Over-tightness detection algorithm for pressure sleeve

Abstract

A system includes a hardware unit storing a signal distortion detection code. The code causes the system to perform the steps of the method for evaluating the fitting and placement of haemodynamic sensors. The method includes acquiring hemodynamic data from a hemodynamic sensor and determining an arterial pressure waveform based on the hemodynamic data. A distortion score is determined based on one or more features extracted from the arterial pressure waveform and a weighting module. The method includes selectively invoking a sensory alarm based on a comparison of the distortion score to one or more predetermined criteria.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 389,338, filed on July 14, 2022, entitled “PRESSURE CUFF OVERTIGHTENING DETECTION ALGORITHM,” the disclosure of which is hereby incorporated by reference in its entirety. Background Art

[0003] The present disclosure relates generally to arterial pressure monitoring, and more particularly to evaluating the fit and / or positioning quality of a hemodynamic sensor by analyzing an arterial pressure signal.

[0004] Volume clamping is a technique for non-invasively measuring arterial pressure in which pressure is applied to a patient's finger so that the arterial pressure can be balanced by the time-varying pressure to maintain a constant arterial volume. In a properly fitted and calibrated system, the applied time-varying pressure is equal to the arterial pressure in the finger. The applied time-varying pressure can be measured to provide a reading of the patient's arterial pressure.

[0005] This can be done by a cuff placed around the patient's finger. The cuff can include an infrared light source, an infrared sensor, and an inflatable balloon. Infrared light can be sent through the finger where the finger artery is located. The infrared sensor receives the infrared light, and the amount of infrared light recorded by the sensor can be inversely proportional to the diameter of the artery and indicate the pressure within the artery.

[0006] In a cuff embodiment, pressure is applied to the artery in the finger by inflating a balloon in the cuff. If the pressure is high enough, it will compress the artery and the amount of light recorded by the sensor will increase. The amount of pressure necessary in the inflatable balloon to compress the artery depends on the blood pressure. By controlling the pressure of the inflatable balloon so that the diameter of the finger artery remains constant, blood pressure can be monitored very accurately because the pressure in the inflatable balloon is directly related to the blood pressure. In a typical cuff embodiment today, a volume clamp system is used with the cuff. The volume clamp system typically includes a pressure generating system and a regulating system, which includes: a pump, a valve, and a pressure sensor in a closed-loop feedback system, which are used to measure the arterial volume. In order to accurately measure blood pressure, the feedback loop provides sufficient pressure generation and release capabilities to match the pressure oscillations of the subject's blood pressure.

[0007] Current finger cuffs are easily placed on the patient's finger. However, to ensure that the blood pressure measurement system can obtain and report the correct blood pressure measurement, it is critical to correctly place the finger cuff on the patient's finger. In order to obtain the correct attachment of the finger cuff to the finger, the finger cuff needs to be placed on the finger at the correct depth, the correct angle, and the correct tightness. Current finger cuffs are usually made of flexible materials, which are difficult for medical staff to adjust. Wrap it around the patient's finger and apply it with a simple A fastening mechanism (such as Velcro) locks to These current types of cuffs require the healthcare provider to be able to control all three variables (e.g., correct depth, correct angle, and correct tightness) simultaneously when attaching the cuff. Unfortunately, this can result in errors in the placement and tightness of the cuff. Such incorrect placement and tightness can result in an unsuccessful reading of the patient's blood pressure. Even if the healthcare provider initially places and tightens the cuff correctly, patient activity or changes in the patient's condition can cause the cuff to become improperly placed or tightened. For example, a patient's limbs may develop edema. Swelling due to edema or other causes can also result in an unsuccessful reading of the patient's blood pressure. Summary of the invention

[0008] According to an exemplary embodiment of the present disclosure, a system for monitoring the fit of a hemodynamic sensor (e.g., a cuff) to a patient includes a hardware unit. The hardware unit includes a hardware processor, an analog-to-digital converter (ADC), and a system memory. The system further includes a signal distortion detection code and a weighting module stored in the system memory and a sensory alarm. The hardware processor is configured to execute the signal distortion detection code to continuously acquire hemodynamic data, which can be converted by the ADC from a signal received from the hemodynamic sensor. The code is further configured to acquire an arterial pressure waveform based on the hemodynamic data and extract a plurality of features from the first arterial pressure waveform, which indicates a distortion of the first arterial pressure waveform associated with the fit or positioning quality of the hemodynamic sensor and the patient. By using the weighting module and the extracted features (i.e., distortion parameters), the code is further configured to determine a distortion score and call a sensor alarm when the distortion score meets a predetermined criterion. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a schematic diagram of an exemplary patient monitoring system.

[0010] Figure 2 is a schematic diagram of an exemplary patient monitoring system including a cuff.

[0011] Figure 3A is a schematic diagram of an exemplary hemodynamic sensor cuff and pressure controller.

[0012] Figure 3B is a schematic diagram of the finger sleeve in an uninstalled state.

[0013] Figure 4A , Figure 4B and Figure 4C is an exemplary graph comparing arterial pressure parameters with a properly fitting cuff to arterial pressure parameters with a cuff that fits too tightly.

[0014] Figure 5 is a schematic diagram depicting features of an arterial pressure waveform obtained from a patient's finger and associated brachial and radial artery pressure waveforms derived from the arterial pressure waveform.

[0015] Figure 6 is a graph depicting exemplary features of a radial artery pressure waveform.

[0016] Figure 7 is a graph depicting exemplary features of a brachial artery pressure waveform.

[0017] Figure 8 is a flow chart illustrating the steps of a signal distortion detection method. DETAILED DESCRIPTION

[0018] Figure 1 1 is a schematic diagram depicting components of an exemplary patient monitoring system capable of evaluating the fit and / or placement of a sensor attached to a patient. As shown, patient monitoring system 10 includes a hemodynamic sensor 12 mounted to patient 14, a hardware unit 16, and a patient monitoring device 18.

[0019] The hemodynamic sensor 12 may be a non-invasive sensor that is mounted around an appendage of the patient 14 and, in some cases, at a specific location along the patient's appendage. Mis-mounting or mis-positioning of the hemodynamic sensor 12 may occur accidentally during application of the procedure by the practitioner 20, or may occur due to patient movement and / or changes in the patient's condition (e.g., swelling of the patient's appendage). When mis-mounted and / or mis-positioned, the hemodynamic sensor 12 may provide erroneous hemodynamic data to the hardware unit 16, thereby affecting the monitoring of the patient 14.

[0020] The hardware unit 16 includes a system processor 24, a system memory 26, and an analog-to-digital (ADC) converter 27, which interfaces with the patient monitoring device 18. In some cases, the hardware unit 16 is separate from the patient monitoring device 18 and can be installed into a corresponding socket or slot of the patient monitoring device 18. In other examples, the hardware unit 16 is integrated with the internal components of the patient monitoring device 18. In other examples, one or more components and / or the described functions can be distributed among multiple hardware units 16, patient monitoring devices 18, and / or other patient monitoring equipment.

[0021] The system processor 24 executes signal distortion detection code 28, which uses a weighting module 30 and a signal distortion parameter 32 to determine a distortion score 34 representing the fit quality and / or positioning quality of the hemodynamic sensor 12. Examples of the system processor 24 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other equivalent discrete or integrated logic circuits.

