Hemodynamic monitoring for triage of patients with aortic stenosis

Abstract

A hemodynamic monitor for detecting aortic stenosis includes an integrated hardware unit with a noninvasive blood pressure sensor and a system processor, a system memory, and a display with a user interface. The system memory includes instructions that, when executed by the system processor, are configured to adjust, by a pressure controller, the pressure in an inflatable blood pressure bladder. Patient arterial pressure waveform data is generated based on the adjusted pressure in the inflatable blood pressure bladder for a period of time, and multiple signal measurements are extracted from the patient's arterial pressure waveform data. Input features are extracted from the multiple signal measurements indicative of the patient's aortic stenosis score, and the patient's aortic stenosis score is determined based on the extracted input features.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 375,843, filed September 15, 2022, entitled "HEMODYNAMIC MONITOR FOR TRIAGING PATIENTS WITH LOW EJECTION FRACTION OR AORTIC STENOSIS," the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to aortic stenosis, and more particularly to systems and methods for detecting aortic stenosis in a patient. [Background technology]

[0003] Aortic stenosis is a condition in which the leaflets of the aortic valve stiffen, causing a narrowing of the aortic valve opening. In patients with aortic stenosis, the aortic valve is unable to open and close fully like a healthy aortic valve. Aortic valve stenosis, and its inability to open and close fully, reduces blood flow through the aortic valve to the systemic circulatory system. Traditionally, aortic stenosis is detected and measured in patients through imaging tests such as echocardiograms, electrocardiograms, chest x-rays, computed tomography (CT) scans, or cardiac magnetic resonance imaging (MRI). Other tests used to detect and measure aortic stenosis include cardiac catheterization and nuclear stress testing. Each of these tests requires highly trained professionals to perform the test and interpret the results. Therefore, patients must visit a cardiologist or other cardiovascular specialist for initial cardiac screening. These tests can be expensive, and it can take days or weeks to determine whether a patient has aortic stenosis and the extent of their condition. A solution is needed that makes it easier for patients to be screened for aortic stenosis with less travel. Preferably, the solution would also reduce the amount of time patients must wait to receive the results of their aortic stenosis screening, allowing them to seek further testing and / or treatment sooner. Summary of the Invention [Means for solving the problem]

[0004] In one example, a hemodynamic monitor for detecting aortic valve stenosis is disclosed. The hemodynamic monitor includes a non-invasive blood pressure sensor with an inflatable blood pressure bladder, a pressure controller pneumatically connected to the inflatable blood pressure bladder, and an optical transmitter and receiver electrically connected to the pressure controller. The hemodynamic monitor further includes an integrated hardware unit with a system processor, a system memory, and a display with a user interface. The system memory includes instructions that, when executed by the system processor, are configured to adjust, by the pressure controller, the pressure in the inflatable blood pressure bladder to maintain a constant arterial volume of the patient for a period of time based on a feedback signal generated by the optical transmitter and receiver. The instructions, when executed by the system processor, are further configured to generate arterial pressure waveform data for the patient based on the adjusted pressure in the inflatable blood pressure bladder over the period of time and extract a plurality of signal measurements from the arterial pressure waveform data for the patient. When executed by the system processor, the instructions are further configured to: extract input features indicative of the patient's aortic stenosis score from the plurality of signal measurements; determine the patient's aortic stenosis score based on the extracted input features; generate a first sensory alert signal configured to generate a first sensory alert indicating that the patient has severe aortic stenosis when the aortic stenosis score exceeds a threshold score, or generate a second sensory alert signal configured to generate a second sensory alert indicating that the patient does not have severe aortic stenosis when the aortic stenosis score is below the threshold score; transmit the first sensory alert signal or the second sensory alert signal to a user interface; and output the first sensory alert or the second sensory alert through the user interface.

