Patent Application
20210401297
The invention described herein relates to systems for drug and fluid delivery, and to systems for monitoring patients in, e.g., hospitals and medical clinics.
Unless a term is expressly defined herein using the phrase “herein “______””, or a similar sentence, there is no intent to limit the meaning of that term beyond its plain or ordinary meaning. To the extent that any term is referred to in this document in a manner consistent with a single meaning, that is done for sake of clarity only; it is not intended that such claim term be limited to that single meaning. Finally, unless a claim element is defined by reciting the word “means” and a function without the recital of any structure, it is not intended that the scope of any claim element be interpreted based on the application of 35 U.S.C. § 112(f).
Proper care of hospitalized patients typically requires: 1) delivery of medications and fluids using intravenous (herein “IV”) catheters and infusion pumps; and 2) measuring vital signs and hemodynamic parameters with patient monitors. Typically, IV catheters insert in veins in the patient's hands or arms, and patient monitors connect to sensors or electrodes worn on (or inserted in) the patient's body.
Conventional patient monitors typically measure electrocardiogram (herein “ECG”) and impedance pneumography (herein “IP”) waveforms using torso-worn electrodes, from which they calculate heart rate (herein “HR”), heart rate variability (herein “HRV”), and respiration rate (herein “RR”). Most conventional monitors also measure optical signals, called photoplethysmogram (herein “PPG”) waveforms, with sensors that typically clip on the patient's fingers or earlobes. Such sensors can calculate blood oxygen levels (herein “SpO2”) and pulse rate (herein “PR”) from these PPG waveforms. More advanced monitors can also measure blood pressure (herein “BP”), notably systolic (herein “SYS”), diastolic (herein “DIA”), and mean (herein “MAP”) BP, typically using cuff-based techniques called oscillometry, or pressure-sensitive catheters that insert into a patient's arterial system called arterial lines. Digital stethoscopes, which can be either portable and body-worn devices, can measure phonocardiogram (herein “PCG) waveforms that indicate heart sounds and murmurs.
Some patient monitors are entirely body-worn. These typically take the shape of patches that measure ECG, HR, HRV and, in some cases, RR. Such patches can also include accelerometers that measure motion (herein “ACC”) waveforms. Algorithms can determine the patient's posture, degree of motion, falls, and other related parameters from the ACC waveforms. Patients typically wear these types of patches in the hospital or, alternatively, for ambulatory and home use. The patches are typically worn for relatively short periods of time (e.g., from a few days to several weeks). They are typically wireless, and usually include technologies such as Bluetooth® transceivers to transmit information over a short range to a secondary ‘gateway’ device, which typically includes a cellular or Wi-Fi radio to transmit the information to a cloud-based system.
Even more complex patient monitors measure parameters such as stroke volume (herein “SV”), cardiac output (herein “CO”), and cardiac wedge pressure using an invasive sensor called a Swan-Ganz or pulmonary-artery catheter. To make a measurement, these sensors are positioned in the patient's left heart, where they are ‘wedged’ into a small pulmonary blood vessel using a balloon catheter. As an alternative to this highly invasive measurement, patient monitors can use non-invasive techniques such as bio-impedance and bio-reactance to measure similar parameters. These methods deploy body-worn electrodes (typically deployed on the patient's chest, legs, and/or neck) to measure impedance plethysmogram (herein “IPG”) and/or bio-reactance (herein “BR”) waveforms. Analysis of IPG and BR waveforms yields SV, CO, and thoracic impedance, which is a proxy for fluids in the patient's chest (herein “FLUIDS”). Notably, IPG and BR waveforms generally have similar shapes and are sensed using similar measurement techniques, and are thus used interchangeably herein.
Devices that measure SV, CO, and FLUIDS can establish a patient's blood volume, fluid responsiveness, and, in some cases, related metrics such as central venous pressure (herein “CVP”). Taken collectively, these parameters can diagnose certain medical conditions and guide resuscitation efforts. But the highly invasive nature of Swan-Ganz and pulmonary-artery catheters can be disadvantageous and comes with a high risk of infection. Additionally, CVP measurements may be slower to change in response to certain acute conditions, such as when the circulatory system attempts to compensate for blood volume disequilibrium (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 may 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.
To address these and other shortcomings, a measurement technique called peripheral intravenous waveform analysis (herein “PIVA”) has been developed, as described in U.S. patent application Ser. No. 14/853,504 (filed Sep. 14, 2015 and published as U.S. Patent Publication No. 2016/0073959) and PCT Application No. PCT/US16/16420 (filed Feb. 3, 2016, and published as WO 2016/126856), the contents of which are incorporated herein by reference. These documents describe sensors featuring pressure transducers that receive signals from in-dwelling catheters inserted in a patient's venous system, and connect through cables to remote electronics that process signals generated therefrom (herein “PIVA sensor”). PIVA sensors measure time-dependent waveforms indicating peripheral venous pressure (herein “PVP”) using existing IV lines, which typically include IV tubing attached to a saline drip or infusion pump. Measurements made with PIVA sensors typically feature a mathematical transformation of the PVP waveforms into the frequency domain, performed with a remote computer, using a methodology called fast Fourier Transform (herein “ITT”). Analysis of a frequency-domain spectrum generated with an FFT can yield a RR frequency (herein “F0”) and a HR frequency (herein “F1”) indicating, respectively, the patient's HR and RR. A more detailed analysis of F0 and F1, e.g. use of a computer algorithm to determine the amplitude of these peaks or, alternatively, integrate an area underneath the curve centered around the maximum peak amplitude, determines the ‘energy’ of these features. Further processing of these energies yields an indication of a patient's blood volume status. Such measurements have been described, for example, in the following references, the contents of which are herein incorporated by reference: 1) Hocking et al., “Peripheral venous waveform analysis for detecting hemorrhage and iatrogenic volume overload in a porcine model.”, Shock. 2016 October; 46(4):447-52; 2) Sileshi et al., “Peripheral venous waveform analysis for detecting early hemorrhage: a pilot study.”, Intensive Care Med. 2015 June; 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 August; 24(8):525-532; and 4) Hocking et al., “Peripheral i.v. analysis (PIVA) of venous waveforms for volume assessment in patients undergoing haemodialysis.”, Br J Anaesth. 2017 Dec. 1; 119(6):1135-1140.
Unfortunately, during typical measurements with PIVA sensors, 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 magnitudes of corresponding signals in time-dependent PVP waveforms measured by conventional pressure transducers are often very weak (e.g. typically 5-50 □V). Additionally, PVP waveforms are typically amplified, conditioned, digitized, and ultimately processed with electronic systems located remotely from the patient. Thus, prior to these steps, analog versions of the waveforms travel through cables that can attenuate them and add noise (due, e.g., to motion). And in some cases, PVP waveforms simply lack signatures corresponding to F0 and F1. Or peaks of one primary frequency are obscured by ‘harmonics’ (i.e. integer multiple of a given frequency) of the other primary frequency. This can make it difficult or impossible for an automated medical device to accurately determine F0 and F1, and the energy associated with these features.