[0022] System memory 26 may be configured to store information within hardware unit 16 during operation. Figure 1 As shown, the system memory 26 stores the signal distortion detection code 28. In some examples, the system memory 26 is described as a computer-readable storage medium. In some examples, the computer-readable storage medium may include a non-transient medium. The term "non-transient" may mean that the storage medium is not embodied as a carrier or a propagation signal. In some examples, the non-transient storage medium may store data that can change over time (for example, in a RAM or cache). The system memory 26 may include volatile and non-volatile computer-readable memories. Examples of volatile memories may include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories. Examples of non-volatile memories may include, for example, magnetic hard disks, optical disks, flash memory, or electrically programmable memories (EPROM) or electrically erasable programmable (EEPROM) memories.

[0023] The hardware unit 16 receives hemodynamic data from the hemodynamic sensor 12. The hemodynamic data may include any one or more features of the arterial pressure waveform or any one or more features derived from the arterial pressure waveform. The arterial pressure waveform may be measured at a radial position, a brachial position, or a finger position of the patient 14. In addition, features from multiple waveforms (e.g., from one or more arterial waveforms collected at a radial position, a brachial position, and / or a finger position) may be used in combination by the signal distortion detection code 28. For example, a non-exhaustive list of potential arterial pressure waveform features includes mean arterial pressure, maximum arterial pressure, minimum arterial pressure, pulse rate, pulse pressure, maximum and / or minimum rate of change of pressure relative to time during a heartbeat (or a subset of heartbeats), maximum and / or minimum rate of change of pressure relative to time in the systolic and / or diastolic phases of a heartbeat (or a subset of heartbeats), end-diastolic pressure, diastolic gradient, systolic gradient, pulse transit time, cardiac output, stroke volume, blood temperature, systolic rise area, diastolic mean pressure, systemic vascular resistance, exponential constant associated with systolic pressure decay, and other possible parameters.

[0024] The weighting module 30 applies the distortion coefficients 31 determined by training to the signal distortion parameters 32, which are features extracted from the arterial pressure waveform. Certain signal distortion parameters 32 may indicate a hemodynamic sensor 12 with a proper fit and / or proper positioning, while other signal distortion parameters 32 may indicate a hemodynamic sensor 12 with an improper fit and / or improper positioning. Based on the distortion coefficients 31 applied by the weighting module 30 and the signal distortion parameters 32, the signal distortion detection code 28 determines a distortion score 34 representing the quality of fit and / or positioning of the hemodynamic sensor 12.

[0025] The selection of the distortion coefficients 31 and / or signal distortion parameters 32 may be accomplished by training (e.g., offline training) a distortion detection model using machine learning or other techniques to minimize a cost function representing the error between the distortion detection model output and the true value of a training subset of hemodynamic sensors 12 that define proper fit and improper fit. The training of the distortion detection model may be accomplished using various supervised techniques and / or unsupervised techniques known in the art. An exemplary technique includes obtaining an arterial pressure feature dataset from a hemodynamic sensor 12 known to have proper fit and proper positioning and from a different hemodynamic sensor 12 known to have improper fit and / or improper positioning. Based on the features associated with the two or more datasets, logistic regression may be used to identify which features indicate proper fit and / or proper positioning of the hemodynamic sensor 12, and which features indicate improper fit and / or improper positioning of the hemodynamic sensor 12. Further training of the distortion model using the feature subset determines the weighting factors (i.e., the distortion coefficients 31) of the weighting module 30. The subset of features of the hemodynamic sensor 12 extracted from the arterial pressure waveform that are indicative of proper fit and / or improper fit becomes the distortion signal parameters 32 .

[0026] The distortion score 34 may be a normalized value between 0 and 1 (or between 0 and 100 or other normalized ranges), where in some examples, higher values ​​represent a higher likelihood that the hemodynamic sensor 12 is mis-fitted or mis-positioned, and lower values ​​represent a lower likelihood that the hemodynamic sensor 12 is mis-fitted or mis-positioned. In another example, the normalized range of the distortion score may be refined into two or more consecutive sequential sub-ranges, each sub-range indicating the quality of fit and / or positioning of the hemodynamic sensor 12 to the patient 14. The first of the two sub-ranges (e.g., a distortion score value between 0 and 50 in the normalized range from 0 to 100) may indicate a properly fitted hemodynamic sensor 12. The second sub-range (e.g., a distortion score value between 51 and 100 in the normalized range from 0 to 100) may indicate an improperly fitted or mis-positioned hemodynamic sensor 12. When the normalized range is composed of two sub-ranges, the system 10 can provide a binary indication of a properly fitted / positioned or improperly fitted / positioned hemodynamic sensor 12. In other examples, the normalized range of the distortion score 34 can be subdivided into at least three consecutive sequential sub-ranges. The first of the three sub-ranges (e.g., a distortion score value between 1 and 30 in the normalized range from 0 to 100) can indicate a properly fitted hemodynamic sensor 12. The second sub-range (e.g., a distortion score between 31 and 60 in the normalized range from 0 to 100) can indicate a potentially improperly fitted and / or potentially mis-positioned hemodynamic sensor 12. The third sub-range (e.g., a distortion score between 61 and 100 in the normalized range from 0 to 100) can indicate an improperly fitted or mis-positioned hemodynamic sensor 12. In this manner, the second sub-range (ie, the middle sub-range) may provide an indication that the hemodynamic sensor 12 should be re-applied or repositioned, while the third sub-range may indicate that the hemodynamic sensor 12 should be replaced.

[0027] An example equation for determining the distortion score 34 is as follows:

[0028]

[0029] Where i=1, 2, 3, ..., 13 (ie, the total number of distortion parameters in the distortion detection model).

[0030] where x i is an example parameter determined from the hemodynamic data measured by the hemodynamic sensor 12, w i are the corresponding parameter weights (i.e. coefficients), and w0 is the bias term.

[0031] x1 = mean arterial pressure,

[0032] x2 = maximum arterial pressure,

[0033] x3 = minimum arterial pressure,

[0034] x4 = pulse rate,

[0035] x5 = pulse pressure,

[0036] x6 = maximum rate of change of pressure with respect to time during a heartbeat (or subset of heartbeats),

[0037] x7 = minimum rate of change of pressure with respect to time during a heartbeat (or subset of heartbeats),

[0038] x8 = the maximum rate of change of pressure with respect to time during the systolic phase of a heartbeat (or a subset of heartbeats),

[0039] x9 = minimum rate of change of pressure with respect to time during the systolic phase of a heartbeat (or subset of heartbeats),

[0040] x 10 = the maximum rate of change of pressure with respect to time during the diastole phase of a heartbeat (or subset of heartbeats),

[0041] x 11 = the minimum rate of change of pressure with respect to time during the diastole of a heartbeat (or a subset of heartbeats),

[0042] x 12 = end-diastolic pressure,

[0043] x 13 = diastolic gradient,

[0044] x 14 = systolic pressure gradient,

[0045] x 15 = pulse transmission time,

[0046] x 16 = cardiac output,

[0047] x 17 = stroke volume,

[0048] x 18 = blood temperature,

[0049] x 19 = systolic ascending area,

[0050] x 20 = mean diastolic pressure,

[0051] x 21 = systemic vascular resistance,

[0052] x 22= exponential constant associated with the decay of systolic blood pressure.

[0053] The distortion score 34 may be determined using one, a selected number, or all of the distortion parameters 32 , and some examples of determining the distortion score 34 may include utilizing other parameters from multiple arterial pressure waveforms associated with different measurement locations (eg, radial, brachial, or finger locations).

