figures 13A, 13B, 13C, 13D, and 13E are, respectively, a schematic drawing of a patient wearing an embodiment of an iPIVA physiological sensor according to the present invention, a time-dependent plot of an ECG waveform measured using the iPIVA physiological sensor of Figure 13A, a time-dependent plot of a PPG waveform measured using the iPIVA physiological sensor of Figure 13A, a time-dependent plot of an IPG / BR waveform measured using the iPIVA physiological sensor of Figure 13A, and a time-dependent plot of a PVP-AC waveform measured using the iPIVA physiological sensor of Figure 13A.

[0076] [Figure 14] Figures 14A, 14B, 14C, 14D, 14E, and 14F are, respectively, a schematic drawing of a patient wearing an embodiment of an iPIVA physiologic sensor according to the present invention, a time-dependent plot of an ECG waveform measured using the iPIVA physiologic sensor of Figure 14A, a time-dependent plot of a PPG waveform measured using the iPIVA physiologic sensor of Figure 14A, a time-dependent plot of an IPG / BR waveform measured using the iPIVA physiologic sensor of Figure 14A, a time-dependent plot of a PCG waveform measured using the iPIVA physiologic sensor of Figure 14A, and a time-dependent plot of a PVP-AC waveform measured using the iPIVA physiologic sensor of Figure 14A.

[0077] [Figure 15-1] FIG. 15A is a flow chart illustrating the algorithm used by the system of FIG. 1 to process signals from both the iPIVA and the patch sensor to monitor a patient.

[0078] [Figure 15-2] FIG. 15B is a time-dependent plot of the ECG, PPG, and IPG / BR waveforms shown in FIGS. 8A, 8B, and 8C, respectively.

[0079] [Figure 15-3]Figure 15C is a time-dependent plot of the PVP-AC waveform (referred to as "PVP-ACtime" in the flow chart of Figure 15A) measured using an iPIVA sensor. Figure 15D is a time-dependent plot of a waveform segment (referred to as "PVP-ACtime,segment" in the flow chart of Figure 15A) obtained from the time-dependent plot of the PVP-AC waveform of Figure 15C.

[0080] [Figure 15-4] FIG. 15E is a frequency domain spectrum (referred to as “PVP-AC frequency, segment, ave”) showing the ensemble average of the DWT of the time domain waveform segments shown in FIG. 15C. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0081] Although the following text describes detailed descriptions of many different embodiments, it should be understood that the legal scope of the invention described herein is defined by the language of the claims set forth at the end of this patent. The detailed description is intended to be exemplary only and does not describe every possible embodiment, as this would be impractical, if not impossible. Those skilled in the art may implement many alternative embodiments that would still fall within the scope of the claims.

[0082] (iPIVA sensor) With reference to FIG. 1, a system 10 featuring an IV system 19 incorporating an iPIVA sensor 15 in cooperation with an iPIVA physiological sensor 70 characterizes vital signs and hemodynamic parameters from a patient 11 located at a hospital bed 24. The iPIVA sensor 15 includes an arm-worn housing 20 that encapsulates a fiberglass circuit board (shown in FIGS. 2B and 2D and described in detail below) configured to amplify, filter, and digitize the PVP signal. The arm-worn housing 20 terminates in an intravenous catheter 21 that is inserted into a vein in the patient's hand or arm. A remote processor 36 (e.g., a tablet computer or device with comparable functionality) connects to the arm-worn housing 20 through a cable 22 and connects to the iPIVA physiological sensor 70 through a wireless interface (e.g., Bluetooth®). In an embodiment, the remote processor 36 can connect to both the arm-worn housing 20 and the iPIVA physiological sensor 70 through wired (e.g., cable) or wireless (e.g., Bluetooth®) means. During measurement, the remote processor 36 receives the PVP signal from the iPIVA sensor 15 and the vital signs and hemodynamic parameters from the iPIVA physiological sensor 70, collectively analyzes them, and monitors the patient, as described in detail below.

[0083] Both the iPIVA sensor 15 and the iPIVA physiological sensor 70 are tightly coupled and integrated within the IV system 19. The combination of these components, along with the collective analysis (e.g., by a remote processor) of the information they measure, is the focus of the invention described herein. More specifically, during measurement, the iPIVA physiological sensor 70 measures the patient's vital signs (e.g., HR, HRV, RR, BP, SpO2, TEMP) and hemodynamic parameters (SV, CO, FLUIDS), while the iPIVA sensor 15 measures the PVP waveform, which uses processing to yield F0 and F1. Digital versions of these data sets flow to the remote processor 36 for further processing. For example, in an embodiment, the remote processor 36 analyzes the digitized PVP waveforms and calculates their frequency domain transforms using techniques such as FFT, CWT, and DWT to yield a frequency domain spectrum. It then uses the HR and RR values ​​from the iPIVA physiological sensor 70 to detect F0 and F1 from the frequency domain spectrum, then determines the associated energy of these features to estimate parameters indicative of the patient's fluid status (e.g., wedge pressure). In an embodiment, the energy associated with F0 and F1 can be used, along with measurements from the iPIVA physiological sensor, 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 FIG. 15A. The remote processor can also include an internal wireless transmitter (e.g., Bluetooth or Wi-Fi transmitter) that transmits information to the hospital's EMR system through antenna 57, as indicated by icon 39. It can also generate audio and / or visual "alarms" and "alerts" when the physiological parameters measured by the iPIVA sensor 15 and the iPIVA physiological sensor 70 indicate a trend in the patient's status above or below some predetermined threshold, thereby indicating that the patient is decompensating.

[0084] The IV system 19 features a bag 16 containing medicinal compounds and / or fluids (herein "medication" 17) for the patient. The bag 16 connects to the infusion pump 12 through a first tube 14. A standard IV pole 28 supports the bag 16, the infusion pump 12, and a remote processor 36. A display 13 on the front panel of the infusion pump 12 indicates the type of drug being delivered to the patient, its flow rate, the measured time, etc. The drug 17 passes from the bag 16 through the first tube 14 into the infusion pump 12. From the infusion pump 12, the drug 17 is suitably metered and controlled and passes through a second tube 18, through a connector 58 and a cable section 42, into the arm-worn housing 20, and finally through an intravenous catheter 21 into the patient's venous system 23. The arm-worn housing 20 is typically affixed to the patient's arm or hand using, for example, an adhesive such as medical tape or a disposable electrode.

[0085] The venous catheter 21 may be a standard venous access device and thus may include a needle, catheter, cannula, or other means of establishing a fluid 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 thereof. In this manner, the IV system 19 delivers medication 17 to the patient's venous system 23 while featuring a pressure measurement system, iPIVA sensor 15 and iPIVA physiological sensor 70, described in more detail below, simultaneously measure signals related to the patient's PVP, vital signs, and hemodynamic parameters.

[0086] Importantly, as described in more detail below, the wrist-worn housing 20 is designed to be in constant "fluid communication" with the patient's circulatory system (particularly the venous system) while it is deployed adjacent to (or directly on) the patient's body. It features an electronic system that measures analog pressure signals in the patient's venous system, generates PVP waveforms, and then amplifies and filters these to optimize their signal-to-noise ratio. An analog-to-digital converter in the wrist-worn housing digitizes the analog PVP waveforms prior to transmitting them over the cable, thereby minimizing any noise (e.g., caused by cable movement) that would normally affect the transmitted analog signals and ultimately introduce inaccuracies into the values ​​of F0 and F1 (and their associated energies) measured downstream. Notably, this design provides a relatively short conduction path between where the PVP waveform is initially detected and then processed and digitized, which ultimately results in a signal that is more likely to yield highly accurate values ​​of wedge pressure (and in embodiments, pulmonary artery pressure (particularly the diastolic component to this pressure), blood volume, and other fluid-related parameters).

[0087] 2A-D show in more detail the arm-worn housing 20, its method of operation, and the various components contained therein. The housing 20 is designed to rest comfortably in close proximity to or on a patient while 1) allowing fluids (and / or medications) from an IV system to flow into the patient's venous system (as indicated by arrow 25 in FIG. 2A) (box 27 in FIG. 2A), 2) measuring a pressure signal from the patient's venous system using a pressure sensor (box 29 in FIG. 2A), 3) filtering / amplifying the pressure signal using a small-scale printed circuit board featuring circuitry that functions as an analog amplifier and filter (box 31 in FIG. 2A), 4) digitizing the filtered / amplified signal using an analog-to-digital converter (box 33 in FIG. 2A), and 5) transmitting the digitized signal using a serial protocol (e.g., SPI, I2C) for further processing by a remote processor (arrow 35 in FIG. 2A).