In view of the foregoing, it would be beneficial to improve a conventional PIVA sensor so that it overcomes historical problems related to weak, noisy PVP waveforms and inadequate detection of F0 and F1. Such as system could improve how patients are monitored in hospitals and medical clinics. To cure these and other deficiencies, described herein is an augmented, improved PIVA sensor (herein “iPIVA sensor”) featuring: 1) a circuit board located in close proximity to an in-dwelling venous catheter that amplifies, filters, and digitizes PVP waveforms immediately after a pressure sensor detects them (e.g. directly on the patient's body); and 2) a chest-worn physiological sensor (herein “patch sensor”) that makes accurate, independent measurements of vital signs, including HR and RR, which can assist in locating F0 and F1, and then processes these features to determine their corresponding energies. An iPIVA sensor according to the invention can include one or both of these improvements. Additionally, according to the invention, measurements from the iPIVA sensor can be coupled with independent measurements of hemodynamic parameters, e.g. SV, CO, and FLUIDS (which can be made with the patch sensor or a comparable patient monitor) to yield an improved understanding of the patient's fluid status. Ultimately the combination of these technologies—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 how patients are monitored and resuscitated in hospitals and medical clinics.
The iPIVA sensor described herein is designed to work with a conventional IV system, and connects to the patient with an in-dwelling catheter, both of which are standard equipment. The catheter includes a housing, worn close to or on the patient's body, and typically on their arm or hand, that encloses a signal-conditioning circuit board featuring complex circuitry that amplifies, filters, and digitizes analog PVP waveforms. The circuit board may also include components for processing and storing the digitized signals, measuring motion (e.g. an accelerometer and/or gyroscope), and wirelessly transmitting information (e.g. a Bluetooth® transmitter). In this way, the circuit board can integrate with a remote processor (e.g. server, gateway, tablet, smartphone, computer, infusion pump, or some combination thereof) that can collectively analyze PVP waveforms and complementary information from the patch sensor.
The iPIVA sensor described herein simplifies traditional measurements of vital signs and hemodynamic parameters, which can involve multiple devices and can take several minutes to accomplish. The remote processor—which wirelessly couples with both the iPIVA sensor and patch sensor—can additionally integrate with existing hospital infrastructure and notification systems, such as a hospital electronic medical records (herein “EMR”) system. Such a system can alarm and alert caregivers to changes in a patient's condition, thereby allowing them to intervene.
The patch sensor measures vital signs such as HR, HRV, RR, SpO2, TEMP, and BP, along with complex hemodynamic parameters such as SV, CO, and FLUIDS. Measurement of BP is typically cuffless and calibrated with a cuff-based device, such as one based on oscillometry. The patch sensor is typically a body-worn device that adheres to a patient's chest and continuously and non-invasively measures the above-mentioned parameters. The chest is an ideal location when such measurements are made on hospital-based patients: it is usually easily accessible, and a sensor placed there is typically unobtrusive, comfortable, and removed from the hands (which typically undergo relatively large amounts of motion). Because the patch sensor is small and therefore considerably less noticeable and obtrusive than various other patient-monitoring devices, emotional discomfort over wearing it can be reduced, thereby fostering long-term compliance, healing, and general patient well-being.
Alternatively, in place of the patch sensor, the system providing independent measurements of HR, RR, and hemodynamic parameters can be a conventional vital sign or hemodynamic monitor, such as the Starling™ SV patient monitor manufactured by Cheetah Medical based in Newton Center, Mass., USA.
The patch sensor can also include a motion-detecting accelerometer and gyroscope, from which it can determine motion-related parameters such as posture, degree of motion, activity level, respiratory-induced heaving of the chest, and falls. Such parameters could determine, for example, a patient's posture or movement during a hospital stay. The patch sensor can operate additional algorithms that process the motion-related parameters, allowing it to only measure vital signs and hemodynamic parameters when motion is minimized or below a predetermined threshold, thereby reducing artifacts. Moreover, the patch sensor estimates motion-related parameters such as posture to improve the accuracy of calculations for vital signs and hemodynamic parameters.
Disposable electrodes on a bottom surface of the patch sensor secure it to the patient's body without requiring bothersome cables. In embodiments, such electrodes easily connect to (and disconnect from) the sensor by means of magnets, thus allowing the sensor to easily snap back into proper position if it is removed. The patch sensor is typically lightweight, weighing about 20 grams. It is powered with a Li:ion battery that can be recharged with a conventional cable or using a wireless mechanism.
Given the above, in one aspect, the invention provides an IV system for monitoring a patient that is positioned on the patient's body. The IV system includes: 1) a catheter that inserts into the patient's venous system; 2) a pressure sensor connected to the catheter that measures physiological signals indicating a pressure in the patient's venous system; 3) a motion sensor that measures motion signals; and 4) a processing system that: i) receives the physiological signals from the pressure sensor; ii) receives the motion signals from the motion sensor; iii) processes the motion signals by comparing them to a pre-determined threshold value to determine when the patient has a relatively low degree of motion; and iv) process the physiological signals to determine a physiological parameter when the processing system determines that the motion signals are below the pre-determined threshold value.
In another aspect, the motion sensor is used to measure the patient's posture, as opposed to their motion, and the processing system determines the physiological parameter when the patient is in a pre-determined posture.
In another aspect, the invention provides an IV system for monitoring a patient that includes: 1) a catheter that inserts into the patient's venous system; 2) a pressure sensor connected to the catheter that measures physiological signals indicating a pressure in the patient's venous system; 3) a motion sensor that measures motion signals; and 4) a processing system that only transmits the physiological signals, or parameters calculated from these signals, when the motion signals fall below a pre-determined threshold.
In embodiments, the motion sensor is an accelerometer (e.g. a 3-axis accelerometer) and/or a gyroscope. In embodiments, the processing system calculates a motion vector by analyzing a motion signal corresponding to each axis of the 3-axis accelerometer. The pre-determined motion threshold used to determine if the patient's motion is too severe to make an accurate measurement typically corresponds to a vector magnitude of 0.1G. In other embodiments, the processing system compares the motion vector to a pre-determined look-up table to determine the patient's posture.
In other embodiments, the processing system digitally filters the signals (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/respiration rates. In embodiments, the processing system additionally processes the signal components indicating the patient's heart rate and respiration rate to determine a physiological parameter (e.g. wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary arterial pressure) indicating the patient's fluid status.
In embodiments, the processing system transforms the signals into the frequency domain to generate a frequency-domain signal prior to determining the physiological parameter. The method for the transform is typically an FFT, continuous wavelet transform, or a discrete wavelet transform.