[0054] Patient monitoring device 18 may be any type of medical electronic device capable of reading, collecting, processing, displaying, etc., physiological readings / data of a patient, including blood pressure and any other suitable physiological patient readings. Patient monitoring device 18 may include user interface 36 and sensory alarm 37. In some examples, power / data line 38 may be connected via a power cord such as a Figure 1 One or more input and / or output connectors 40 shown as solid lines in FIG. 1 are connected to the hardware unit 16. In other examples, the patient monitoring device 18 may be connected to the hardware unit 16 by Figure 1 A power / data line 38 and one or more input and / or output connectors 40 shown as dashed lines in FIG. 1 are connected to the hemodynamic sensor 12. In this example, the hardware unit 16 receives hemodynamic data via the patient monitoring device 18.

[0055] User interface 36 may be a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, or other display device suitable for providing information to a user in a graphical form. In some examples, for example Figure 2 In the example shown, user interface 36 may be a touch-sensitive and / or presence-sensitive display device configured to receive user input in the form of gestures, such as touch gestures, scroll gestures, zoom gestures, swipe gestures, or other gesture inputs.

[0056] The power / data line 38 can transmit data to and from the patient monitoring device 18, and can provide power from the patient monitoring device 18 to the hemodynamic sensor 12 and / or other components associated with the operation of the hemodynamic sensor 12. In some examples, the patient monitoring device 18 can be a hemodynamic monitor. The input and / or output (I / O) connector 40 is configured for wired connection (e.g., electrical connection and / or communication connection) with one or more peripheral components (e.g., one or more hemodynamic sensors).

[0057] In operation, the hardware unit 16 receives hemodynamic data from the patient 14 via the hemodynamic sensor 12, which may be digitally converted by an analog-to-digital converter (ADC) 27. In response to receiving the hemodynamic data from the patient 14, the hardware unit 16 executes a signal distortion detection code 28 to determine a distortion score 34 representing a quality of fit and / or a quality of positioning of the hemodynamic sensor 12 to the patient 14. Based on the distortion score 34, the signal distortion detection code 28 causes the patient monitoring device 18 to provide an indication of the quality of fit and / or the quality of positioning via a user interface 36 and / or a sensory alarm 37. The indication of the quality of fit and / or the quality of positioning of the hemodynamic sensor 12 may include directly displaying the distortion score 34 via the user interface 36. In other examples, the indication of the quality of fit and / or the quality of positioning may include displaying a message or warning representing the quality of fit and / or the quality of positioning of the hemodynamic sensor 12. In addition, the hardware unit 16 can invoke a sensory alarm 37, such as an audible alarm, a tactile alarm, or other sensory alarm, in response to determining that the distortion score satisfies one or more predetermined criteria (e.g., the distortion score is within one of the predefined sub-ranges). Accordingly, the hardware unit 16 can provide a warning or message to the patient monitoring device 18 so that the practitioner 20 can correct the improper fit and / or improper positioning of the hemodynamic sensor 12 and / or replace the hemodynamic sensor 12.

[0058] Figure 2 FIG. 1 is a schematic diagram of an exemplary arterial pressure monitoring system 10A. Figure 1 , and includes the components of the patient monitoring system 10 except for the differences that may be noted. Thus, the arterial pressure monitoring system 10A includes a hardware unit 16 and a patient monitoring device 18. As shown, the hardware unit 16 is a discrete module that can be inserted into a slot of the patient monitoring device 18. The hardware unit 16 includes a system processor 24 for executing a signal distortion detection code 28 and a system memory 26 for storing the signal distortion detection code 28 and a weighting module 30 for determining a distortion score 34 based on a distortion coefficient 31 and a distortion parameter 32. The hardware unit also includes an analog-to-digital (ADC) converter 27. The patient monitoring device 18 includes a user interface 36 and a sensory alarm 37.

[0059] The hemodynamic sensor 12 takes the form of a cuff 42, a pressure controller 44, and a cardiac reference sensor 46. In one example, the cuff 42 may be attached to a finger (or toe) of the patient 14, and the pressure controller 44 may be attached to the body of the patient 14 via an attachment bracelet that wraps around the patient's wrist or hand. It should be understood that this is just one example of attaching the pressure controller 44, and any suitable means of attaching the pressure controller 44 to or near the patient's body may be utilized.

[0060] The pressure controller 44 may be further connected to the hardware unit 16 via a power / data line 38, which is connected to the hardware unit 16 via an input / output connector 40. The cuff 42 provides hemodynamic data representing the arterial pressure of an artery within a finger (or toe) of the patient 14 to the hardware unit 16. A cardiac reference sensor 46 is connected to the pressure controller 44 to provide a signal representing the pressure differential between the patient's finger and the heart height. The pressure differential signal enables the pressure controller 44 to compensate the hemodynamic data for changes in arterial pressure caused by the patient's finger being at a different height from the patient's heart. The cuff 42, the pressure controller 44, and the cardiac reference sensor 46 together provide the arterial waveform of the patient 14 to the hardware unit 16.

[0061] Figure 3A FIG. 4 is a schematic diagram of an exemplary hemodynamic sensor 12 and a cuff 42 and a pressure controller 44. Figure 2 Magnified view of area A in FIG. Figure 3B The cuff 42 is depicted in an uninstalled state to illustrate features of the cuff 42 that are hidden when attached to the patient 14 .

[0062] like Figure 3B As shown, the cuff 42 includes a substrate 48, an inflatable balloon 50, a light emitting diode (LED) 52, a photodiode (PD) 54, and a tube 56. The substrate 48 is a flexible or semi-rigid material that forms the outer layer of the cuff 42. The inflatable balloon 50 is mounted to the inner surface of the substrate 48 and is fluidly connected to the tube 56. The LED 52 is spaced apart from the PD 54 along the longitudinal direction of the substrate 48, and each of the LED 52 and the PD 54 is mounted to the inner surface of the substrate 48.

[0063] refer to Figure 3A , the pressure controller 44 may include a small internal pump, a small control valve, a pressure sensor, and a control circuit. The pump may be disposed along an internal conduit that connects the tube 56 of the cuff 42 to the air inlet of the pressure controller 44 that is in communication with the atmosphere. The control circuit may be configured to control the pneumatic pressure applied by the internal pump to the airbag of the cuff 42 to replicate the patient's blood pressure based on measuring the signal received from the LED-PD pair of the cuff 42. One end of the cardiac reference sensor 46 may be configured as Figure 3A Attached to the patient's adjacent cuff 42 as shown, and the other end of the cardiac reference sensor 46 as shown Figure 2 Attached to the patient's body at heart level as shown. In addition, the control circuit can be configured to control the opening of the control valve to release the pneumatic pressure from the air bag. Alternatively, the control valve can be replaced with an uncontrolled orifice. In these examples, the air bag pressure in the cuff 42 varies based on the operating speed or periodic operation of the internal pump and / or the position of the control valve.

[0064] Continuing with this example, the patient's hand may be placed next to the patient's body in a sitting or prone position to measure the patient's blood pressure using the blood pressure measurement system 10. The pressure controller 44 of the system 10A may be coupled to the bladder 50 of the cuff 42 to provide pneumatic pressure to the bladder 50 for blood pressure measurement. The pressure controller 44 may be coupled to the hardware unit 16 ( Figure 1-Figure 2 ). Accordingly, the pressure controller 44 operates according to the volume clamping method described above to change the internal pressure of the balloon 50 and outputs a signal representing the arterial pressure in the finger (or toe) of the patient 14 as a function of time (i.e., an arterial pressure waveform).