[0088] 2B and 2C respectively show a mechanical drawing of the arm-worn housing 20 enclosing a circuit board 62 according to the present invention and a photograph of the arm-worn housing 20 connected to a second tube 18 (receiving medication from an IV system) and a cable 22 (transmitting signals to a remote processor). Specifically, the circuit board 62 supports a collection of integrated circuits (herein "ICs") and separate electrical components that cooperate to perform the functions shown diagrammatically in FIG. 2A and are deployed on the circuit board 62 according to the electrical schematic shown in FIG. 3 and described in more detail below. The circuit board 62 connects through a back panel 64 on the distal end of the housing to a short cable section 37 that is terminated in a multi-pin connector (not shown in the figure) and encapsulated by an overmold 54 that mates with a corresponding connector (also not shown in the figure) encapsulated by a similar overmold 56. The overmold 56 connects to the cable 22, which in turn connects to the remote processor 36. In this arrangement, the cable 22 can be easily removed from the wrist-worn housing 20, for example, when the patient is moved or when a new infusion system is connected. The cable 22 features individual electrical connectors that provide power (5V, 3.3V, GND) to the circuit board, as well as transmit the digitized PVP waveform via a serial protocol (e.g., SPI, I2C) to the remote processor 36 for subsequent processing, as described in more detail below. In other embodiments, the circuit board 62 can include an internal wireless transceiver (e.g., Bluetooth, Wi-Fi, or cellular transceiver) so that it can wirelessly communicate with remote systems such as remote processors, infusion pumps, and hospital EMRs. It may also include an accelerometer for estimating movement of the wrist-worn housing 20, flash and RAM memory for storing information, a high-end microprocessor for analyzing PVP waveforms and other signals, a battery, and additional circuitry and sensors for measuring TEMP and physiological waveforms (e.g., 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 the PVP signal along with other physiological signals using an approach comparable to that deployed in conventional vital signs monitors such as those described in U.S. Pat. Nos. 10,314,496 and 10,188,349, the contents of which are incorporated herein by reference.

[0089] 2B, the arm-worn housing 20 is surrounded by a flange 50 and features a connector 60 that connects to an indwelling intravenous catheter (not shown in the figure) that is inserted into the patient's venous system during measurement. The catheter is typically housed within a mating plastic component (also not shown in the figure) that is secured to the flange 50 and forms a leak-proof seal using a rubber gasket 66. The circuit board 62 is held securely in place within the arm-worn housing using a set of plastic ribs 59. It connects to the cable 22 using a short cable section 37 that is typically only a few centimeters in length.

[0090] 2D and 2E show an image and a photograph, respectively, of the circuit board 62 in the arm-worn housing. The circuit board 62 was fabricated according to the electrical schematic shown in FIG. 3 (specifically, component 100) and described in more detail below. The circuit board 62 shown in the figures is a four-layer fiberglass / metal construction that includes metal pads that are soldered to an analog-to-digital converter 68, an accelerometer 75, operational amplifiers 71a-f, and power conditioners 72a-b, among other components. More specifically, operational amplifiers 71a-d constitute analog high-pass and low-pass filters, while operational amplifiers 71e-f and power conditioners 72a-b collectively adjust the power levels for the various components in the circuit board 62. The accelerometer 75 measures the movement of the circuit board 62, and in so doing, the movement of any part of the patient's body to which it is attached. Analog-to-digital converter 68 digitizes the 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 kilosamples per second (herein "Ksps").

[0091] The circuit board 62 additionally includes a set of metal plated holes that support a four pin connector 69, two six pin connectors 77, 78, and a three pin connector 79. More specifically, the connector 69 connects directly to a pressure transducer, which receives a common ground signal and an analog PVP waveform representative of the pressure within the patient's venous system. These waveforms are filtered and digitized as described in more detail below. Through the connector 79, the circuit board receives power (+5V, +3.3V, and ground) from an external power source, e.g., a battery or power supply located within the wrist-worn housing. These power levels may be different in other embodiments of the invention. The digital signals and corresponding grounds from the analog to digital converter 68 are terminated at the connector 78, and they leave the circuit board 62 at this point, e.g., through the cable section 37 shown in FIG. 2C. Connector 77 is primarily used for testing and debugging purposes, in particular to allow the analog PVP signals to be measured using an external device such as an oscilloscope once they have passed through analog high-pass and low-pass filters.

[0092] In an embodiment, the circuit board 62 additionally includes components for processing, storing, and transmitting data that is digitized by the analog-to-digital converter 68. For example, the circuit board 62 can include a microprocessor, microcontroller, or similar integrated circuit, and can additionally provide analog and digital circuitry for the iPIVA physiological sensor. In an embodiment, the microprocessor or a microcontroller thereon can operate computer code to process the PVP-AC, PVP-DC, ECG, PCG, PPG, IPG, BP, and other time-dependent waveforms from both the iPIVA sensor and the iPIVA physiological sensor, and determine vital signs (e.g., HR, HRV, RR, BP, SpO2, TEMP), hemodynamic parameters (CO, SV, FLUIDS), components of the PVP waveform (e.g., F0, F1, and associated amplitudes and energies thereof), and associated parameters related to the patient's fluid status (e.g., wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary artery pressure). "Processing" by such a microprocessor, as used herein, means using algorithms known in the art to digitally filter (e.g., with high-pass, low-pass, and / or band-pass filters), transform (e.g., using FFT, CWT, and / or DWT), mathematically manipulate, and generally use computer code or comparable approaches to process and analyze the waveforms and parameters and constructs derived therefrom.Examples of such algorithms include those described in the following co-pending issued patents, the contents of which are incorporated herein by reference: "NECK-WORN PHYSIOLOGICAL MONITOR," U.S. Ser. No. 14 / 975,646, filed December 18, 2015; "NECKLACE-SHAPED PHYSIOLOGICAL MONITOR," U.S. Ser. No. 14 / 184,616, filed August 21, 2014; and "BODY-WORN SENSOR FOR CHARACTERIZING PATIENTS WITH HEART FAILURE," U.S. Ser. No. 14 / 145,253, filed July 3, 2014.

[0093] In a related embodiment, the circuit board may include both flash and random access memory for storing time-dependent waveforms and numerical values, either before or after processing by the microprocessor. In yet other embodiments, the circuit board may include a Bluetooth and / or Wi-Fi transceiver for both transmitting and receiving information.

[0094] 1 and 2A-2E, during measurements with the iPIVA sensor, the venous catheter delivers medication 17, metered and controlled by the infusion pump 12, through the second tube 18 into the patient's venous system 23. The second tube 18 terminates in a connector 58 that connects to the arm-worn housing through a short cable section 42. This allows the arm-worn housing to be easily decoupled (i.e., separated) from the IV system 19. In this embodiment, the second tube 18 can be temporarily pinched with a small plastic piece 60 to block the flow of fluid into and out of the patient. In a related embodiment, the arm-worn housing 20 can include a power source (such as an internal battery), a processor, and an on-board wireless transmitter. In this way, the iPIVA sensor 15 can function as a body-worn device for, for example, an outpatient, that can measure PVP waveforms, process them, determine the energies associated with F0 and F1, and then transmit digitized versions of these components to a remote device. Such a system may also be effectively coupled with the iPIVA physiological sensor 70, which is also a body-worn vital signs and hemodynamic monitor, both wireless and battery powered, and thus capable of measuring vital signs and hemodynamic parameters from an ambulatory patient. This means that when working together according to the embodiments mentioned above, the iPIVA sensor and the iPIVA physiological sensor may function as an effective stand-alone device for patients assigned to hospital beds, and for patients transported to different areas of the hospital, and ultimately transitioning from the hospital to home.

[0095] PVP waveforms measured using the systems described herein are characterized by signal components related to cardiac and respiratory events that can vary very rapidly with time. Such 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 signal components that change rapidly in 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 wrist-worn housing 20 to improve their signal-to-noise ratio. Similarly, low frequency components of PVP waveforms that are relatively stable and do not vary over time are referred to herein 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 with time.