In another aspect, the invention provides a system for monitoring a patient while simultaneously supplying IV fluids to the patient. The system features a housing positioned on the patient's body. The housing includes a catheter that inserts into the patient's venous system to supply the IV fluids, and a pressure sensor connected to the housing that measures time-dependent pressure signals indicating a pressure in the patient's venous system. The housing also includes a circuit system connected to the pressure sensor that receives the time-dependent signals it generates. The circuit system features: i) a differential amplifier that amplifies the time-dependent pressure signals to generate an amplified signal; ii) a low-pass filter that filters the amplified signal to generate a filtered signal, and iii) a secondary amplifier system that amplifies the filtered signal to generate a twice-amplified signal.
In embodiments, the differential amplifier, low-pass filter, and secondary amplifier can be positioned in any order within a circuit that differs from that described above.
In another aspect, the system additionally includes a processing system operating computer code that analyzes the twice-amplified signal to estimate a vital sign (e.g. HR, RR) corresponding to the patient. And in yet another aspect, the system additionally includes a wireless transmitter that transmits a digital representation of the vital sign to a remote receiver, and a power source that supplies power to the pressure sensor, circuit system, processing system, and wireless transmitter.
In embodiments, the IV system that includes a housing and completely encloses the circuit system and the pressure sensor, and attaches to the catheter. The catheter, for example, can be worn on the patient's hand or arm.
In embodiments, the differential amplifier features a gain of at least 10×. The low-pass filter typically separates out from the amplified signal a signal component containing heart rate and respiration rate components. The low-pass filter typically includes circuit components that generate a filter cutoff of between 10 and 30 Hz. In other embodiments, the circuit system additionally includes a high-pass filter that receives the twice-amplified signals and, in response, generates a twice-filtered signal. In this case, the high-pass filter typically includes circuit components that generate a filter cutoff of between 0.01 and 1 Hz.
In embodiments, the circuit system additionally includes a secondary low-pass filter that receives the twice-amplified signals and, in response, generates a thrice-filtered signal. In this case, the secondary low-pass filter typically includes circuit components that generate a filter cutoff of between 10 and 30 Hz.
In other embodiments, the circuit system additionally includes a motion sensor, such as an accelerometer or gyroscope. In other embodiments, the circuit system additionally includes a wireless transmitter, such as a Bluetooth®, Wi-Fi, or cellular transmitter. In other embodiments, the circuit system additionally includes a microprocessor that operates an algorithm to process the twice-amplified signal, or a signal derived therefrom. And in still other embodiments, the circuit system additionally includes a flash memory system that stores a digital representation of the twice-amplified signal or a signal derived therefrom.
In another aspect, the invention provides a system for monitoring a patient that includes a physiological sensor, connected to the patient, that features a bio-impedance and/or bio-reactance sensing element that measures a first set of parameters indicating the patient's fluid status. The system also includes an IV system featuring: 1) a catheter that inserts into the patient's venous system; 2) a pressure sensor that receives fluids from the catheter and, in response, measures a waveform indicating a pressure in the patient's venous system; and 3) a first processing system that receives the waveform and process it, or new signals derived from it, to estimate a second set of parameters indicating the patient's fluid status. A second processing system then receives the first and second sets of parameters, or a new parameter derived from them, and collectively process them to estimate a physiological parameter from the patient.
In another aspect, the invention provides a similar system, only the physiological sensor is worn on the patient. It includes the bio-impedance and/or bio-reactance sensing element and the first processing system.
In yet another aspect, the invention provides a system for monitoring a patient that includes: 1) a bio-impedance and/or bio-reactance sensing element connected to the patient that measures a first time-dependent waveform; 2) an IV system inserted in the patient's venous system featuring 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 process them to estimate a physiological parameter from the patient.
In embodiments, the second processing system is selected from the group consisting of a computer, tablet computer, and mobile phone. This system can operate an algorithm that compares the first set of parameters to the second set of parameters to estimate the physiological parameter. In other embodiments, the physiological sensor includes a first wireless transmitter, the IV system includes a second wireless transmitter, and the second processing system includes a third wireless transmitter. Here, the third wireless transmitter can wirelessly communicate with both the first and second wireless transmitters.
In other embodiments, the first set of parameters indicating the patient's fluid status are selected from a group including BP, SpO2, SV, stroke index, CO, cardiac index, thoracic impedance, FLUIDS, inter-cellular fluids, and extra-cellular fluids. In other embodiments, the second set of parameters are selected from a group including F0, F1, energies associated with F0 and F1, mathematical combinations of F0 and F1, and parameters determined from these.
The second processing system can operate a linear mathematical model to collectively process the first and second sets of parameters. Alternatively, it can operate an algorithm based on artificial intelligence to collectively process the first and second sets of parameters.
In embodiments, the physiological parameter estimated by the second processing system indicates the patient's fluid status. For example, the physiological parameter estimated can be one of the patient's blood volume, wedge pressure, and pulmonary arterial pressure.
In another aspect, the invention provides a system for monitoring a patient that includes: 1) a physiological sensor connected to the patient and featuring sensing elements that measure a first set of signals indicating the patient's physiology; 2) an IV system featuring: i) a catheter that inserts into the patient's venous system; and ii) a pressure sensor that senses fluids from the catheter and, in response, measures a second set of signals indicating a pressure in the patient's venous system; and 3) a processing system that receives the first and second sets of signals and collectively process them, or new signals derived from them, to estimate a physiological parameter indicating the patient's status.
In another aspect, the invention provides a similar system, only all the elements—the physiological sensor, the pressure sensor, and the processing system—are worn on the patient's body.
And in yet another aspect, the invention provides a system for monitoring a patient that features: 1) a physiological sensor worn on the patient's body with sensing elements that measure heart rate and/or respiration rate; 2) a catheter that inserts into the patient's venous system and collects a fluid; 3) a pressure sensor connected to the catheter that senses the fluid and, in response, measures signals indicating a pressure in the patient's venous system; and 4) a processing system that receives the value of heart rate and/or respiration rate from the physiological sensor, and collectively process this value and the signals indicating the pressure in the patient's venous system, or new signals derived from these, to estimate a physiological parameter indicating the patient's status.
In embodiments, the physiological sensor measures an ECG waveform, and then process this to determine a value of HR. The physiological sensor can also measure an IPG or BR waveform, and then process this to determine a value of RR. In these embodiments, both HR and RR represent the ‘first set of signals’, as used herein.
In embodiments, the pressure sensor measures a time-dependent pressure waveform indicating pressure in the patient's venous system; this represents the ‘second set of signals’, as used herein. The processing system can then be configured to process the time-dependent waveform with an algorithm (e.g., an algorithm for performing an FFT, continuous wavelet transform, or 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); it then processes F1 or a parameter estimated therefrom (e.g. its amplitude or corresponding energy, as described herein) to estimate the physiological parameter indicating 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); it then processes F0 or a parameter estimated therefrom (e.g. its amplitude or corresponding energy, as described herein) to estimate the physiological parameter indicating the patient's status. In yet another embodiment, both F0 and F1, or parameters derived therefrom, are collectively processed to estimate the physiological parameter indicating the patient's status. This parameter can be, e.g., wedge pressure, central venous pressure, pulmonary arterial pressure, blood volume, fluid volume, or a related value.