[0065] An arterial pressure waveform initially representing arterial pressure within a patient's finger may be mathematically transformed to represent an arterial pressure waveform at a location such as a brachial and / or radial site of the patient 14. As used herein, such radial and brachial arterial pressure waveforms are reconstructed from the finger arterial pressure waveforms rather than being directly measured (e.g., via invasive catheterization techniques).

[0066] Figure 4A , Figure 4B and Figure 4C is a depiction representing the radial region within the patient 14 and using a cuff 42, a pressure controller 44, and a cardiac reference sensor 46 ( Figure 2 Graph of exemplary arterial pressure characteristics measured by Figure 4A , Figure 4B and Figure 4C As shown in each of the figures, an arterial pressure feature from a properly fitting cuff 42 is compared to an arterial pressure feature from an improperly fitting cuff 42. Based on this comparison, the arterial pressure feature that has the closest correlation with a properly fitting and / or improperly fitting cuff 42 can be identified and used to determine the distortion score 34 via Equation 1. Repeating this process for each potential arterial pressure waveform feature can identify a set of arterial pressure waveform features that are indicative of a properly fitting cuff 42 and / or an improperly fitting cuff 42.

[0067] refer to Figure 4A , Figure 4B and Figure 4C , arterial parameters 58A, 58B and 58C represent the systolic arterial pressure, mean arterial pressure and diastolic arterial pressure of the properly fitted and positioned first cuff, respectively. Arterial pressure parameters 60A, 60B and 60C represent the systolic arterial pressure, mean arterial pressure and diastolic arterial pressure produced by the second cuff, respectively. Prior to time step t1, both the first cuff and the second cuff have an appropriate tightness. Measurements of both the first cuff and the second cuff are stopped at time step t1, then the second cuff is intentionally over-tightened, and measurements are continued at time t2. Both the first cuff and the second cuff are mounted on separate fingers of the same patient 14 in order to compare the arterial pressures output from each cuff. As Figure 4A , Figure 4B and Figure 4C As shown in , over-tightening the second cuff at time t2 causes deviations in arterial parameters 60A, 60B, and 60C relative to the properly fitted and positioned first cuff. In this particular example, arterial parameters 60A, 60B, and 60C are reduced relative to corresponding parameters (e.g., arterial parameters 58A, 58B, and 58C) generated by the properly fitted first cuff. The local maximum pressure and / or local minimum pressure of parameters 60A, 60B, and 60C relative to the average value of the corresponding parameters 60A, 60B, and 60C are greater or less than the corresponding local maximum pressure and local minimum pressure of the corresponding parameters (e.g., arterial parameters 58A, 58B, and 58C) generated by the first cuff. Therefore, these deviations can be used to identify arterial pressure features that indicate proper fit and / or improper fit of cuff 42. Once identified, the deviation of the arterial pressure feature relative to the properly fitted cuff 42 characterizes the effect of improperly fitting the cuff 42 to the patient's finger.

[0068] Figure 5 64A-64L。 Figure 64B is a schematic diagram depicting an exemplary arterial pressure waveform 62 obtained at a patient's finger and features of a reconstructed brachial artery pressure waveform and a radial artery pressure waveform derived therefrom. Arterial pressure waveform 62 includes at least features 64A, 64B, 64C, 64D, 64E, 64F, 64G, 64H, 64I, 64J, 64K and 64L, collectively referred to as arterial pressure features 64A-64L.

[0069] Features 64A, 64B, and 64C are the minimum arterial pressure (i.e., end-diastolic pressure), maximum arterial pressure, and dichotic notch pressure of a heartbeat, respectively. Feature 64D is the mean arterial pressure of a heartbeat (or a series of heartbeats). Feature 64E is the maximum rate of change of pressure during the rise of systolic pressure. The maximum rate of change of pressure during the decay of systolic pressure is indicated by feature 64F. The maximum rate of change during diastole is represented by feature 64G. Feature 64H represents the area under the arterial pressure waveform 62 between the minimum arterial pressure 64A and the maximum arterial pressure 64B of the heartbeat (i.e., during the rise of systolic pressure), and feature 64I represents the area under the arterial pressure waveform 62 between the minimum arterial pressure 64A and the dichotic pressure 64C (i.e., during the systolic period). Feature 64J can be the time of the systolic period of a given heartbeat. Feature 64K is the time between consecutive heartbeats of the arterial pressure waveform 62 from which the pulse rate can be determined. The exponential decay constant of the systolic period is represented by feature 64L.

[0070] Additional features can be derived from arterial pressure feature 64. For example, stroke volume is proportional to systolic area (i.e., feature 64I) and can therefore be derived from systolic area (i.e., feature 64I). Cardiac output is equal to stroke volume multiplied by pulse rate (i.e., feature 64K). Systemic vascular resistance can be determined by the difference between mean arterial pressure and central venous pressure divided by cardiac output.

[0071] Similar features may be extracted from the radial artery pressure waveform 66 and / or the brachial artery pressure waveform 68. The radial artery pressure waveform 66 is derived from the finger artery pressure waveform 62 using a transfer function 70. The brachial artery pressure waveform 68 is derived from the finger artery pressure waveform 62 using a transfer function 72. Each of the transfer function 70 and the transfer function 72 is determined by correlating the arterial pressure feature 64 of the finger artery pressure waveform 62 with corresponding features of the radial artery pressure waveform 66 and the brachial artery pressure waveform 68.

[0072] Figure 6 is a graph depicting exemplary features 74A, 74B, 74C, 74D, 74E, 74F, 74G, 74H, 74I, 74K, and 74L (collectively, radial artery pressure features 74A- 74L) of the reconstructed radial pressure waveform 66 . Figure 7 FIG. 6 is a graph depicting exemplary features 76A, 76B, 76C, 76D, 76E, 76F, 76G, 76H, 76I, 76K, and 76L (collectively, brachial artery pressure features 76A-76L) of the reconstructed brachial pressure waveform 68. Each of features 74A-74L and features 76A-76L corresponds to Figure 5 Similar features 64A-64L of the finger pressure waveform 62 shown in FIG.

[0073] Therefore, corresponding features 74A-74L are determined from the reconstructed radial artery pressure waveform 66. Features 74A, 74B, and 74C are the minimum arterial pressure (i.e., end-diastolic pressure), maximum arterial pressure, and dicrotic notch pressure of the heartbeat from the reconstructed radial artery pressure waveform 66, respectively. Feature 74D is the mean arterial pressure of a heartbeat (or a series of heartbeats). Feature 74E is the maximum rate of change of pressure during the rise of systolic pressure. The maximum rate of change of pressure during the decay of systolic pressure is indicated by feature 74F. The maximum rate of change during diastole is represented by feature 74G. Feature 74H represents the area under the radial artery pressure waveform 66 between the minimum arterial pressure 74A and the maximum arterial pressure 74B of the heartbeat (i.e., during the rise of systolic pressure), and feature 74I represents the area under the radial artery pressure waveform 66 between the minimum arterial pressure 74A and the dicrotic pressure 74C (i.e., during the systolic period). Feature 74J can be the time of the systolic period of a given heartbeat. Feature 74K is the time between successive heartbeats of the reconstructed radial artery pressure waveform 66 from which the pulse rate can be determined. The exponential decay constant of the systolic phase is represented by feature 74L. Each of features 74A-74L is determined from the radial artery pressure waveform 66 reconstructed or derived from the finger artery pressure waveform 62.