[0096] More specifically, PVP waveforms typically have signal levels in the 5-50 V range, which is a relatively weak amplitude that can be difficult to process. Such signals have been previously described (e.g., in U.S. Patent Application No. 16 / 023,945, filed June 29, 2018, published as U.S. Patent Publication No. 2019 / 0000326, U.S. Patent Application No. 14 / 853,504, filed September 14, 2015, published as U.S. Patent Publication No. 2016 / 0073959, and PCT Application No. PCT / US16 / 16420, filed February 3, 2016, published as WO 2016 / 126856). The contents of these pending patent applications are hereby incorporated by reference herein above. In conventional PIVA measurements, as described in these documents, the PVP waveform is measured using a pressure sensor proximal to the patient that generates analog signals, which typically pass through a relatively long cable and are amplified, filtered, and digitized using a system located remotely from the patient. In addition, conventional PIVA sensors such as those disclosed above typically include a transformation of the PVP waveform into the frequency domain (typically using, for example, an FFT) and then attempt to identify F0 (indicating a frequency associated with RR) and F1 (indicating a frequency associated with HR) without any secondary determination of these parameters. The energy associated with F0 and F1 is then analyzed to estimate other metrics related to the patient's fluid status (e.g., wedge pressure, pulmonary artery pressure). However, because PVP waveforms are very weak and characterized by a low signal-to-noise ratio, they can be extremely difficult to measure. In addition, when transformed into the frequency domain, signal components associated with F0, F1, and their respective harmonics (i.e., frequencies corresponding to integer multiples of F0 and F1) may overlap with one another, making them difficult to separate and explicitly measure. These and other factors may ultimately complicate the determination of parameters determined from the energies associated with F0 and F1, such as the fluid status of a patient.

[0097] The present invention attempts to address these deficiencies in measuring the PVP waveform, and ultimately the energies associated with F0 and F1, by: 1) amplifying, filtering, digitizing, and in some cases processing the PVP waveforms immediately after they are sensed by the pressure transducer (as opposed to first passing the analog signal through a long noise-inducing cable) to improve their signal-to-noise ratio and create a digital representation of them that is not affected by cable-induced noise; 2) simultaneously and independently measuring HR and RR using an external iPIVA physiological sensor tightly integrated with the iPIVA sensor; and 3) collectively processing the amplified / filtered / digitized PVP waveform along with the HR and RR measurements from the iPIVA physiological sensor to better determine the energies associated with F0 and F1. Additionally, other measurements from the iPIVA physiological sensor, such as BP, SV, CO, and FLUIDS, can be combined with measurements from the iPIVA sensor to better determine the patient's fluid status, thereby improving the treatment of the patient in the hospital.

[0098] Figure 3 shows a schematic 100 of the circuit board 62 illustrated in Figures 2A-C. The schematic 100 includes 1) a first set of circuit elements 102 designed to amplify and filter the PVP-AC waveform, 2) a second set of circuit elements 104 designed to amplify and filter the PVP-DC waveform, and 3) a 16-bit, 200 Ksps analog-to-digital converter 106 for digitizing both the PVP-AC and PVP-DC waveforms.

[0099] More specifically, the circuitry described by schematic 100 is designed to sequentially perform the following functions on the incoming PVP waveform:

[0100] (Incoming PVP waveform) 1) Amplify the signal with a gain of 100x using a zero-drift amplifier 2) Differentially amplify the signal with an additional 10x gain 3) Filter the amplified signal using a 25Hz, 2-pole low-pass filter.

[0101] This first portion of the circuit provides a combined gain of approximately 1,000 times on the incoming PVP waveform, thereby amplifying the input signal (typically in the V range) to a larger signal (in the mV range). A subsequent low pass filter removes any high frequency noise. Ultimately, these steps facilitate the processing of both PVP-AC and PVP-DC waveforms, as described below.

[0102] In the description provided herein, the term "differentially amplify" refers to a process in which a circuit measures the difference between the positive (P_IN in FIG. 3) and negative (N_IN in FIG. 3) terminals. Notably, the output of a differential amplifier is a single-ended signal that is zeroed at the midpoint voltage of the system. Alternatively, it can be zeroed at 0V, but the midpoint between the voltage rails generally provides a more accurate and clear output signal.

[0103] Similarly, the term "zero-drift amplifier" refers to an amplifier that 1) internally corrects for temperature and other forms of low frequency signal errors, 2) has very high input impedance, and 3) has very low offset voltage. The incoming signal received by a zero-drift amplifier is typically very small, meaning it may be subject to interference, gain shifts, or bleed-out of generated currents at the amplifier input, which the amplifier's zero-drift architecture helps to reduce or eliminate.

[0104] After processing the input PVP waveform, the circuitry illustrated by schematic 100 is designed to sequentially perform the following functions on the PVP-AC and PVP-DC waveforms:

[0105] (PVP-AC waveform only) 1) Filter the signal with a 0.1Hz, 2-pole high-pass filter 2) Filter the signal using a 15Hz, 2-pole low-pass filter 3) Amplify the signal by 50x gain

[0106] (PVP-DC signal only) 1) Filter the signal using a 0.07Hz, 2-pole low-pass filter 2) Filter the signal using a 0.13Hz, 2-pole low-pass filter 3) Amplify the signal by 10x gain

[0107] (Both PVP-AC and PVP-DC waveforms) 1) Digitize the signal using a 16-bit, 200Ksps Delta-Sigma Analog-to-Digital Converter

[0108] With this level of digital signal processing, the circuit board 62 is able to process signals associated with the PVP waveforms directly on the patient's body, and more specifically, the respiratory rate (F0) and heart rate (F1). It performs these functions without the need to transmit signals through external cables, an approach that can add noise and other signal artifacts and thus adversely affect the measurement of F0, F1, and their associated energies as described above.

[0109] As will be appreciated by those skilled in the art, the circuit elements 102, 104, and 106 shown in FIG. 3 may have comparable designs that perform the steps described above with a slightly different schematic diagram than that shown in FIG. 3. In addition, it may include other integrated circuits and components to improve the measurement of F0, F1, and their associated energies, and thus provide additional functionality. For example, the circuit board 62 may also include a temperature / humidity sensor, a multi-axis accelerometer, an integrated gyroscope, or other motion detection sensors configured to sense motion signals associated with the patient (e.g., the movement of the patient's arm, wrist, or hand). In an embodiment, for example, the motion signal may be processed in conjunction with the PVP waveform and used as an adaptive filter to remove the motion component. Alternatively, the motion signal measured by one of these components may be processed and compared to an existing threshold, and if the signal exceeds a predetermined threshold, it may indicate that the patient is moving too much and accurate measurements cannot be made, and if the signal is below a predetermined threshold, it may indicate that the patient is stable and accurate measurements can be made.

[0110] Such circuits 102, 104, and 106 are typically fabricated on small fiberglass circuit boards, such as the one shown in FIG. 2E, characterized by dimensions designed to fit inside the wrist-worn housing shown in FIGS. 2B and 2C.

[0111] FIG. 4-C illustrates how the circuit board 62 and associated circuit elements 102 as shown in FIGS. 2A-C and 3, respectively, amplifies and generally improves an analog version of the PVP-AC waveform. More specifically, FIG. 4A illustrates a time-dependent plot of the PVP-AC waveform measured at a location 130 in the 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 cardiac or respiratory induced pulses. In contrast, after passing through three additional amplification / filtering stages, namely: 1) a differential amplifier with an additional 10x gain, 2) a filter with a 25Hz 2-pole low-pass filter, then a 0.1Hz 2-pole high-pass filter, then a 15Hz 2-pole low-pass filter, and 3) an amplifier with a 50x gain, the signal is greatly improved. 4B shows a time-dependent waveform measured at a second location 132 further down the amplifier chain of the circuit, which is characterized by a relatively high signal-to-noise ratio and a clear heartbeat-induced pulse (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, will yield a clear feature corresponding to F1, thereby improving the measurement of F0, F1, and their associated energies.

[0112] Importantly, as explained above, the analog signal processing and digitization of the PVP waveforms shown in Figures 4A-C is ideally performed as close as possible to the signal source, i.e., in the arm-worn housing shown in Figures 2A-D. Such a configuration minimizes noise and attenuation caused by signals propagating through long "lossy" cables (plus subject to motion) to remote filter / amplification circuits. Ultimately, this approach results in time-dependent waveforms with the highest possible signal-to-noise ratio, thereby maximizing the accuracy with which F0, F1, and their associated energies can ultimately be determined.

[0113] FIG. 5 shows the results of an actual experiment designed to verify the effectiveness of the circuit board shown in FIG. 2E for isolating and amplifying both PVP-AC and PVP-DC signals. For the experiment, a function generator and a signal reduction circuit were combined to generate input analog sinusoidal waveforms representing PVP-AC and PVP-DC signals similar to those measured from a patient. Like the actual versions of these signals, the input waveforms had frequencies ranging from 0.5 to 100 Hz and amplitudes within the 20 V range. In the experiment, the waveforms were passed through a circuit board similar to that shown in FIG. 2E, where they were filtered and amplified according to the parameters described above (and shown in FIG. 3), and then digitized using an analog-to-digital converter (component 106 shown in FIG. 3). The digitized waveforms were stored in memory, and the peak-to-peak voltages were then calculated from the digitized signals. Finally, these values ​​were compared to ideal theoretical frequency-dependent gains for the PVP-AC and PVP-DC signals, as determined using a circuit / simulator program.