In another aspect, the invention provides an IV system for monitoring a patient that is positioned on the patient's body. The system features: 1) a catheter that inserts into the patient's venous system; 2) a pressure sensor connected to the catheter that measures signals indicating a pressure in the patient's venous system; and, 3) a processing system that receives the signals from the pressure sensor and, in response, process them to measure a physiological parameter.
In another aspect, the invention provides an IV system for monitoring a patient that is positioned on the patient's body. The system features: 1) a catheter that inserts into the patient's venous system; 2) a pressure sensor connected to the catheter that measure signals indicating a pressure in the patient's venous system; and, 3) a processing system that receives the signals from the pressure sensor and process them to determine signal components indicating either (or both) of the patient's heart rate and respiration rate.
In yet another aspect, the invention provides a system for monitoring a patient that is positioned on the patient's body. The system features: 1) a catheter that inserts into the patient's venous system and collects a fluid; 2) a pressure sensor connected to the catheter that senses the fluid and, in response, measure signals indicating a pressure in the patient's venous system; and, 3) a processing system that receives the signals from the pressure sensor and, in response, processes them to determine either (or both) of the patient's heart rate and respiration rate.
In embodiments, the processing system digitally filters the signals (e.g. with a digital high-pass filter, low-pass filter, and/or band-pass filter) to generate a filtered signal. It then processes the filtered signal to determine the patient's heart/respiration rate. In embodiments, the processing system additionally processes the signal components indicating the patient's heart rate and respiration rate to determine a physiological parameter (e.g. F0, F1, energy associated with F0, energy associated with F1, wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary arterial pressure) indicating the patient's fluid status.
In embodiments, the processing system transforms the signals into the frequency domain to generate a frequency-domain signal. The method for the transform is typically an FFT, continuous wavelet transform (herein “CWT”), or a discrete wavelet transform (herein “DWT”).
In embodiments, 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 signals from the pressure sensor. In other still other embodiments, the processing system additionally includes a motion sensor, such as an accelerometer or gyroscope. In other embodiments, the processing system additionally includes a wireless transmitter, such as a Bluetooth®, Wi-Fi, or cellular transmitter.
In another aspect, the invention provides an IV system that monitors a patient and is positioned in its entirety on the patient's body. The IV system includes: 1) a catheter that inserts into the patient's venous system; 2) a pressure sensor connected to the catheter that measures signals indicating pressure in the patient's venous system; and, 3) a circuit system that receives the signals from the pressure sensor. The circuit system features: i) a differential amplifier that amplifies the signals to generate an amplified signal; ii) a low-pass filter that filters the amplified signal to generate a filtered signal; and iii) a secondary amplifier system that amplifies the filtered signal to generate a twice-amplified signal.
In another aspect, the invention provides a similar IV system, also positioned in its entirety on the patient's body, that includes a catheter, pressure sensor, and circuit system similar to those described above. Here, the circuit system features: i) an amplifier that amplifies the signals to generate an amplified signal; ii) a filter that filters the amplified signal to generate a filtered signal; iii) a secondary amplifier system that amplifies the filtered signal to generate a twice-amplified signal; and iv) an analog-to-digital converter that digitizes the twice-amplified signal, or a signal derived therefrom.
In embodiments, the amplifiers, filters, and secondary filters described above can be arranged in any order within the circuit system.
In yet another aspect, the invention provides a system for monitoring a patient featuring a catheter that inserts into the patient's venous system, and a housing positioned in its entirety on the patient's body that encloses: 1) a pressure sensor configured to sense fluids from the catheter and, in response, measure pressure signals; and 2) a circuit system with circuit elements that amplify, filter, and digitize the pressure signals to identify the signal components indicating the patient's HR and RR.
In embodiments, the IV system includes a housing that completely encloses the circuit system and the pressure sensor, and attaches to the catheter. The housing, for example, can be worn on the patient's hand or arm. For example, it can be attached to these body parts using a band or adhesive.
In embodiments, the differential amplifier features a gain of at least 10×. The low-pass filter typically separates the amplified signal into a first amplified signal component containing components related to both HR and RR, and a second amplified signal component that lacks these components. The low-pass filter typically includes circuit components that generate a filter cutoff of between 10 and 30 Hz. In other embodiments, the circuit system additionally includes a high-pass filter that receives the twice-amplified signals and, in response, generates a twice-filtered signal. In this case, the high-pass filter typically includes circuit components that generate a filter cutoff of between 0.01 and 1 Hz.
In embodiments, the circuit system additionally includes a secondary low-pass filter that receives the twice-amplified signals and, in response, generates a thrice-filtered signal. In this case, the secondary low-pass filter typically includes circuit components that generate a filter cutoff of between 10 and 30 Hz.
In other embodiments, the circuit system additionally includes a motion sensor, such as an accelerometer or gyroscope. In other embodiments, the circuit system additionally includes a wireless transmitter, such as a Bluetooth®, Wi-Fi, or cellular transmitter. In other embodiments, the circuit system additionally includes a microprocessor that operates an algorithm to process the twice-amplified signal, or a signal derived therefrom. And in still other embodiments, the circuit system additionally includes a flash memory system that stores a digital representation of the twice-amplified signal or a signal derived therefrom.
Advantages of the invention should be apparent from the following detailed description, and from the claims.
Although the following text sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the invention described herein is defined by the words of the claims set forth at the end of this patent. The detailed description is to be construed as exemplary only; it does not describe every possible embodiment, as this would be impractical, if not impossible. One of ordinary skill in the art could implement numerous alternate embodiments, which would still fall within the scope of the claims.
Referring to
Both the iPIVA sensor 15 and iPIVA physiological sensor 70 are tightly coupled and integrated within the IV system 19. It is the combination of these components, along with the collective analysis of the information they measure (e.g. by the remote processor), that is the focus of the invention described herein. More specifically, during a 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 PVP waveforms that, with processing, yield F0 and F1. Digital versions of these data sets flow to the remote processor 36 for follow-on processing. For example, in embodiments, the remote processor 36 analyzes the digitized PVP waveforms and calculates their frequency-domain transform-using techniques such as FFTs, CWTs, and DWTs—to yield a frequency-domain spectrum. It then uses HR and RR values from the iPIVA physiological sensor 70 to detect F0 and F1 from the frequency-domain spectrum, and then determines the associated energies of these features, to estimate a parameter indicating a patient's fluid status (e.g. wedge pressure). In embodiments, energies associated with F0 and F1, along with measurements from the iPIVA physiological sensor, can be used to estimate other parameters related to the patient's fluid status, such as pulmonary arterial pressure and blood volume, as described in more detail below with reference to
The IV system 19 features a bag 16 containing pharmaceutical compounds and/or fluid (herein “medication” 17) for the patient. The bag 16 connects to an infusion pump 12 through a first tube 14. A standard IV pole 28 supports the bag 16, the infusion pump 12, and the remote processor 36. A display 13 on the front panel of the infusion pump 12 indicates the type of medication delivered to the patient, its flow rate, measurement time, etc. Medication 17 passes from the bag 16 through the first tube 14 and into the infusion pump 12. From there, it is metered out appropriately, and passes through a second tube 18, through a connector 58 and cable segment 42, into the arm-worn housing 20, and finally through the venous catheter 21 and into the patient's venous system 23. The arm-worn housing 20 is typically affixed to the patient's arm or hand, e.g. using an adhesive such as medical tape or a disposable electrode.