[0074] Similarly, corresponding features 76A-76L are determined from the brachial artery pressure waveform 68. Features 76A, 76B, and 76C are the minimum arterial pressure (i.e., end-diastolic pressure), maximum arterial pressure, and dicrotic notch pressure of a heartbeat from the brachial artery pressure waveform 68, respectively. Feature 76D is the mean arterial pressure of a heartbeat (or a series of heartbeats). Feature 76E is the maximum rate of change of pressure during the rise of systolic pressure. The maximum rate of change of pressure during the decay of systolic pressure is indicated by feature 76F. The maximum rate of change during diastole is represented by feature 76G. Feature 76H represents the area under the brachial artery pressure waveform 68 between the minimum arterial pressure 76A and the maximum arterial pressure 76B of the heartbeat (i.e., during the rise of systolic pressure), and feature 76I represents the area under the brachial artery pressure waveform 68 between the minimum arterial pressure 76A and the dicrotic pressure 76C (i.e., during the systolic period). Feature 76J can be the time of the systolic period of a given heartbeat. Feature 76K is the time between successive heartbeats of the brachial artery pressure waveform 68 from which the pulse rate can be determined. The exponential decay constant of the systolic phase is represented by feature 76L. Each of features 76A-76L is determined from the brachial artery pressure waveform 68 reconstructed or derived from the finger artery pressure waveform 62.

[0075] Figure 81 is a flow chart describing a signal distortion detection method 100 for evaluating the fit of a cuff 42 or another hemodynamic sensor 12. The method 100 includes steps 102, 104, and 106. In further examples, the method 100 may include one or more of steps 108, 110, and 112. Figure 8 The sequence depicted in is for illustrative purposes only and is not meant to limit method 100 in any way, as it should be understood that some portions of method 100 may be performed in a different logical order, may include additional or intermediate portions, or the described portions of method 100 may be divided into multiple portions, or the described portions of method 100 may be omitted without detracting from the above.

[0076] In step 102, an arterial pressure waveform is obtained from a patient monitoring system. For example, the pressure controller 44 may vary the balloon pressure of the cuff 42 according to the volume clamp to obtain hemodynamic data representative of the patient's arterial pressure waveform. The hemodynamic data may have been previously converted to digital hemodynamic data by an analog-to-digital converter 27 located within the pressure controller 44, the hardware unit 16, or the patient monitoring device 18. Based on the hemodynamic data, the hardware unit 16 continuously determines the arterial pressure waveform (i.e., arterial pressure as a function of time). As generally represented by the patient monitoring system 10, different hemodynamic sensors 12 that generate hemodynamic data representative of the patient's arterial pressure may be used. In either example, step 102 includes measuring or calculating the arterial pressure waveform based on measurements of a hemodynamic sensor that is sensitive to fit and / or placement on the patient.

[0077] Step 104 includes extracting one or more features from the arterial pressure waveform measured or determined in step 102. As described above, the features of the arterial pressure waveform may include one or more of mean arterial pressure, maximum arterial pressure, minimum arterial pressure, pulse rate, pulse pressure, maximum and / or minimum rate of change of pressure with respect to time during a heartbeat (or a subset of heartbeats), maximum and / or minimum rate of change of pressure with respect to time during the systolic and / or diastolic phases of a heartbeat (or a subset of heartbeats), end-diastolic pressure, diastolic gradient, systolic pressure gradient, pulse transit time, cardiac output, stroke volume, blood temperature, systolic rise area, diastolic mean pressure, systemic vascular resistance, exponential constant associated with systolic pressure decay, and other possible features. Any one or more of these features may be extracted from the finger arterial pressure waveform 62 or, as discussed further below, the radial arterial pressure waveform 66 or the brachial arterial pressure waveform 68, which are reconstructed or derived from the finger arterial pressure waveform 62. In other examples, the features may be extracted from each of the finger waveform, radial waveform, and brachial waveform, or any two of the arterial waveforms (e.g., two arterial waveforms selected from the finger arterial pressure waveform 62, radial arterial pressure waveform 66, and brachial arterial pressure waveform 68).

[0078] Step 106 includes determining a distortion score 34 based on one or more of the extracted features. For example, a subset of potential features may be incorporated into a distortion detection model using the training method described above. Continuing with this example, the distortion coefficient associated with each extracted feature may be used to determine a distortion score 34 based on equation 1. In one example, the distortion score 34 is determined based on one or more of the mean diastolic pressure, stroke volume, systolic vascular resistance of the radial artery pressure waveform 66, and the systolic rise area and exponential decay constant of the systolic phase of the brachial artery waveform 68. Steps 102, 104, and 106 or steps 102, 104, 106, and 112 may be repeated to determine a trend in the distortion score 34 over a period of time. In some examples, the method 100 determines the distortion score 34 as an average distortion score 34 over a period of time (e.g., a 20 second time interval). In other examples, the method 100 may determine whether the distortion score 34 is increasing or decreasing. In such an example, the method 100 may determine a rate of increase or decrease in the distortion score 34.

[0079] In step 108, the distortion score 34 or a trend of the distortion score 34 (e.g., an average distortion score 34, a rate of increasing distortion score 34, a rate of decreasing distortion score 34, or a combination of these distortion score trend parameters) is selected against a predetermined one or a set of criteria. For example, the distortion score 34 can be compared to one of a plurality of sub-ranges of a nominal total range of the distortion score 34. In a further example, the distortion score 34 can be compared to a value of the distortion score 34 and a trend of the distortion score over a period of time, the value of the distortion score 34 being associated with one or more sub-ranges of the nominal range of the distortion score 34. In a further example, the distortion score 34 can be a value within a nominal range that is divided into two consecutive sub-ranges (e.g., for a nominal range in which the distortion score 34 can be a value between 0 and 100, a first sub-range between 0 and 50 and a second range between 51 and 100). For distortion scores 34 within the first sub-range, the hardware unit 16 may cause one or more of the following actions to occur: a) displaying the distortion score 34 on the user interface 36 of the patient monitoring device 18, and b) displaying an indication (e.g., a message or other status indication) that the hemodynamic sensor 12 is properly affixed to the patient 14. Alternatively, when the distortion score 34 indicates proper affixation and positioning of the hemodynamic sensor 12, no message or status indication is provided. In this example, steps 102, 104, and 106 may be repeated to provide continuous or periodic monitoring of the hemodynamic sensor 12. For distortion scores 34 within the second sub-range, the hardware unit 16 may invoke a sensory alarm in step 110. Thereafter, steps 102, 104, 106, 108, and 110 may be continuously repeated to invoke a sensory alarm until the medical practitioner re-applies, repositions, and / or replaces the hemodynamic sensor 12.