[0114] As shown in Figure 5, the measured peak-to-peak voltage output for the PVP-AC and PVP-DC signals is shown by the solid line (with triangular signal markers for the PVP-AC signal and square signal markers for the PVP-DC signal) and the y-axis on the left side of the graph. The ideal theoretical gain response of the circuit board is shown by the dashed line and the y-axis on the right side of the graph. The x-axis shows the logarithm of the frequency corresponding to the input sinusoidal waveform.

[0115] Figure 5 shows that there is a strong agreement between the ideal theoretical gain of the circuit board and the measured peak-to-peak voltage of the sinusoidal waveform after it has been amplified and filtered. The agreement persists from frequencies ranging from approximately 0.5 to 50 Hz. This indicates that the circuit board shown in Figure 2E is functioning as expected, effectively filtering and amplifying both the PVP-AC and PVP-DC signals.

[0116] Once measured as described above, a processor analyzes the PVP waveform to determine F0, F1, and their associated energies. Figures 6A-G show typical 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 a waveform measured over a period of approximately 30 minutes. Boxes 110a, 110b, and 110c show a one-minute "waveform snippet" selected to illustrate both the challenges of conventional PIVA sensors and how the invention described herein is designed to overcome these challenges.

[0117] FIG. 6B shows a one minute time-dependent waveform snippet (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 of FIG. 6A as indicated by box 110a. The waveform snippet and its derivative feature a series of heartbeat-induced pulses. Here, the derivative effectively acts as a high-pass filter, removing low frequency components from the signal, such as those due to breathing, and amplifying high frequency signals, such as those due to heartbeat. FIG. 6E shows an FFT of the raw undifferentiated waveform snippet shown in FIG. 6B. The peaks in the figure are labeled to indicate F1 (corresponding to 70 heartbeats / min) and the second and third harmonics of F1.

[0118] While 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 minute, and therefore the absence of respiration-related signals may be due to several factors, such as catheter movement, low signal associated with F0, motion-induced noise, shallow breathing, etc. Indeed, a peak corresponding to F0 may be present in Figure 6E, but is simply very weak and cannot be detected without some prior knowledge of the patient's true RR. However, an independent measurement of the patient's RR, for example with the iPIVA physiological sensor shown in Figure 1, would facilitate an explicit and independent determination of F0. A beat-picking algorithm processing the transformed PVP waveform may then perform a "search" in the frequency domain for F0 and center this search around the respiration frequency as determined by the patch sensor. This may then allow for the determination of both F0, F1, and their associated energies. Alternatively, an adaptive filter can be implemented in software, where the filter is specifically designed to amplify the signal components centered around RR as measured using the iPIVA physiological sensor.

[0119] FIG. 6C shows a second 1-minute waveform snippet selected over 780-840 seconds from the time-dependent PVP-AC waveform of FIG. 6A as indicated by box 110b. In this snippet, signal components due to both F0 (respiratory rate) and F1 (heart rate) are more prominent compared to those shown in FIGS. 6B and 6E. More specifically, the heart rate-induced pulse is clearly prominent in the time domain (FIG. 6C), resulting in a well-defined F1 peak (corresponding to a heart rate of 72 beats / min) along with corresponding 2nd and 3rd harmonics in the frequency domain (FIG. 6F). In addition, the respiratory component for this snippet is better defined than that shown in FIGS. 6B and 6E. The respiratory induced undulations are evident 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 both cardiac and respiratory events as determined using the patch sensor means that an algorithm informed of the corresponding HR and RR values ​​will be more likely to successfully detect the relevant peaks in the frequency domain. Ultimately, this will improve the iPIVA sensor and any measurements made by it.

[0120] A clear example of this is shown in the third 1-minute waveform snippet selected over 1,310-1,370 seconds from the PVP-AC waveform shown in Figure 6A, as indicated by box 110c. Here, the signal components due to both F0 (i.e., RR) and F1 (i.e., HR) are more prominent compared to those described in the previous case. Oscillations presumed to correspond to HR and RR are evident in the time domain (Figure 6D) and result in clearly defined F0 and F1 peaks in the frequency domain (Figure 6G). However, the respiratory component in this snippet is so clearly prominent that the F1 peak (measured at 64 heartbeats / min) may in fact correspond to a fourth harmonic of a respiratory event (4 x 17 heartbeats / min = 68 heartbeats / 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 heartbeats / min) is due to a fourth harmonic of F1 or F0. As mentioned previously, an independent measurement of HR using a patch sensor would solve this problem as it could be used to inform F1 decisions.

[0121] Features associated with F0 and F1 (e.g., their amplitude or energy) can be processed in different ways to estimate fluid-related parameters, such as wedge pressure and / or pulmonary artery pressure. Further processing of the energy then yields the appropriate fluid-related parameters. Examples of such processing are described in the following references, the contents of which are already incorporated herein by reference:

[0122] 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,

[0123] 2)Sileshi et al.,「Peripheral venous waveform analysis for detecting early hemorrhage:a pilot study.」,Intensive Care Med. 2015 Jun;41(6):1147-8、

[0124] 3)Miles et al.,「Peripheral intravenous volume analysis(PIVA) for quantitating volume overload in patients hospitalized with acute decompensated heart failure - a pilot study.」,J Card Fail. 2018 Aug;24(8):525-532、および、

[0125] 4)Hocking et al.,「Peripheral i.v. analysis(PIVA) of venous waveforms for volume assessment in patients undergoing haemodialysis.」,Br J Anaesth. 2017 Dec 1;119(6):1135-1140。

[0126] Parameters such as wedge pressure as determined using both the iPIVA sensor and the iPIVA physiological sensor working together as described herein typically indicate the patient's fluid status and are therefore useful in managing the patient's treatment and resuscitating the patient. These parameters may be useful in the case of certain afflictions that can be treated with fluid delivery (e.g., sepsis) or those that are treated with fluid removal (e.g., heart failure). In particular, sepsis is usually treated in the intensive care unit with IV fluids and antibiotics, both of which are typically administered as soon as the condition is detected. Fluids are typically exchanged so that blood pressure is maintained. In fact, properly treating a patient with a fluid-related illness such as sepsis can mean the difference between life and death. The risk of death from sepsis is as high as 30%, as high as 50% in severe sepsis, and as high as 80% in septic shock. Sepsis affects millions of people annually; it is estimated that in developed countries, approximately 0.2-3 per 1,000 people develop sepsis annually, resulting in approximately 1 million cases per year in the United States.

[0127] (iPIVA Physiological Sensor) Measurements from iPIVA physiological sensors directly related to a patient's fluid status, e.g., BP, FLUIDS, SV, and CO, can complement parameters such as wedge pressure and aid in managing patients suffering from conditions such as sepsis. Sensors measuring such parameters typically deploy bioimpedance and bioreactance measurements and operate 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).

[0128] In general, and referring again to FIG. 1, an iPIVA physiological sensor 70 according to the present invention typically features a central processing unit 83 integrated into a flexible arm-worn wrap 82 that is attached to the patient's arm. In embodiments such as those illustrated in FIGS. 10-14, the arm-worn wrap 82 can include a reflective or transmissive optical sensor and one or more disposable electrodes (not shown in FIG. 1) to measure time-dependent physiological waveforms such as those shown in FIGS. 8 and 10-14 and described in more detail below. In embodiments such as those illustrated in FIGS. 1 and 12-14, the arm-worn wrap 82 and the central processing unit included therein connect through a cable 81 to a secondary sensor 80, which can be worn on the patient's shoulder (as shown in FIGS. 1 and 13A), chest (as shown in FIG. 14A), or upper arm (as shown in FIG. 12A). In a shoulder-worn embodiment, the secondary sensor 80 includes a pair of electrodes, typically adhesive hydrogel-containing electrodes, that adhere the secondary sensor 80 to the patient's skin while, with processing, measuring bioelectrical signals that, when combined with a similar pair of electrodes (e.g., in the arm-worn wrap 82), result in ECG, IPG, and BR waveforms. In a chest-worn embodiment, 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 a pair of electrodes that function as described above. Finally, in an upper arm-worn embodiment, the arm-worn wrap 82 also includes a digital microphone that measures PCG waveforms from the patient's underlying brachial artery, and a pair of electrodes that function as described above.