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 portion of it. In this way, the IV system 19 supplies the medication 17 to the patient's venous system 23 while the iPIVA sensor 15 and iPIVA physiological sensor 70, which features a pressure-measuring system and described in more detailed below, simultaneously measures signals related to the patient's PVP, vital signs, and hemodynamic parameters.
Importantly, and as described in more detail below, the arm-worn housing 20 is designed so that it is in constant ‘fluid connection’ with the patient's circulatory system (and particularly the venous system) while being deployed close to (or directly on) the patient's body. It features electronic systems for measuring analog pressure signals within the patient's venous system to generate PVP waveforms, and then amplifying and filtering these to optimize their signal-to-noise ratios. An analog-to-digital converter within the arm-worn housing digitizes the analog PVP waveforms prior to transmitting them through the cable, thereby minimizing any noise (caused, e.g., by the cable's motion) that would normally affect transmitted analog signals and ultimately introduce inaccuracies into values of F0 and F1 (and their associated energies) measured downstream. Notably, this design provides a relatively short conduction path between where the PVP waveforms are first detected and then processed and digitized; ultimately this results in signals that are more likely to yield highly accurate values of wedge pressure (and in embodiments pulmonary arterial pressure (and particularly the diastolic component on this pressure), blood volume and other fluid-related parameters).
Referring to
The circuit board 62 additionally includes sets of metal-plated holes that support a 4-pin connector 69, two 6-pin connectors 77, 78, and a 3-pin connector 79. More specifically, connector 69 connects directly to the pressure transducer, where it receives a common ground signal and analog PVP waveforms representing pressure in the patient's venous system. These waveforms are filtered and digitized as described in more detail, below. Through the connector 79 the circuit board receives power (+5V, +3.3V, and ground) from an external power supply, e.g. a battery or power supply located in the arm-worn housing. These power levels may be different in other embodiments of the invention. Digital signals and a corresponding ground from the analog-to-digital converter 68 are terminated at connector 78; they leave the circuit board 62 at this point, e.g. through cable segment 37 shown in
In embodiments, the circuit board 62 additionally includes components for processing, storing, and transmitting data that are 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 embodiments, the microprocessor or microcontroller thereon can operate computer code to process PVP-AC, PVP-DC, ECG, PCG, PPG, IPG, BP, and other time-dependent waveforms from both the iPIVA sensor and iPIVA physiological sensor to determine vital signs (e.g. HR, HRV, RR, BP, SpO2, TEMP), hemodynamic parameters (CO, SV, FLUIDS), components of PVP waveforms (e.g. F0, F1, and amplitudes and energies associated thereto), and associated parameters (e.g. wedge pressure, central venous pressure, blood volume, fluid volume, and pulmonary arterial pressure) related to the patient's fluid status. “Processing” by the microprocessor in this way, as used herein, means using computer code or a comparable approach to digitally filter (e.g. with a high-pass, low-pass, and/or band-pass filter), transform (e.g. using FFT, CWTs, and/or DWTs), mathematically manipulate, and generally process and analyze the waveforms and parameters and constructs derived therefrom with algorithms known in the art. Examples of such algorithms include those described in the following co-pending and issued patents, the contents of which are incorporated herein 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.
In related embodiments, the circuit board can include both flash memory and random access memory for storing time-dependent waveforms and numerical values, either before or after processing by the microprocessor. In still other embodiments, the circuit board can include Bluetooth® and/or Wi-Fi transceivers for both transmitting and receiving information.
Referring again to
PVP waveforms measured with the system described herein feature signal components that relate to heartbeat and respiratory events that may vary 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 a signal component that changes rapidly in time as the signal evolves.
More specifically, PVP waveforms typically have signal levels in the 5-50 □V range, a relatively weak amplitude that can be difficult to process. Such signals have been described previously (e.g. in U.S. patent application Ser. No. 16/023,945 (filed Jun. 29, 2018 and published as U.S. Patent Publication 2019/0000326); U.S. patent application Ser. No. 14/853,504 (filed Sep. 14, 2015 and published as U.S. Patent Publication No. 2016/0073959), and PCT Application No. PCT/US16/16420 (filed Feb. 3, 2016, and published as WO 2016/126856)). The contents of these pending patent applications have been previously incorporated herein by reference. In a conventional PIVA measurement, as described in these documents, PVP waveforms are measured with a pressure sensor proximal to the patient that generates analog signals; these typically pass through a relatively long cable, and are amplified, filtered, and digitized with a system located remotely from the patient. Additionally, conventional PIVA sensors, such as those previously disclosed, typically include transformation of the PVP waveforms into the frequency domain (typically using, e.g., a FFT), and then attempt to identify F0 (indicating a frequency related to RR) and F1 (indicating a frequency related to HR) without any secondary determination of these parameters. Energies associated with F0 and F1 are then analyzed to estimate other metrics (e.g. wedge pressure, pulmonary arterial pressure) related to the patient's fluid status. However, because PVP waveforms are so weak and characterized by low signal-to-noise ratios, they can be extremely difficult to measure. Additionally, when transformed into the frequency domain, signal components related to 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 delineate and explicitly measure. These and other factors may ultimately complicate the determination of parameters determined from energies associated with F0 and F1, e.g. the patient's fluid status.
The current invention attempts to cure these deficiencies in measuring PVP waveforms, and ultimately the energies associated with F0 and F1, by: 1) amplifying, filtering, digitizing, and in some cases processing PVP waveforms immediately after they are sensed by the pressure transducer (as opposed to first passing analog signals through a long, noise-inducing cable) to improve their signal-to-noise ratio and create a digital representation of them that is immune to cable-induced noise; 2) simultaneously and independently measuring HR and RR with an external iPIVA physiological sensor, which is tightly integrated with the iPIVA sensor; and 3) collectively processing the amplified/filtered/digitized PVP waveforms with HR and RR measurements from the iPIVA physiological sensor to better determine the energies associated with F0 and F1. Additionally, other measurements from the iPIVA physiological sensor, such as BP, SV, CO, and FLUIDS, and be combined with measurements from the iPIVA sensor to better determine the patient's fluid status, thereby improving their care within a hospital.