[0080] In some examples of method 100, the arterial pressure waveform acquired in step 102 may be mathematically transformed in step 112 to represent an arterial pressure waveform at a location different from the location of the hemodynamic sensor 12. For example, the arterial pressure waveform generated by the cuff 42 represents the arterial pressure within the patient's finger (toe). The finger arterial pressure waveform may be converted into an arterial pressure waveform at a radial location of the patient and / or a brachial location of the patient. Features of the brachial artery pressure waveform and / or the radial artery pressure waveform may be extracted in step 104, and a distortion score 34 may be determined in step 106 based on one or more features extracted from one or more of the finger arterial pressure waveform, the radial artery pressure waveform, and the brachial artery pressure waveform. Thereafter, the distortion score determined according to step 100 may be compared with a predetermined one or more criteria in step 108. Based on the comparison, a sensory alarm may be invoked in step 110. Regardless of whether the sensory alarm is invoked, steps 102, 104, 106, 108, 110, and 112 are repeated to provide continuous and / or periodic monitoring of the fit and / or location of the hemodynamic sensor 12.

[0081] Discussion of possible examples

[0082] The following is a non-exclusive description of possible embodiments of the invention.

[0083] System for monitoring the hemodynamic data of a patient

[0084] A system for monitoring hemodynamic data of a patient according to an example of the present disclosure includes a hardware unit, a signal distortion detection code, a hemodynamic sensor, and a sensory alarm, among other possible components. The hardware unit includes a hardware processor, an analog-to-digital converter (ADC), and a system memory. The signal distortion detection code is stored in the system memory and includes a weighting module. The hemodynamic sensor is coupled to the hardware unit and fits around the patient's appendage. The hardware processor is configured to execute the signal distortion detection code to evaluate the fit of the hemodynamic sensor to the patient.

[0085] Additionally and / or alternatively, the system of the front section may optionally include any one or more of the following features, configurations and / or additional components.

[0086] A further example of the foregoing system, wherein the hardware processor may be configured to execute the signal distortion detection code to continuously acquire digital hemodynamic data converted by the ADC from a signal received from the hemodynamic sensor.

[0087] A further example of any of the foregoing systems, wherein the hardware processor can be configured to execute the signal distortion detection code to acquire a first arterial pressure waveform based on the digital hemodynamic data.

[0088] A further example of any of the foregoing systems, wherein the hardware processor can be configured to execute the signal distortion detection code to extract a plurality of features from the first arterial pressure waveform.

[0089] A further example of any of the foregoing systems, wherein a plurality of features may indicate fit of the hemodynamic sensor to the patient.

[0090] A further example of any of the foregoing systems may include determining, using a weighting module, a distortion score corresponding to a likelihood that the hemodynamic sensor does not fit the patient properly based on a weighted combination of the plurality of features.

[0091] A further example of any of the foregoing systems may include invoking a sensory alert if the distortion score meets a predetermined distortion criterion.

[0092] A further example of any of the foregoing systems may include a patient monitoring device.

[0093] A further example of any of the foregoing systems, wherein the hardware unit may be housed within a patient control unit.

[0094] A further example of any of the foregoing systems, wherein the hemodynamic sensor can be a finger cuff comprising an inflatable bladder configured to wrap around a patient's finger, a light emitting diode, and a photodiode spaced apart from the light emitting diode along a longitudinal dimension of the inflatable bladder.

[0095] A further example of any of the foregoing systems, wherein the first arterial pressure waveform may represent arterial pressure within a finger of the patient.

[0096] A further example of any of the foregoing systems may include a pressure control unit coupled to the cuff via a tube to receive the signal.

[0097] A further example of any of the foregoing systems, wherein the signal may be indicative of air pressure within an inflatable bladder of the cuff.

[0098] A further example of any of the foregoing systems, wherein the pressure control unit may include a control valve and a pump disposed along a conduit connecting a tube of the cuff to an air inlet of the pressure control unit.

[0099] A further example of any of the foregoing systems, wherein an air inlet of the pressure control unit can be communicated with an atmospheric environment.

[0100] A further example of any of the foregoing systems, wherein a control valve of the pressure control unit is operable to vary the air pressure within the inflatable bladder based on a blood pressure of the patient.

[0101] A further example of any of the foregoing systems, wherein acquiring the first arterial pressure waveform includes converting the digital hemodynamic data such that the first arterial pressure waveform represents one of a radial artery pressure waveform and a brachial artery pressure waveform.

[0102] A further example of any of the foregoing systems, wherein the plurality of characteristics may include at least one of mean diastolic pressure, stroke volume, and systemic vascular resistance derived from a first arterial pressure waveform representative of a radial artery pressure waveform.

[0103] A further example of any of the foregoing systems, wherein the plurality of features may include at least one of a systolic rise area and a systolic exponential decay constant derived from a brachial artery waveform.

[0104] A further example of any of the foregoing systems, wherein the plurality of features may include each of mean diastolic pressure, stroke volume, and systemic vascular resistance derived from a first arterial pressure waveform representative of a radial artery pressure waveform, and each of a systolic rise area and a systolic exponential decay constant derived from a second arterial pressure waveform representative of a brachial artery waveform.

[0105] A further example of any of the foregoing systems, wherein the plurality of characteristics may include at least one of mean arterial pressure, maximum arterial pressure, minimum arterial pressure, pulse rate, pulse pressure, maximum rate of pressure change with respect to time, minimum rate of pressure change with respect to time, end-diastolic pressure, diastolic gradient, systolic gradient, pulse transit time, cardiac output, stroke volume, blood temperature, systolic rise area, diastolic mean pressure, systemic vascular resistance, and an exponential constant associated with systolic pressure decay.

[0106] A further example of any of the foregoing systems, wherein at least one of the plurality of features may be extracted from a digital artery pressure waveform, a radial artery pressure waveform, or a brachial artery pressure waveform.

[0107] A further example of any of the foregoing systems, wherein the predetermined distortion criterion may be based on at least one of a value of the distortion score and a trend of the distortion score over a time interval.

[0108] A further example of any of the foregoing systems, wherein the distortion score may be equal to a value within a nominal range of distortion scores subdivided into at least two sub-ranges.

[0109] A further example of any of the foregoing systems, wherein the predetermined distortion criterion may be based on a value of a distortion score associated with a threshold between the sub-ranges.

[0110] A further example of any of the foregoing systems, wherein extracting the plurality of features from the first arterial pressure waveform may include extracting the plurality of features from a time interval beginning with applying the hemodynamic sensor to the patient or a physiological change in the patient.

[0111] A further example of any of the foregoing systems, wherein the time interval may be greater than or equal to twenty seconds and less than or equal to sixty seconds.

[0112] Method used by a system for monitoring hemodynamic data of a patient

[0113] A method used by any of the aforementioned exemplary systems for monitoring hemodynamic data of a patient includes continuously acquiring, by a signal distortion detection code executed by a hardware processor, digital hemodynamic data converted by an ADC from a signal received from a hemodynamic sensor, and other possible steps. The method includes acquiring, by the signal distortion detection code executed by the hardware processor, a first arterial pressure waveform based on the digital hemodynamic data, and extracting, by the signal distortion detection code executed by the hardware processor, a plurality of features from the first arterial pressure waveform. The plurality of features indicate distortion of the first arterial pressure waveform associated with fit of the hemodynamic sensor to the patient. The method includes determining, by the signal distortion detection code executed by the hardware processor using a weighting module, a distortion score corresponding to a likelihood that the hemodynamic sensor is not fit to the patient based on a weighted combination of the plurality of features. The method includes invoking, by the signal distortion detection code executed by the hardware processor, a sensory alarm if the distortion score meets a predetermined distortion criterion.

[0114] Additionally and / or alternatively, the method of the preceding paragraph may optionally include any one or more of the following features, configurations, additional components and / or steps.