[0129] The central processing unit 83 features a microprocessor that operates algorithms to process the waveforms and ultimately yield parameters such as HR, HRV, RR, BP, SpO2, TEMP, SV, CO, FLUIDS, etc. Once measurements are completed, both the iPIVA sensor 15 and the iPIVA physiological sensor transmit information (through wired and / or wireless means) to a remote processor 36, which includes a microprocessor and a display component 38. An algorithm operating through computer code executing on the microprocessor in the remote processor 36 processes 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, an embodiment of the algorithm may use values ​​of HR and RR determined independently by the iPIVA physiological sensor (e.g., from impedance and ECG waveforms) to inform a "search" for F0 and F1 values ​​(corresponding to RR and HR, respectively) measured by the iPIVA sensor 15. 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 flow chart shown in Figure 15A, where the search may involve using a beat-picking algorithm and processing the frequency domain spectrum of the PVP waveform (generated using one of the methodologies described above).

[0130] Another embodiment of the algorithm may collectively process parameters measured by iPIVA sensor 15 (e.g., wedge pressure and blood volume, which may correlate with energies associated with F0, F1, or some combination thereof) along with those measured by iPIVA physiologic sensor 70 (e.g., BP, SpO2, FLUIDS, SV, and CO) to determine the patient's fluid status and effectively inform the delivery of fluids while resuscitating the patient (e.g., during periods of sepsis and / or fluid overload). In general, by using information from both iPIVA sensor 15 and iPIVA physiologic sensor 70, a clinician may be able to better manage patient 11 by characterizing life-threatening conditions and help guide the resuscitation of the patient.

[0131] As a more specific example, in an embodiment, the BP and SpO2 values ​​measured by the iPIVA physiological sensor can be combined with the volume status determined from the iPIVA sensor to estimate the patient's blood flow and perfusion. Knowledge of these parameters can then inform an estimate of the amount of fluid the clinician needs to deliver during resuscitation. Similarly, the SV, CO, BP, and SpO2 measured by the iPIVA physiological sensor, along with the ratio of F0 and F1 energies measured by the iPIVA sensor, each indicate the level of perfusion of the patient. They can also be combined in a mathematical "index" to better estimate this condition. These parameters or indices can then be measured while the patient undergoes a technique called "passive leg raising," a test to assess the need for further fluid resuscitation in critically ill patients. Passive leg raising involves raising the patient's legs (typically without the patient's active participation), which causes gravity to draw blood from the legs into the central organs, thereby increasing the circulatory volume available to the heart (typically called "cardiac preload") by approximately 150-300 milliliters, depending on the volume of the venous reservoir. If the above-mentioned parameters or indices measured by iPIVA and patch sensors increase, it can indicate that leg raising is effectively increasing perfusion in the patient's central organs, thereby indicating that the patient will respond to fluids. A clinician can perform a similar test by providing a bolus of fluid to the patient through an IV system and then monitoring the increase or decrease in the parameters or indices measured by iPIVA and patch sensors.

[0132] In embodiments, 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 sensors and iPIVA physiological sensors, in other embodiments, more sophisticated computational models, such as those involving artificial intelligence and / or machine learning, can be used for the collective processing.

[0133] 7 shows a specific embodiment of an iPIVA physiological sensor 70 according to the present invention. Such a patch 70 can be integrated with the iPIVA sensor described above to perform two functions: 1) independently measure parameters such as HR and RR to better facilitate measurement of F0, F1 and their associated energetics, and 2) additionally measure parameters such as BP, FLUIDS, SV, and CO that complement parameters measured using the iPIVA sensor 15 such as wedge pressure, pulmonary artery pressure, blood volume, and fluid status to assist in managing the patient.

[0134] The iPIVA physiological sensor 70 measures the ECG, PPG, PCG, IPG, and BR waveforms from the patient and calculates from these 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 the remote monitor so that it can be analyzed along with information from the iPIVA sensor to characterize the patient.

[0135] The iPIVA physiological sensor 70 shown in FIG. 7 features 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 wire-containing cable 81 connects the central processing unit 83 and the secondary sensor 80. The central processing unit includes an optical sensor on its bottom surface that measures the PPG waveform from the patient's arm using reflectance mode geometry (shown in more detail in FIG. 9). Electrode leads (two 90a, 90b on the central processing unit, two 107a, 107b on the secondary sensor) each connect to a single-use adhesive electrode (not shown in the figures) and serve to secure the iPIVA physiological sensor 70 (particularly the optical sensor) to the patient. The central sensing / electronics module 130 includes: It features two "halves" 139A, 139B, each housing sensing and electronic components described in more detail below, separated by a first flexible rubber gasket 138. The flexible circuit within the sensor 30 is typically made from Kapton® with embedded electrical traces that connect fiberglass circuit boards (also within the sensor) within the two halves 139A, 139B of the central sensing / electronics module 130, thereby allowing the sensor to flex and conform to the patient's chest.

[0136] Electrode leads 141, 142, 147, 148 connect to single-use electrodes (not shown in the figures) and form two "pairs" of leads, one of the leads 141, 147 in each pair injects current to measure the IPG and BR waveforms, and the other lead 142, 148 in each pair senses bioelectrical signals which are then processed by electronics in the central sensing / electronics module 130 to determine the ECG, IPG, and BR waveforms. Electrode leads 143, 145 also connect to single-use electrodes (also not shown in the figures) but do not perform any electrical function (i.e., they do not measure bioelectrical signals) and serve only to secure the patch sensor 30 to the patient.

[0137] The IPG and BR measurements are taken while the current injection electrodes 141, 147 are injecting a high frequency (e.g., 100 kHz), low amperage (e.g., 4 mA) current into the patient's chest. In embodiments, the injected current can in turn be adjusted to have a range of frequencies (e.g., 5-1,000 kHz). In particular, low frequency measurements (e.g., 5 kHz) typically do not penetrate cell walls within the patient's body and are therefore particularly sensitive to fluids located outside of these walls, i.e., extracellular fluid.

[0138] The electrodes 142, 148 sense a voltage indicative of the impedance encountered by the injected current. The voltage passes through a series of electrical circuits featuring analog filters and differential amplifiers. These filter and amplify selected components of the ECG, IPG, and BR waveforms, respectively. Both the IPG and BR waveforms have low frequency (DC) and high frequency (AC) components that are further filtered and processed to measure different impedance waveforms, as explained in more detail below and in the references cited herein. The IPG waveform is sensitive to both phase and amplitude changes imposed on the injected current by capacitive changes (e.g., those induced by respiratory events) and conductive changes (e.g., those induced by changes in fluid and blood flow). The BR waveform is primarily sensitive to phase changes imposed on the injected current induced by these same components.

[0139] The use of cable 134 to connect central sensing / electronics module 130 and optical sensor 136 allows the electrode leads (141, 142 in central sensing / electronics module 130, 147, 148 in secondary battery 157) to be separated by a relatively long distance when patch sensor 30 is attached to the patient's chest. For example, secondary battery 157 can be attached near the patient's left shoulder. Such separation between electrode leads 141, 142, 147, 148 improves the signal-to-noise ratio of ECG, IPG, and BR waveforms measured by patch sensor 30, since these waveforms are typically determined from differences in bioelectrical signals collected by single-use electrodes, which typically increase with electrode separation. Finally, separation of electrode leads improves the accuracy of any physiological parameters detected from these waveforms, such as HR, HRV, RR, BP, SV, CO, and FLUIDS.

[0140] The acoustic module 146 features a solid-state acoustic microphone, which is typically a thin piezoelectric disk surrounded by a foam substrate. The foam substrate contacts the patient's chest during measurements and couples sounds from the patient's heart to the piezoelectric disk, which then measures the heart sounds from the patient. A plastic enclosure encapsulates the entire acoustic module 146.

[0141] Heart sounds are typically "lub / dub" sounds heard from the heart with a stethoscope that indicate when the underlying mitral and tricuspid (herein "S1" or "lub" sound) and aortic and pulmonary (herein "S2" or "dub" sound) valves close (Note: no detectable sounds are made when the valves open). With signal processing, the heart sounds result in a PCG waveform that is used with other signals to determine BP, as described in more detail below. In other embodiments, 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.