More specifically, the circuit described by the schematic 100 is designed to serially perform the following function on incoming PVP waveforms:
Incoming PVP Waveforms
1) Amplify the signal with 100× gain using a zero-drift amplifier
2) Differentially amplify the signal with an additional 10× gain
3) Filter the amplified signals with a 25 Hz, 2-pole low-pass filter
This first portion of the circuit provides roughly 1000× combined gain for the incoming PVP waveforms, thereby amplifying the input signal (which is typically in the □V range) to a larger signal (in the mV range). The follow-on low-pass filter removes any high-frequency noise. Ultimately these steps facilitate processing of both the PVP-AC and PVP-DC waveforms, as described below.
In the descriptions provided herein, the term ‘differentially amplify’ refers to a process wherein the circuit measures the difference between positive (P_IN in
Likewise, the term ‘zero-drift amplifier’ refers to an amplifier that: 1) internally corrects for temperature and other forms of low-frequency signal error; 2) has very high input impedance; and 3) has very low offset voltages. The incoming signal received by a zero-drift amplifier is typically extremely small, meaning it can be subject to interference, gain shifts, or the amplifier inputs bleeding out generated current; the zero-drift architecture of the amplifier helps reduce or eliminate this.
After processing the input PVP waveforms, the circuit described by the schematic 100 is designed to serially perform the following function on PVP-AC and PVP-DC waveforms:
PVP-AC Waveforms Only
1) Filter the signal with a 0.1 Hz, 2-pole high-pass filter
2) Filter the signal with a 15 Hz, 2-pole low-pass filter
3) Amplify the signal with 50× gain
PVP-DC Signal Only
1) Filter the signal with a 0.07 Hz, 2-pole low-pass filter
2) Filter the signal with a 0.13 Hz, 2-pole low-pass filter
3) Amplify the signal with 10× gain
Both PVP-AC and PVP-DC Waveforms
1) Digitize the signals with a 16-bit, 200 Ksps Delta-Sigma analog-to-digital converter
With this level of digital signal processing, the circuit board 62 can process PVP waveforms directly on the patient's body, and more specifically signals associated with respiration rate (F0) and heart rate (F1). It performs these functions without having to send signals through an external cable, which is an approach that can add noise and other signal artifacts and thus negatively impact measurement of F0, F1, and their associated energies as described above.
As appreciated by those skilled in the art, the circuit elements 102, 104, and 106 shown in
Such circuit elements 102, 104, and 106 are typically fabricated on a small, fiberglass circuit board, such as that shown in
Importantly and as described above, the analog signal processing indicated in
As shown in
Once measured as described above, a processor analyzes PVP waveforms to determine F0, F1, and their associated energies.
While signal components associated with F1 are readily apparent in
A clear example of this is shown in a third 1-minute waveform snippet selected over 1310-1370 seconds from the PVP-AC waveform shown in
Features associated with F0 and F1 (e.g. their amplitude or energy) may be processed in different ways to estimate fluid-related parameters, e.g. wedge pressure and/or pulmonary arterial 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 have been already incorporated herein by reference:
Parameters such as wedge pressure—as determined with both an iPIVA sensor and iPIVA physiological sensor working in concert as described herein—typically indicate the patient's fluid status, and are thus useful in managing the patient's care and resuscitating them. These parameters can be useful in the case of certain afflictions that may 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 an intensive care unit with IV fluids and antibiotics, both of which are typically administered as soon as the condition is detected. Fluids are typically replaced so that blood pressure is maintained. Indeed, properly treating patients with fluid-related illnesses like sepsis can mean the difference between life and death. The risk of death from sepsis is as high as 30%, from severe sepsis as high as 50%, and from septic shock as high as 80%. Estimates suggest sepsis affects millions of people a year; in the developed world, approximately 0.2 to 3 people per 1000 are affected by sepsis yearly, resulting in about a million cases per year in the United States.
Measurements from the iPIVA physiological sensor that directly relate to a patient's fluid status—e.g. BP, FLUIDS, SV, and CO—may complement a parameter like wedge pressure and assist in managing a patient suffering from a condition like sepsis. Sensors that measure such parameters typically deploy bio-impedance and bio-reactance measurements, 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 Ser. No. 62/845,097 (filed May 8, 2019) and U.S. patent application Ser. No. 16/044,386 (filed Jul. 24, 2018).
In general, and referring again to
The central processing unit 83 features a microprocessor that operates algorithms to process the waveforms, ultimately yielding parameters such as HR, HRV, RR, BP, SpO2, TEMP, SV, CO, FLUIDS. Once a measurement is complete, both the iPIVA sensor 15 and iPIVA physiological sensor transmit information (through wired and/or wireless means) to the remote processor 36, which includes a microprocessor and a display component 38. Algorithms operating through computer code running on the microprocessor in the remote processor 36 process signals from both the patch sensor 30 and iPIVA sensor 15 to determine the patient's vital signs and fluid status. For example, and 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’ of F0 and F1 values (corresponding, respectively, to RR and HR) measured by the iPIVA sensor 15. The algorithm then determines corresponding energies of F0 and F1, and finally processes these energies to determine the patient's fluid status. Such an algorithm is indicated by the flow chart shown in
Another embodiment of the algorithm may collectively process parameters measured by the iPIVA sensor 15 (e.g. wedge pressure and blood volume, which may be correlates with energies associated with F0, F1, or some combination thereof) with those measured by the iPIVA physiological sensor 70 (e.g. BP, SpO2, FLUIDS, SV, and CO) to determine the patient's fluid status and effectively inform delivery of fluids while resuscitating the patient (e.g. during periods of sepsis and/or fluid overload). In general, by using information from both the iPIVA sensor 15 and iPIVA physiological sensor 70, a clinician can better manage the patient 11 by characterizing life-threatening conditions and help guide their resuscitation.
As a more specific example, in embodiments values of BP and SpO2 measured by the iPIVA physiological sensor can be combined with volume status determined from the iPIVA sensor to estimate a patient's blood flow and perfusion. Knowledge of these parameters, in turn, can inform estimation of how much fluid a clinician needs to deliver upon resuscitation. Similarly, 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 a patient's level of perfusion. They can also be combined in a mathematical ‘index’ to better estimate this condition. Then these parameters or the index can be measured while the patient undergoes a technique called a ‘passive leg raise’, which is a test to evaluate the need for further fluid resuscitation in a critically ill person. The passive leg raise involves raising a patient's legs (typically without their active participation), which causes gravity to pull blood from the legs into the central organs, thereby increasing circulatory volume available to the heart (typically called ‘cardiac preload’) by around 150-300 milliliters, depending on the amount of venous reservoir. If the above-mentioned parameters or index measured by the iPIVA and patch sensors increase, this can indicate that the leg raise effectively increase perfusion in the patient's central organs, thereby indicating that they will be responsive to fluids. Clinicians can perform a similar test by providing the patient a bolus of fluids through an IV system, and then monitoring the increase or decrease in the parameters or index measured by the iPIVA and patch sensors.