[0115] A further example of the foregoing method, wherein the predetermined distortion criterion may be based on at least one of a value of the distortion score and a trend of the distortion score over a time interval.

[0116] A further example of any of the foregoing methods, wherein acquiring the first arterial pressure waveform may include converting the digital hemodynamic data through a signal distortion detection code executed by a hardware processor so that the first arterial pressure waveform may represent one of a radial artery pressure waveform and a brachial artery pressure waveform.

[0117] A further example of any of the foregoing methods, wherein the plurality of features may include at least one of mean diastolic pressure, stroke volume, and systemic vascular resistance, each feature being derived from a first arterial pressure waveform representative of a radial artery pressure waveform.

[0118] A further example of any of the foregoing methods may include acquiring, by a signal distortion detection code executed by a hardware processor, a second arterial pressure waveform based on the digital hemodynamic data.

[0119] A further example of any of the foregoing methods, wherein acquiring the second arterial pressure waveform may include converting the digital hemodynamic data through a signal distortion detection code executed by a hardware processor so that the second arterial pressure waveform may represent another of a radial artery pressure waveform and a brachial artery pressure waveform.

[0120] A further example of any of the foregoing methods, wherein the plurality of features may include at least one of a systolic rise area and a systolic exponential decay constant, each feature being derived from a brachial artery waveform.

[0121] A further example of any of the foregoing methods, wherein the plurality of features may include each of mean diastolic pressure, stroke volume, and systemic vascular resistance derived from a first arterial pressure waveform representing a radial arterial pressure waveform, and each of a systolic rise area and a systolic exponential decay constant derived from a second arterial waveform representing a brachial artery pressure waveform.

[0122] A further example of any of the foregoing methods, wherein the plurality of features may include at least one of mean arterial pressure, maximum arterial pressure, minimum arterial pressure, pulse rate, pulse pressure, maximum rate of pressure change with respect to time, minimum rate of pressure change with respect to time, end-diastolic pressure, diastolic gradient, systolic gradient, pulse transit time, cardiac output, stroke volume, blood temperature, systolic rise area, diastolic mean pressure, systemic vascular resistance, and an exponential constant associated with systolic pressure decay.

[0123] A further example of any of the foregoing methods, wherein at least one of the plurality of features may be extracted from a digital artery pressure waveform, a radial artery pressure waveform, or a brachial artery pressure waveform.

[0124] A further example of any of the foregoing methods, wherein the distortion score may be equal to a value within a nominal range of distortion scores subdivided into at least two subranges, and wherein the predetermined distortion criterion is based on a value of the distortion score associated with a threshold between the subranges.

[0125] A further example of any of the foregoing methods, wherein acquiring digital hemodynamic data may include receiving a signal indicative of an arterial pressure waveform from a cuff.

[0126] A further example of any of the foregoing methods, wherein acquiring digital hemodynamic data may include controlling air pressure within a bladder of the cuff based on a blood pressure of the patient.

[0127] A further example of any of the foregoing methods, wherein a plurality of features may be extracted from a time interval commencing with application of the hemodynamic sensor to the patient or a physiological change in the patient.

[0128] A further example of any of the foregoing methods, wherein the time interval may be greater than or equal to twenty seconds and less than or equal to sixty seconds.

[0129] Computer readable non-transitory medium

[0130] A computer-readable non-transitory medium according to an example of the present disclosure includes instructions stored thereon and other possible components, which when executed by a hardware processor initiate a method. The method includes continuously acquiring digital hemodynamic data converted by an analog-to-digital converter (ADC) from a signal received from a hemodynamic sensor, and acquiring a first arterial pressure waveform based on the digital hemodynamic data. The method includes extracting a plurality of features from the first arterial pressure waveform, each feature indicating a distortion of the first arterial pressure waveform associated with the fit of the hemodynamic sensor to the patient. The method includes determining a distortion score corresponding to the likelihood that the hemodynamic sensor is not fitted to the patient based on a weighted combination of the plurality of features, and invoking a sensory alarm if the distortion score meets a predetermined distortion criterion.

[0131] Additionally and / or alternatively, the computer-readable non-transitory medium of the preceding paragraph may optionally include any one or more of the following features, configurations, additional components and / or instruction steps.

[0132] A further example of the foregoing computer readable non-transitory medium, wherein the predetermined distortion criterion may be based on at least one of a value of the distortion score and a trend of the distortion score over a time interval.

[0133] A further example of any of the foregoing computer-readable non-transitory media, wherein the distortion score can be equal to a value within a nominal range of distortion scores subdivided into at least two sub-ranges, and wherein the predetermined distortion criterion can be based on a value of the distortion score associated with a threshold between the sub-ranges.

[0134] A further example of any of the foregoing computer-readable non-transitory media, wherein acquiring the first arterial pressure waveform may include converting the digital hemodynamic data such that the first arterial pressure waveform may represent one of a radial artery pressure waveform and a brachial artery pressure waveform.

[0135] A further example of any of the foregoing computer-readable non-transitory media, wherein the plurality of characteristics may include at least one of mean diastolic pressure, stroke volume, and systemic vascular resistance derived from a first arterial pressure waveform representative of a radial arterial pressure waveform.

[0136] A further example of any of the foregoing computer-readable non-transitory media may include instructions that, when executed by a hardware processor, may acquire a second arterial pressure waveform based on the digital hemodynamic data.

[0137] A further example of any of the foregoing computer readable non-transitory media, wherein acquiring the second arterial pressure waveform may include converting the digital hemodynamic data such that the second arterial pressure waveform represents the other of a radial artery pressure waveform and a brachial artery pressure waveform.

[0138] A further example of any of the foregoing computer readable non-transitory media, wherein the plurality of features may include at least one of a systolic rise area and a systolic exponential decay constant derived from a second arterial pressure waveform representative of a brachial artery waveform.

[0139] A further example of any of the foregoing computer-readable non-transitory media, wherein the plurality of features may include each of mean diastolic pressure, stroke volume, and systemic vascular resistance derived from a first arterial pressure waveform representing a radial artery pressure waveform, and each of a systolic rise area and an exponential decay constant of systole derived from a brachial artery waveform.

[0140] A further example of any of the foregoing computer-readable non-transitory media, wherein the plurality of characteristics may include at least one of mean arterial pressure, maximum arterial pressure, minimum arterial pressure, pulse rate, pulse pressure, maximum rate of pressure change with respect to time, minimum rate of pressure change with respect to time, end-diastolic pressure, diastolic gradient, systolic gradient, pulse transit time, cardiac output, stroke volume, blood temperature, systolic rise area, diastolic mean pressure, systemic vascular resistance, and an exponential constant associated with systolic pressure decay.

[0141] A further example of any of the foregoing computer readable non-transitory media, wherein the first arterial waveform can represent a digital arterial pressure waveform, a radial arterial pressure waveform, or a brachial arterial pressure waveform.

[0142] A further example of any of the foregoing computer readable non-transitory media, wherein the second arterial waveform can represent a digital arterial pressure waveform, a radial arterial pressure waveform, or a brachial arterial pressure waveform.

[0143] A further example of any of the foregoing computer readable non-transitory media, wherein a plurality of features can be extracted from a time interval commencing with application of a hemodynamic sensor to a patient or a physiological change in the patient.

[0144] A further example of any of the foregoing computer readable non-transitory media, wherein the time interval may be greater than or equal to twenty seconds and less than or equal to sixty seconds.