[0142] The optical sensor 136 features an optical system 160 including an array of photodetectors 162 arranged in a circular pattern surrounding an LED 161 that emits radiation in the red and infrared spectral regions. During measurement, the red and infrared radiation emitted sequentially from the LED 161 illuminates and reflects from the underlying tissue in the patient's chest and is detected by the array of photodetectors 162. The detected radiation is modulated by blood flowing through capillary beds in the underlying tissue. Processing the reflected radiation with electronics in the central sensing / electronics module 130 results in a PPG waveform corresponding to the red and infrared radiation, which is used to determine BP and SpO2, as described below.

[0143] The exterior surface of the optical sensor 136 is covered by a heating element, which features a thin Kapton® film 165 with embedded electrical conductors arranged, for example, in a serpentine pattern. Other patterns of electrical conductors can also be used. The Kapton® film 165 features cutouts that pass radiation emitted by the LED 161 and detected by the photodetector 162 after it reflects off the patient's skin. A tab portion 167 on the thin Kapton® film 165 is folded back so that it can be plugged into a circuit board in the patch sensor 30. In use, software running on the patch sensor 30 controls the power management circuitry on the circuit board to apply a voltage to the embedded conductors in the thin Kapton® film 165, thereby passing a current through them. The resistance of the embedded conductors causes the film 165 to gradually heat and 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 integrates with the power management circuitry, allowing the software to operate in a closed-loop manner to carefully control and regulate the applied temperature. In this context, "closed-loop manner" means that the software analyzes the amplitude of the cardiac evoked pulses in the PPG waveform, and if necessary, increases the voltage applied to the Kapton® film 165, increasing its temperature, to maximize the cardiac evoked pulses in the PPG waveform. Typically, the temperature is adjusted at a level of 41-42°C, which has little effect on the underlying tissue and is considered safe by the U.S. Food and Drug Administration (FDA).

[0144] The patch sensor 30 typically also includes a 3-axis digital accelerometer and a temperature / humidity sensor (not specifically identified in the figure) to measure three time-dependent motion waveforms (along the x, y, and z axes), humidity, and TEMP values, respectively.

[0145] The patch sensor 30 typically samples the time-dependent waveforms at a relatively high frequency (e.g., 250 Hz). An internal microprocessor running firmware processes the waveforms using computational algorithms to generate vital signs and hemodynamic parameters at a frequency of approximately once per minute. Example algorithms are described in the following co-pending issued patents, the contents of which are previously incorporated by reference: "NECK-WORN PHYSIOLOGICAL MONITOR," U.S. Ser. No. 14 / 975,646, filed Dec. 18, 2015; "NECKLACE-SHAPED PHYSIOLOGICAL MONITOR," U.S. Ser. No. 14 / 184,616, filed Aug. 21, 2014; and "BODY-WORN SENSOR FOR CHARACTERIZING PATIENTS WITH HEART FAILURE," U.S. Ser. No. 14 / 145,253, filed Jul. 3, 2014.

[0146] The patch sensor 30 shown in FIG. 7 is designed to maximize comfort and reduce "cable clutter" when deployed on a patient while optimizing the ECG, IPG, BR, PPG, and PCG waveforms it measures and determines physiological parameters such as HR, HRV, BP, SpO2, RR, TEMP, FLUIDS, SV, and CO. A flexible rubber gasket 138 allows the sensor 30 to flex on the patient's chest, thereby improving comfort for both male and female patients. An added benefit of its chest-worn configuration is the reduction of motion artifacts that can distort waveforms and cause erroneous values ​​of vital signs and hemodynamic parameters to be reported. This is due in part to the fact that the chest typically moves less than the hands and fingers during daily activities, and the subsequent artifact reduction ultimately improves the accuracy of the parameters measured from the patient.

[0147] (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 hemodynamic parameters (FLUIDS, SV, CO) by collectively processing the time-dependent ECG, IPG, BR, PPG, PCG, and ACC waveforms, as shown in Figures 8A-E (Note: the BR and IPG waveforms have similar morphology, and therefore, for simplicity only, only the IPG waveform is shown in Figure 8D). The ECG, IPG, BR, PPG, and PPG waveforms are typically characterized by heartbeat-induced "pulses," which are indicated in the figures by dashed lines 170a, 170b. The temporal separation of the pulses is inversely proportional to HR, 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 such events change the capacitance and therefore the impedance in the patient's chest. In particular, Figure 8C features undulations indicated by dashed lines 180a, 180b with a separation that is inversely proportional to RR. Values ​​corresponding to these vital signs, i.e., HR and RR, can be used to inform a beat picker algorithm that is used to locate F0 and F1 in the frequency domain spectrum, as described in detail above.

[0148] During measurement, built-in firmware running on the patch sensor processes the pulses in these waveforms using a "beat-picking" algorithm, such as that described above, to determine fiducial markers corresponding to the characteristics of each pulse; these markers are then processed using additional algorithms described herein to determine vital signs and hemodynamic parameters.

[0149] For example, FIG. 8A shows an ECG waveform measured by a patch sensor described herein. It includes a heartbeat-induced QRS complex that informally marks the start of each cardiac cycle. Compared to other physiological waveforms, ECG waveforms typically have a relatively good signal-to-noise ratio and are easy to analyze with beat-picking algorithms, and therefore they are often used to measure HR, with the QRS complex serving as a "fiducial" marker for analyzing some of the more complex waveforms described below. FIG. 8B shows a PPG waveform measured by an optical sensor, showing the volumetric changes in the underlying capillaries caused by heartbeat-induced blood flow. As is well known in the art, the AC and DC components of the PPG waveform measured using optical radiation in the red (about 660 nm) and infrared (about 940 nm) can be collectively processed to determine a value of SpO2.

[0150] The IPG waveform includes both AC and DC components, where the DC component indicates the amount of fluid in the thorax by measuring the baseline electrical impedance, and the average value of Z0 is used to determine FLUIDS, as referenced above. The AC component, shown in FIG. 8C, tracks the blood flow in the thoracic vasculature and represents the pulsatile component of the IPG waveform. The time-dependent derivative of the AC component includes a well-defined peak indicating the maximum acceleration of blood flow in the thoracic vasculature. Both the AC and DC components can be processed together with a parameter called left ventricular ejection time (herein "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 valve, and as known in the art, it can be determined directly from the time-dependent derivative of the AC component, or alternatively, estimated from the HR value using a standard regression equation called the 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.

[0151] The PCG waveform shown in FIG. 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 pulmonary valves). The amplitude, timing, and frequency domain spectrum of S1 and S2 are known to be sensitive to BP. A motion waveform measured along a single axis by an accelerometer is shown in FIG. 8E. Motion waveforms are typically measured along the x, y, and z axes and can be used to characterize the degree and type of a patient's motion, and the patient's posture.

[0152] BP-related parameters can be determined by analyzing the time difference between features in different waveforms. For example, an algorithm running in firmware on the patch sensor can calculate the time interval between the QRS complex and a fiducial marker on each of the other waveforms. One such interval is the time separating the "foot" of the pulse in the PPG waveform (FIG. 8B) and the QRS complex (FIG. 8A), referred to herein as the pulse arrival time (PAT). PAT is inversely proportional to BP and systemic vascular resistance. Similarly, vascular transit time (herein "VTT") is the time difference between a fiducial marker in a waveform other than ECG, e.g., the S1 or S2 point of the pulse in the PCG waveform (FIG. 8D) and the foot of the PPG waveform (FIG. 8B). Or, the peak of the pulse in the waveform (FIG. 8C) and the foot of the PPG waveform (FIG. 8B). In general, any set of time-dependent fiducials determined from a waveform other than ECG can be used to determine VTT. Collectively, PAT, VTT, and other time-dependent parameters extracted from the pulses in the four physiological waveforms are referred to as "systolic time intervals" and are typically inversely proportional to BP.

[0153] Typically, BP measurement methods based on systolic time intervals indicate changes in BP, and they require calibration from a cuff-based system (e.g., manual auscultation or automated oscillometry) to determine absolute values ​​of BP. Typically, such calibration methods provide an initial BP value and a patient-specific relationship between BP and PAT / VTT. During cuff-free measurements, PAT / VTT values ​​are measured in a quasi-continuous manner and then combined with the BP and PAT / VTT values ​​determined during calibration to yield a quasi-continuous value of BP. Such calibration typically involves measuring the patient multiple times (e.g., 2-4 times) with a cuff-based BP monitor employing oscillometry while simultaneously collecting PAT and VTT values ​​such as those described above. Each cuff-based measurement yields a separate BP value. Calibrations typically last for about a day, after which they need to be repeated.