In embodiments, simple linear computational methods, combined with results from clinical studies, can be used to develop models that collectively process data generated by the iPIVA sensor and iPIVA physiological sensor. In other embodiments, more sophisticated computational models, such as those involving artificial intelligence and/or machine learning, can be used for the collective processing.
The iPIVA physiological sensor 70 measures ECG, PPG, PCG, IPG, and BR waveforms from a patient, and from these calculates vital signs (HR, HRV, SpO2, RR, BP, TEMP) and hemodynamic parameters (FLUIDS, SV, and CO) as described in detail below. Once this information is determined, the patch sensor 30 wirelessly transmits it to a remote monitor so that it can be analyzed with information from the iPIVA sensor to characterize the patient.
The iPIVA physiological sensor 70 shown in
The electrode leads 141, 142, 147, 148 connect to a single-use electrode (not shown in the figure) and form two ‘pairs’ of leads, wherein one of the leads 141, 147 in each pair injects electrical current to measure IPG and BR waveforms, and the other leads 142, 148 in each pair sense bio-electrical signals that 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 a single-use electrode (also not shown in the figure), but serve no electrical function (i.e. they do not measure bio-electrical signals) and only help secure the patch sensor 30 to the patient.
IPG and BR measurements are made when the current-injecting electrodes 141, 147 inject high-frequency (e.g. 100 kHz), low-amperage (e.g. 4 mA) current into the patient's chest. In embodiments, the injected current can be sequentially adjusted to have a range of frequencies (e.g. 5-1000 kHz). In particular, low-frequency measurements (e.g. 5 kHz) typically do not penetrate cellular walls within the patient's body, and are therefore particularly sensitive to fluids disposed outside these walls, i.e. extra-cellular fluids.
The electrodes 142, 148 sense a voltage that indicates the impedance encountered by the injected current. The voltage passes through a series of electrical circuits featuring analog filters and differential amplifiers. These, respectively, filter and amplify select components of the ECG, IPG, and BR waveforms. Both the IPG and BR waveforms have low-frequency (DC) and high-frequency (AC) components that are further filtered and processed, as described in more detail below and in the references cited herein, to measure different impedance waveforms. The IPG waveform is sensitive to both phase and amplitude changes imparted on the injected current by capacitive changes (e.g. those induced by respiratory events), and conductive changes (e.g. those induced by changes in, e.g. fluids and blood flow). The BR waveform is primary sensitive to phase changes imparted on the injected current induced by these same components.
Use of a cable 134 to connect the central sensing/electronics module 130 and the optical sensor 136 allows the electrode leads (141, 142 in the central sensing/electronics module 130; 147, 148 in the secondary battery 157) can be separated by a relatively large distance when the patch sensor 30 is attached to a patient's chest. For example, the secondary battery 157 can be attached near the patient's left shoulder. Such separation between the electrode leads 141, 142, 147, 148 typically improves the signal-to-noise ratios of the ECG, IPG, and BR waveforms measured by the patch sensor 30, as these waveforms are determined from difference of bio-electrical signals collected by the single-use electrodes, which typically increases with electrode separation. Ultimately, the separation of the electrode leads improves the accuracy of any physiological parameter detected from these waveforms, such as HR, HRV, RR, BP, SV, CO, and FLUIDS.
The acoustic module 146 features a solid-state acoustic microphone that typically is a thin, piezoelectric disk surrounded by foam substrates. The foam substrates contact the patient's chest during the measurement, and couple sounds from the patient's heart into the piezoelectric disk, which then measures heart sounds from the patient. A plastic enclosure encloses the entire acoustic module 146.
The heart sounds are the ‘lub/dub’ sounds typically heard from the heart with a stethoscope: they indicate when the underlying mitral and tricuspid valves (herein “S1”, or ‘lub’ sound) and aortic and pulmonary valves (herein “S2”, or ‘dub’ sound) close (note: no detectable sounds are generated when the valves open). With signal processing, the heart sounds yield a PCG waveform that is used along with other signals to determine BP, as is 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.
The optical sensor 136 features an optical system 160 that includes an array of photodetectors 162, arranged in a circular pattern, that surround a LED 161 that emits radiation in the red and infrared spectral regions. During a measurement, sequentially emitted red and infrared radiation from the LED 161 irradiates and reflects off 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 PPG waveforms corresponding to the red and infrared radiation, which are used to determine BP and SpO2, as described below.
The outer surface of the optical sensor 136 is covered by a heating element featuring a thin Kapton® film 165 with embedded electrical conductors arranged, e.g., in a serpentine pattern. Other patterns of electrical conductors can also be used. The Kapton® film 165 features cut-out portions that pass radiation emitted by the LED 161 and detected by the photodetectors 162 after it reflects off the patient's skin. A tab portion 167 on the thin Kapton® film 165 folds over so it can plug into the circuit board within the patch sensor 30. During use, software operating on the patch sensor 30 controls power-management circuitry on the circuit board to apply a voltage to the embedded conductors within the thin Kapton® film 165, thereby passing electrical current through them. Resistance of the embedded conductors causes the film 165 to gradually heat up 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 adjust the applied temperature. Here, ‘closed-loop manner’ means that the software analyzes amplitudes of heartbeat-induced pulses the PPG waveforms, and, if necessary, increases the voltage applied to the Kapton® film 165 to increase its temperature and maximize the heartbeat-induced pulses in the PPG waveforms. Typically, the temperature is regulated at a level of between 41-42° C., which has minimal affect on the underlying tissue and is considered safe by the U.S. Food and Drug Administration (FDA).
The patch sensor 30 also typically includes a three-axis digital accelerometer and a temperature/humidity sensor (not specifically identified in the figure) to measure, respectively, three time-dependent motion waveforms (along x, y, and z-axes), humidity and TEMP values.
The patch sensor 30 typically samples time-dependent waveforms at relatively high frequencies (e.g. 250 Hz). An internal microprocessor running firmware processes the waveforms with computational algorithms to generate vital signs and hemodynamic parameters with a frequency of about once every minute. Examples of algorithms are described in the following co-pending and issued patents, the contents of which have already been incorporated herein 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.
The patch sensor 30 shown in
The patch sensor described above determines vital signs (HR, RR, SpO2, TEMP) and hemodynamic parameters (FLUIDS, SV, CO) by collectively processing time-dependent ECG, IPG, BR, PPG, PCG, and ACC waveforms, as shown in
During a measurement, embedded firmware operating on the patch sensor processes pulses in these waveforms, like those described above, with ‘beatpicking’ algorithms to determine fiducial makers corresponding to features of each pulse; these markers are then processed with additional algorithms, described herein, to determine vital signs and hemodynamic parameters.