[0145] Although the present invention has been described with reference to exemplary embodiments, it will be appreciated by those skilled in the art that various changes may be made and equivalents of its elements may be substituted without departing from the scope of the present invention. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the present invention without departing from its basic scope. Therefore, the present invention is not intended to be limited to the specific embodiments disclosed, but the present invention will include all examples falling within the scope of the appended claims.

Claims

1. A system for monitoring hemodynamic data of a patient, the system comprising: A hardware unit, which includes a hardware processor, an analog-to-digital converter (ADC), and a system memory; a signal distortion detection code stored in said system memory and comprising a weighting module; a hemodynamic sensor coupled to the hardware unit and fitted around the patient's appendage; as well as sensory alarms; wherein the hardware processor is configured to execute the signal distortion detection code to: continuously acquiring digital hemodynamic data converted by the ADC from signals received from the hemodynamic sensor; acquiring a first arterial pressure waveform based on the digital hemodynamic data; extracting a plurality of features from the first arterial pressure waveform, wherein the plurality of features are indicative of fit of the hemodynamic sensor to the patient; determining, using the weighting module, a distortion score based on a weighted combination of the plurality of features, the distortion score corresponding to a likelihood that the hemodynamic sensor is not properly fitted to the patient; and If the distortion score meets a predetermined distortion criterion, the sensory alert is invoked.

2. The system of claim 1, wherein the hemodynamic sensor is a finger cuff, the finger cuff comprising: an inflatable bladder configured to wrap around a finger of the patient; Light Emitting Diodes; as well as a photodiode spaced apart from the light emitting diode along a longitudinal dimension of the inflatable airbag; The first arterial pressure waveform represents the arterial pressure in the finger of the patient.

3. The system according to claim 2, further comprising: A pressure control unit is coupled to the cuff through a tube to receive the signal, wherein the signal represents the air pressure within the inflatable bladder.

4. A system according to claim 3, wherein the pressure control unit includes a control valve and a pump arranged along a catheter, the catheter connecting the tube to an air inlet connected to the atmospheric environment, and wherein the control valve is operable to change the air pressure within the inflatable bladder based on the patient's blood pressure.

5. The system according to claim 3, further comprising: A patient monitoring device, wherein the hardware unit is housed within or attached to the pressure control unit.

6. The system of claim 1, wherein acquiring the first arterial pressure waveform comprises: The digital hemodynamic data is converted so that the first arterial pressure waveform represents one of a radial artery pressure waveform and a brachial artery pressure waveform.

7. The system of claim 6, wherein the plurality of characteristics comprises at least one of mean diastolic pressure, stroke volume, and systemic vascular resistance derived from the first arterial pressure waveform representative of the radial artery pressure waveform.

8. The system of claim 7, wherein the hardware processor is configured to execute the signal distortion detection code to: Acquiring a second arterial pressure waveform based on the digital hemodynamic data, wherein acquiring the second arterial pressure waveform comprises: The digital hemodynamic data is converted so that the second arterial pressure waveform represents the other of the radial artery pressure waveform and the brachial artery pressure waveform.

9. The system of claim 8, wherein the plurality of features comprises at least one of a systolic rise area and a systolic exponential decay constant, each feature being derived from the brachial artery waveform.

10. The system of claim 9, wherein the plurality of features comprises each of mean diastolic pressure, stroke volume, and systemic vascular resistance derived from the first arterial pressure waveform representative of the radial arterial pressure waveform, and each of systolic rise area and a systolic exponential decay constant derived from the second arterial pressure waveform representative of the brachial arterial pressure waveform.

11. The system of claim 1 , wherein the plurality of characteristics comprises at least one of mean arterial pressure, maximum arterial pressure, minimum arterial pressure, pulse rate, pulse pressure, maximum rate of change of pressure over time, minimum rate of change of pressure over time, end-diastolic pressure, diastolic gradient, systolic gradient, pulse transit time, cardiac output, stroke volume, blood temperature, systolic rise area, diastolic mean pressure, systemic vascular resistance, and an exponential constant associated with systolic pressure decay.

12. The system of claim 1, wherein at least one of the plurality of features is extracted from a digital artery pressure waveform, a radial artery pressure waveform, or a brachial artery pressure waveform.

13. The system of claim 1, wherein the predetermined distortion criterion is based on at least one of a value of the distortion score and a trend of the distortion score over a time interval.

14. The system of claim 1, wherein the distortion score is equal to a value within a nominal range of distortion scores subdivided into at least two subranges, and the value of the distortion score is associated with a threshold between subranges.

15. The system of claim 1, wherein extracting the plurality of features from the first arterial pressure waveform comprises extracting the plurality of features from a time interval beginning with application of the hemodynamic sensor to the patient or a physiological change in the patient.

16. The system of claim 15, wherein the time interval is greater than or equal to twenty seconds and less than or equal to sixty seconds.

17. A method for use by a system for monitoring hemodynamic data of a patient, the system comprising a hemodynamic sensor, a sensory alarm, and a hardware unit, the hardware unit comprising a hardware processor, an analog-to-digital converter ADC, and a signal distortion detection code, the signal distortion detection code being stored in a system memory and comprising a weighting module, the method comprising: continuously acquiring, by the signal distortion detection code executed by the hardware processor, digital hemodynamic data converted by the ADC from a signal received from the hemodynamic sensor; acquiring, by the signal distortion detection code executed by the hardware processor, a first arterial pressure waveform based on the digital hemodynamic data; extracting, by the signal distortion detection code executed by the hardware processor, a plurality of features from the first arterial pressure waveform, the plurality of features indicative of distortion of the first arterial pressure waveform associated with fit of the hemodynamic sensor to the patient; determining, by the signal distortion detection code executed by the hardware processor using the weighting module, a distortion score corresponding to a likelihood of a misfit between the hemodynamic sensor and the patient based on a weighted combination of the plurality of features; as well as The sensory alert is invoked, by the signal distortion detection code executed by the hardware processor, if the distortion score meets predetermined distortion criteria.

18. The method according to claim 17, wherein the predetermined distortion criterion is based on at least one of a value of the distortion score and a trend of the distortion score over a time interval; and Wherein obtaining the first arterial pressure waveform comprises: The digital hemodynamic data is converted, by the signal distortion detection code executed by the hardware processor, so that the first arterial pressure waveform represents one of a radial artery pressure waveform and a brachial artery pressure waveform.

19. The method of claim 18, wherein the plurality of characteristics comprises at least one of mean diastolic pressure, stroke volume, and systemic vascular resistance, each characteristic being derived from the first arterial pressure waveform representative of the radial arterial pressure waveform.

20. A computer-readable non-transitory medium having instructions stored thereon, the instructions, when executed by a hardware processor, initiating a method, the method comprising: continuously acquiring digital hemodynamic data converted by an analog-to-digital converter ADC from a signal received from a hemodynamic sensor; acquiring a first arterial pressure waveform based on the digital hemodynamic data; extracting a plurality of features from the first arterial pressure waveform, each feature indicative of a distortion of the first arterial pressure waveform associated with fit of the hemodynamic sensor to the patient; determining a distortion score corresponding to a likelihood of a misfit between the hemodynamic sensor and the patient based on a weighted combination of the plurality of features; as well as If the distortion score meets a predetermined distortion criterion, a sensory alarm is invoked.

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