[0154] In an embodiment, one of the cuff-based BP measurements coincides with a "challenge event" that alters the patient's BP, such as gripping a hand grip, changing posture, or lifting the patient's leg. This adds variance in the calibration measurement, thereby improving the sensitivity of the post-calibration measurement to BP fluctuations. In other embodiments, a "universal calibration" (e.g., a single calibration for all patients) can be used for the BP measurements. In other embodiments, the BP measurements are left uncalibrated and only a relative measure of BP is calculated.

[0155] (Alternative patch sensor) The patch sensors described herein can have different form factors than those shown in FIG. 7. For example, FIGS. 9A-B show top and bottom images, respectively, of such an alternative embodiment. Like the patch sensor described in FIG. 7, the patch sensor 230 shown in FIGS. 9A-B features two main components: a central sensing / electronics module 252 that is mounted near the center of the patient's chest and features 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 features electrode leads 250a-d that incorporate circular magnets 251a-d that connect to magnetically active posts mated with single-use electrodes (not shown in the figures) during measurements. The single-use electrodes secure the central sensing / electronics module 252 to the patient's chest. In addition, electrode lead 250a serves as a "sense" electrode to detect bioelectrical signals that, after processing, result in ECG, IPG, and BR waveforms as described above. Similarly, electrode lead 250b serves as a "drive" electrode to inject high frequency, low amperage currents into the patient's chest for IPG and BR measurements. Electrode leads 250c-d, along with magnets 251c-d, do not perform any electrical function and are simply used to better secure the sensing / electronics module 252 to the patient's chest. To complete the ECG, IPG, and BR measurements, secondary module 254 includes a single sense electrode 256a and corresponding magnet 257a, and a single drive electrode 256b and corresponding magnet 257b, which form an electrode pair with sense electrode lead 250a and drive electrode lead 250b. As previously mentioned, IPG and BR waveforms can be measured at multiple frequencies ranging from about 5 to 1,000 KHz.

[0156] The patch sensor 230 shown in Figures 9A-9B includes a reflective optical sensor 274 featuring LEDs 272 that emit red and infrared wavelengths, like the one shown in Figure 7. A circular array of photodetectors 270 surrounds the LEDs 272. A thin Kapton® film 273 with embedded electrical traces surrounds the photodetectors 270 and LEDs 272 and generates heat when a voltage is applied, which gently warms the skin to 41°C-42°C using a closed loop system, thereby increasing perfusion and amplifying the corresponding PPG waveform.

[0157] The patch sensor 230 includes a thermally conductive metal post 264 that connects to a temperature sensor (not shown in the figure) and to the patient's skin during measurement. With this, the patch sensor 230 can measure the skin temperature. It is powered by a rechargeable Li-ion battery that can be charged through a small USB port 261 or, alternatively, with an embedded transformer that provides wireless charging. A simple on / off switch 260 powers the sensor 230. The sensor 230 lacks an acoustic sensor, which means that the sensor 230 cannot measure S1 and S2 as described above.

[0158] In other embodiments, the patch sensor 230 can have other form factors and can include additional sensors. For example, the secondary module 254 can include an acoustic sensor similar to the acoustic sensor (component 146) shown in Figure 7. The reflective optical sensor 274 can include other non-circular configurations of photodetectors and LEDs, such as the optical sensor (component 136) shown in Figure 7. For example, in embodiments, the photodetectors can be arranged in a linear, square, or rectangular array.

[0159] 10-14 show alternative embodiments of patch sensors according to the present invention along with time-dependent plots of the waveforms they measure. In these cases, the numbered components of each patch sensor have the same functions as those described in FIG. 1. For example, FIG. 10A shows an embodiment of a patch sensor 70 worn on the wrist of a patient 11. FIGS. 10B and 10c show the PPG and PVP-AC waveforms, respectively, measured by the patch sensor. Here, a wrist-worn wrap 82 includes a reflective optical sensor that measures the PPG waveform from the patient's wrist. FIG. 11A shows a similar embodiment of a patch sensor 70 in which only the optical sensor 210 is worn as a band around the thumb of the patient 11 and connects to a central processing unit 83 through a thin cable 112. For this embodiment, the PPG and PVP-AC waveforms measured by the sensor are shown in FIGS. 11B and 11C, respectively.

[0160] FIG. 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 connects to a central processing unit 83 through a thin cable 181 and measures the PCG waveform from acoustic sounds generated by blood pulsating through the underlying brachial artery. In this embodiment, like the one shown in FIG. 10A, the optical sensor is of the reflective type and measures the PPG waveform from the patient's wrist. The time-dependent PPG, PCG, and PVP-AC waveforms corresponding to this embodiment are shown in FIGS. 12B-12D, respectively.

[0161] Figure 13A shows another two-part patch sensor 70 according to the present invention, in which an electrode-containing secondary sensor 80 is placed near the shoulder of a patient 11 and connects to a central processing unit 83 through a cable 181. The electrode-containing secondary sensor 80 allows ECG and IPG / BR waveforms to be measured along the patient's brachial artery using a methodology similar to that described above. Figures 13B-13E show the ECG, PPG, ICG / BR, and PVP-AC waveforms, respectively, measured using this embodiment of the present invention.

[0162] FIG 14A shows yet another embodiment of patch sensor 70. Like FIG 13A, this embodiment also includes an electrode-containing secondary sensor 85, only in this case the secondary sensor 85 includes both electrodes and a phonocardiogram sensor that measures PPG waveforms from the underlying heart of patient 11. The time-dependent ECG, PPG, IPG / BR, PCG, and PVP-AC waveforms measured by patch sensor 70 are shown in FIGs 14B-14F, respectively.

[0163] (Algorithms for processing signals from both iPIVA and patch sensors) Figure 15A shows a flowchart 300 illustrating the steps used by the algorithm to process signals from both the iPIVA and patch sensors described herein to determine parameters related to the patient's fluid status (e.g., wedge pressure, pulmonary artery pressure, blood volume, fluid status). Figures 15B-E show graphical plots corresponding to the different steps listed in flowchart 300.

[0164] The algorithm begins by explicitly determining HR / RR parameters using a patch sensor (step 320), as described above. For such measurements, as shown in FIG. 10B (derived directly from FIGS. 8A-C), 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 performs a PVP-AC time To generate a PVP-AC, the iPIVA sensor is used to collect PVP waveforms in the time domain (step 322). For this step, the algorithm may additionally include filtering algorithms (e.g., band-pass filtering) or other signal processing techniques (e.g., adaptive filters or averaging techniques, use of accelerometers or acoustic sensors to account for pump-induced movements and noise) to reduce or eliminate artifacts due to the pump. The signals are typically collected over a period of at least several minutes. The algorithm then generates a PVP-AC timeinto shorter time intervals (e.g., similar to the waveform snippets shown in Figures 6A-D), which time,segments (Step 324). time An example of PVP-AC is shown in FIG. time,segments is indicated by the region of the waveform time between dashed lines 340 in the figure. FIG. 15D shows the PVP-AC time,segments 4 shows a time-dependent plot of , which has features indicative of both cardiac and respiratory events.

[0165] Algorithm is PVP-AC time,segments After generating the PVP-AC, each partition is transformed into the frequency domain (e.g., using FFT, CWT, or DWT) and frequency,segments The algorithm then generates individual frequency domain partitions classified as PVP-AC frequency,segments We take the ensemble average of the set of PVP-AC frequency,segments,ave (Step 328). frequency,segments,ave Once F0 and F1 are determined, the algorithm uses the HR / RR values ​​determined independently by the patch sensor during step 320 (step 330) to inform a peak picking algorithm that identifies values ​​and energies corresponding to F0 and F1 (step 332). More specifically, the algorithm uses the HR / RR values ​​from the patch sensor as the "truth" and then incorporates these into a filter that prevents the algorithm from selecting false peaks in the frequency domain. Alternatively, during step 330, the HR / RR values ​​determined from the patch sensor can be used in an adaptive or comparable mathematical filter to remove false peaks and other features (e.g., associated with movement or noise) from the frequency domain spectrum, thereby making it easier to detect F0 and F1.

[0166] 15E shows plots of F0 (upper plot) and F1 (lower plot), in this case generated using a discrete wavelet transform. As is evident from the plots, the signal-to-noise ratios of both F0 and F1 determined using this approach are high, making it relatively easy to process the parameters associated with these fiducial markers.

[0167] Once F0 and F1 are selected, their frequencies are determined from their peak maxima and their energies are determined from their peak amplitudes or, alternatively, by integrating the area under the curve centered on the maximum peak amplitude (step 332). The algorithm then processes parameters corresponding to F0 and F1 or a combination thereof to determine parameters related to the patient's fluid status (step 334). A clinician can then use such parameters to treat the patient.