For example,
The IPG waveform includes both AC and DC components: the DC component indicates the amount of fluid in the chest by measuring baseline electrical impedance; the average value of Z0 is used to determine FLUIDS, as referenced above. The AC component which is shown in
The PCG waveform shown in
Parameters related to BP can be determined by analyzing the time difference between features in different waveforms. For example, algorithms operating in firmware on the patch sensor can calculate time intervals between the QRS complex and fiducial markers on each of the other waveforms. One such interval is the time separating a ‘foot’ of a pulse in the PPG waveform (
Typically, BP-measurement methods based on systolic time intervals indicate changes in BP; 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 initial BP values and patient-specific relationships between BP and PAT/VTT. During a cuffless measurement, the PAT/VTT values are measured in a quasi-continuous manner, and then combined with the values of BP and PAT/VTT determined during calibration to yield quasi-continuous values of BP. Such calibrations typically involve measuring the patient multiple (e.g. 2-4) times with a cuff-based BP monitor employing oscillometry, while simultaneously collecting PAT and VTT values like those described above. Each cuff-based measurement results in separate BP values. Calibrations typically last about 1 day before they need to be repeated.
In embodiments, one of the cuff-based BP measurements is coincident with a ‘challenge event’ that alters the patient's BP, e.g. squeezing a handgrip, changing posture, or raising their legs. This imparts variation in the calibration measurements, thereby improving sensitivity of the post-calibration measurements to BP swings. In other embodiments, a ‘universal calibration’ (e.g. a single calibration for all patients) can be used for the BP measurement. In other embodiments, the BP measurement is left uncalibrated, and only relative measurements of BP are calculated.
The patch sensor described herein can have a form factor that differs from that shown in
The patch sensor 230 shown in
The patch sensor 230 includes a thermally conductive metal post 264 that connects to a temperature sensor (not shown in the figure) and the patient's skin, during a measurement. With this, the patch sensor 230 can measure skin temperature. It is powered by a rechargeable Li:ion battery that can be charged through a small-scale USB port 261, or alternatively with an embedded transformer that performs wireless charging. A simple on/off switch 260 powers on the sensor 230. The sensor 230 lacks an acoustic sensor, meaning it cannot measure S1 and S2, as described above.
In other embodiments, the patch sensor 230 can have other form factors, and may include additional sensors. For example, the secondary module 254 may include an acoustic sensor, similar to the acoustic sensor (component 146) shown in
The algorithm begins by explicitly determining HR/RR parameters with the patch sensor (step 320), as described above. As shown in
Once the algorithm generates PVP-ACtime,segments, each segment is transformed into the frequency domain (using, e.g., a FFT, CWT, or DWT) to generate individual frequency-domain segments classified as PVP-ACfrequency,segments (step 326). The algorithm then takes an ensemble average of the collection of PVP-ACfrequency,segments to form PVP-ACfrequency,segments (step 328). Once PVP-ACfrequency,segments,ave is determined, the algorithm uses HR/RR values determined independently by the patch sensor (step 330) during step 320 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 ‘truth’, and then incorporates these into a filter that prevents the algorithm for selecting erroneous peaks in the frequency-domain. Alternatively, during step 330, the HR/RR values determined from the patch sensor can be used in an adaptive filter or comparable mathematical filter to remove erroneous peaks and other features (associated, e.g., with motion or noise) from the frequency-domain spectrum, thereby making it easier to detect F0 and F1.
Once F0 and F1 are selected, their frequency is determined from the peak maximum, and their energy is determined from their peak amplitude or alternatively by integrating an area underneath the curve centered around the maximum peak amplitude (step 332). The algorithm then processes the parameters corresponding to F0 and F1, or a combination thereof, to determine a parameter related to the patient's fluid status (step 334). A clinician can then use such a parameter to treat the patient.
The algorithm indicated by step 334 in
Alternatively, a machine-learning approach can be used to develop a model that converts parameters related to F0 and F1 measured with iPIVA to those related to the patient's fluid status. One such a machine-learning approach is called a support vector machine (herein “SVM”). The approach here is similar to that used with the linear regression: data determined from a clinical trial is used to build the SVM, which is then used going forward to convert iPIVA parameters into things like cardiac wedge pressure. Other computation models that can be used in similar applications include Gaussian Kernel Functions, Boosting Ensemble, and Bagging Ensemble.
In embodiments of the invention, algorithms operating on the iPIVA sensor can use the following steps to identify features associated with RR (i.e. F0) and HR (i.e. F1):
In embodiments, variations of this approach (e.g. using an FFT or DWT in place of a CWT) can be used with the steps listed above to determine values of F0 and F1.
In other embodiments of the invention, an amplitude of either S1 or S2 (or both) heart sounds can be used to predict BP. This parameter typically increases in a linear manner with the amplitude of the heart sound. In embodiments, a universal calibration describing this linear relationship may be used to convert the heart sound amplitude into a value of BP. The algorithm for determining BP may also be based on a technique using machine learning or artificial intelligence, e.g. a technique using a SVM.
The calibration for the BP measurement, for example, may be determined from data collected in a clinical trial conducted with a large number of subjects. Here, numerical coefficients describing the relationship between BP and heart sound amplitude are determined by fitting data collected during the trial. These coefficients and a linear algorithm are coded into the sensor for use during an actual measurement. Alternatively, a patient-specific calibration can be determined by measuring reference blood pressure values and corresponding heart sound amplitudes during a calibration measurement, which proceeds an actual measurement. Data from the calibration measurement can then be fit as described above to determine the patient-specific calibration, which is then used going forward to convert heart sounds into BP values.
Time and frequency-domain analyses of IPG, BR, and PCG waveforms can be used to distinguish respiratory events such as coughing, wheezing, and to measure respiratory tidal volumes. In particular, respiratory tidal volumes are determined by integrating the area underneath a ‘respiratory pulse’ in an IPG or BR waveform (such as that indicated in
In other embodiments, a sensitive accelerometer can be used in place of the acoustic sensor (e.g. in the patch sensor shown in
In other embodiments, signals from PIVA and iPIVA can be used to estimate conditions such as IV infiltration, extravasation, and IV occlusion. Here, changes in the time and frequency-domain PVP waveforms can indicate these conditions. For example, a gradual increase in PVP combined with a gradual reduction in F0 and F1 may indicate that an IV catheter is slipping out of the patient's vein and into surrounding tissue. Alternatively, a rapid increase in PVP coupled with a rapid elimination of F0 and F1 may 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 if the IV system is in a free-flow state.
These and other embodiments of the invention are deemed to be within the scope of the following claims.
This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63/043,494, entitled PATIENT-MONITORING SYSTEM, filed Jun. 24, 2020, the entire contents of which are hereby incorporated by reference in its entirety and relied upon.
| Number | Date | Country | |
|---|---|---|---|
| 63043494 | Jun 2020 | US |