[0168] The algorithm depicted by step 334 of FIG. 15A can take several forms. For example, it can be a simple linear regression equation that converts parameters related to F0 and F1 measured using iPIVA (e.g., magnitude, mean, variability, phase, upslope, or downslope) into parameters related to the patient's fluid status (wedge pressure, blood volume, pulmonary artery pressure). Here, the constants of the linear regression (slope, y-intercept) are typically determined in advance using a clinical trial that simultaneously measures: 1) iPIVA using the system described herein; and 2) parameters related to the patient's fluid status using 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 by processing the information, which is then subsequently used with the iPIVA measurements to determine parameters related to the patient's fluid status. The constants of the linear regression can be grouped according to biometric parameters associated with the patient, such as the patient's weight, sex, or vital signs (e.g., HR, BP). In a related embodiment, the linear regression can be replaced with a more complex mathematical function, such as a polynomial, exponential, or nonlinear equation, whose parameters are previously determined using the approach described above and then used to convert the iPIVA values ​​into parameters related to the patient's fluid status.

[0169] Alternatively, machine learning approaches can be used to develop models that convert parameters related to F0 and F1 measured using iPIVA into those related to the patient's fluid status. One such machine learning approach is called a support vector machine (herein "SVM"). The approach is similar to that used here with linear regression, where data determined from clinical trials is used to build an SVM, which is then subsequently used to convert iPIVA parameters into something like cardiac wedge pressure. Other computational models that can be used in similar applications include Gaussian kernel functions, boosting ensembles, and bagging ensembles.

[0170] Other Alternatives In an embodiment of the present invention, an algorithm running on the iPIVA sensor can use the following steps to identify features associated with RR (i.e., F0) and HR (i.e., F1).

[0171] Step 1) Acquire the PVP waveform in the time domain and select the desired segment to be processed.

[0172] Step 2) Divide the desired section of the PVP waveform into 36-second segments and take the CWT of each segment.

[0173] Step 3) Identify the possible value of F0 for the CWT of each segment as the median of the frequencies associated with the greatest energy between 0 and 0.5 Hz. Then calculate the median F0 value for five consecutive segments, which becomes the working estimate of F0 for the following steps.

[0174] Step 4) Identify the median energy at the second, third, and fourth harmonics of F0 as determined in step 3. If the energy at the fourth harmonic is the highest of the three, then the fourth harmonic frequency is a candidate for F1.

[0175] Step 5) Find all local maxima from frequencies above the 4th harmonic of F0. For each maximum, count the number of other maxima with frequencies that are within 10% of a multiple of the frequency of that maximum. The maximum with the highest number of multiples is the final F1 for this section. 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.

[0176] Step 6) Find the frequency above the 4th harmonic of F0 with the largest corresponding energy (i.e., integrated area under the peak). This becomes the new candidate for F1. If a candidate F1 from step 4 also exists, compare the energies in the two candidate F1s and select the candidate F1 with the larger associated energy. If a candidate F1 from step 4 does not exist, a new candidate F1 is calculated as described in this step and becomes the final F1 for this partition.

[0177] Step 7) The median F1 from the previous five partitions becomes the working estimate of F1.

[0178] In embodiments, variations of this approach (eg, using an FFT or DWT instead of a CWT) may be used in conjunction with the steps recited above to determine the values ​​of F0 and F1.

[0179] In other embodiments of the present invention, the amplitude of one (or both) of the S1 or S2 heart sounds can be used to predict BP. This parameter typically increases in a linear fashion with the amplitude of the heart sounds. In embodiments, a universal calibration that accounts for this linear relationship can be used to convert the heart sound amplitude into a value of BP. The algorithm for determining BP can also be based on techniques that use machine learning or artificial intelligence, such as techniques that use SVM.

[0180] A calibration for BP measurements can be determined, for example, from data collected in a clinical trial conducted on a large number of subjects. Here, numerical coefficients describing the relationship between BP and heart sound amplitudes are determined by fitting data collected during the trial. These coefficients and linear algorithms are coded into the sensor for use during the actual measurement. Alternatively, a patient-specific calibration can be determined by measuring baseline blood pressure values ​​and corresponding heart sound amplitudes during a calibration measurement that precedes the actual measurement. The data from the calibration measurement can then be fitted as described above to determine a patient-specific calibration, which is then subsequently used to convert heart sounds to BP values.

[0181] Time and frequency domain analysis of the IPG, BR, and PCG waveforms can be used to distinguish respiratory events such as coughing, gasping, etc., and to measure respiratory tidal volume. In particular, respiratory tidal volume is determined by integrating the area under the "respiratory pulse" in the IPG or BR waveform (such as that shown in FIG. 8C) and then comparing this to a predefined calibration. Such events can be combined with information from the iPIVA sensor to help predict patient decompensation. In other embodiments, the present invention can use variations of the algorithms described above for determining vital signs and hemodynamic parameters. For example, to improve the signal-to-noise ratio of the pulses in the IPG, PCG, and PPG waveforms, the built-in firmware running on the patch sensor can operate a signal processing technique called "beat stacking." In beat stacking, for example, an average pulse is calculated from multiple (e.g., seven) consecutive pulses from the IPG waveform, which are separated by analysis of 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 seven sample window as an ensemble average and then used as described above.

[0182] In other embodiments, sensitive accelerometers can be used in place of acoustic sensors (e.g., in the patch sensor shown in Figures 9A-B) to measure small-scale oscillatory motion of the chest driven by the patient's underlying beating heart. Such waveforms are called oscilloscope cardiograms (SCGs) and can be used in place of (or in conjunction with) PCG waveforms to measure S1 and S2 heart sounds.

[0183] In other embodiments, signals from PIVA and iPIVA can be used to estimate conditions such as IV infiltration, extravasation, and IV occlusion, where changes in 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 can indicate that the IV catheter is sliding out of the patient's vein and into the surrounding tissue. Alternatively, a rapid increase in PVP combined with a rapid clearance of F0 and F1 can indicate that the IV catheter is occluded. In other embodiments, these signals can be used to monitor IV pump performance (e.g., flow rate) or whether the IV system is in a free-flowing state.

[0184] These and other embodiments of the invention are considered to be within the scope of the following claims.

Claims

1. 1. An intravenous ("IV") system for monitoring a patient, the system being configured to be positioned on a body of the patient, the system comprising: a wrist-worn housing including a flange at a first end of the wrist-worn housing and a cable section and a tubing section at a second end of the wrist-worn housing; a catheter configured to be inserted into the patient's venous system, the catheter in fluid communication with the flange of the arm-worn housing; a pressure sensor disposed within the arm-worn housing, the pressure sensor connected to the catheter and configured to measure a physiological signal indicative of pressure within the patient's venous system; and a physiological sensor configured to measure at least one of a heart rate and a respiratory rate of the patient; a motion sensor disposed within the wrist-worn housing, the motion sensor configured to measure a motion signal; and a processing system disposed within the wrist-worn housing; Equipped with The processing system includes: i) receiving the physiological signal from the pressure sensor; ii) receiving the motion signal from the motion sensor; and iii) receiving at least one of a heart rate and a respiratory rate of the patient from the physiological sensor; iv) filtering, amplifying and digitizing the physiological signal from the pressure sensor; and v) processing the motion signals by comparing the motion signals to a predetermined threshold to determine when the patient has a relatively low degree of motion, the predetermined threshold for motion corresponding to a vector magnitude of 0.1 G; vi) transforming the physiological signal into a frequency domain to generate a frequency domain signal; vii) when the processing system determines that the motion signal is below the predetermined threshold, collectively analyzing the frequency domain signal and at least one of the patient's heart rate and respiration rate from the physiological sensors to determine a respiration frequency, a heart rate frequency, an energy associated with the respiration frequency, and an energy associated with the heart rate frequency; A system configured to:

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

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

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

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

6. The system of claim 5 , wherein the processing system is configured to digitally filter the physiological signal with a high pass filter to generate a filtered signal.

7. The system of claim 6 , wherein the processing system is further configured to process the filtered signal to determine signal components indicative of a heart rate and a respiratory rate of the patient.

8. The system of claim 1 , wherein the processing system is configured to transform the physiological signal into the frequency domain using an FFT to generate a frequency domain signal.

9. The system of claim 1 , wherein the processing system is configured to transform the physiological signal into the frequency domain using a wavelet transform to generate a frequency domain signal.

10. 10. The system of claim 9, wherein the processing system is configured to transform the physiological signal into the frequency domain using one of a continuous and a discrete wavelet transform to generate a frequency domain signal.

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