[0005] In another example, a hemodynamic monitor for detecting aortic stenosis includes an arterial blood pressure sensor including a housing, a fluid input port connected to a fluid source via tubing, a catheter fluid port connected to a catheter inserted into a patient's arterial system, a pressure transducer in communication with the fluid source through the fluid port, and an I / O cable in electrical communication with the pressure transducer. The hemodynamic monitor further includes an integrated hardware unit with a system processor, system memory, a display with a user interface, and an analog-to-digital converter (ADC). The system memory includes instructions that, when executed by the system processor, are configured to: receive from a pressure transducer an electrical signal based on pressure in the patient's arterial system conducted through a fluid source over a period of time; convert the electrical signal to a digital signal; generate arterial pressure waveform data for the patient based on the digital signal; extract a plurality of signal measurements from the arterial pressure waveform data; extract input features from the plurality of signal measurements indicative of the patient's aortic stenosis score; determine an aortic stenosis score for the patient based on the extracted input features; generate a first sensory alert signal configured to generate a first sensory alert indicating that the patient has severe aortic stenosis when the aortic stenosis score exceeds a threshold score, or generate a second sensory alert signal configured to generate a second sensory alert indicating that the patient does not have severe aortic stenosis when the aortic stenosis score is below the threshold score; transmit the first sensory alert signal or the second sensory alert signal to a user interface; and output the first sensory alert or the second sensory alert through the user interface.

[0006] In a further example, a method for triaging a patient for aortic stenosis is disclosed. The method includes receiving, by a hemodynamic monitor, sensed hemodynamic data representing the patient's arterial pressure waveform. The hemodynamic monitor performs waveform analysis of the sensed hemodynamic data to calculate a plurality of signal measurements of the sensed hemodynamic data. Input features indicative of the patient's aortic stenosis score are extracted by the hemodynamic monitor from the plurality of signal measurements. The hemodynamic monitor determines the patient's aortic stenosis score based on the input features. The patient's aortic stenosis score is output by the hemodynamic monitor to a display. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a perspective view of an exemplary hemodynamic monitor that analyzes a patient's arterial pressure to provide a medical professional with the patient's aortic stenosis score. [Figure 2] FIG. 1 is a perspective view of an exemplary minimally invasive pressure sensor for sensing hemodynamic data representative of a patient's arterial pressure. [Figure 3] FIG. 1 is a perspective view of an exemplary non-invasive sensor for sensing hemodynamic data representative of a patient's arterial blood pressure. [Figure 4] FIG. 1 is a block diagram illustrating an exemplary hemodynamic monitoring system that determines a patient's aortic stenosis score based on a set of input features derived from signal measurements of the patient's arterial pressure waveform. [Figure 5] FIG. 1 is a schematic diagram of a method for patient triage based on aortic stenosis score. [Figure 6] 1 is a diagram of a first clinical data set, a second clinical data set, a third clinical data set, and a fourth clinical data set used for data mining and machine training of a hemodynamic monitoring system. FIG. [Figure 7] 1 is a flow diagram for extracting a set of input features derived from signal measurements of a patient's arterial pressure waveform for training a machine learning model of a hemodynamic monitoring system. [Figure 8]1 is a graph illustrating an example trajectory of an arterial pressure waveform including example indices corresponding to signal measurements used to extract input features for determining a patient's aortic stenosis score. DETAILED DESCRIPTION OF THE INVENTION

[0008] As described herein, a hemodynamic monitoring system uses a patient's arterial waveform to detect the patient's aortic stenosis. The hemodynamic monitoring system uses machine learning to extract a set of input features from the patient's arterial pressure. The set of input features is used by the hemodynamic monitoring system to determine whether a patient has aortic stenosis during a visit to a primary care physician's office, in an emergency medical setting, or in any other patient care setting. The hemodynamic monitoring system may even be made available over the counter for patients to use at home.

[0009] Depending on the degree of aortic stenosis detected by the hemodynamic monitoring system, the hemodynamic monitoring system may issue a signal or alarm to a medical professional and / or the patient to alert the medical professional and / or the patient that the patient has mild, moderate, or severe aortic stenosis. Hemodynamic monitoring systems are described in more detail below with reference to Figures 1-8.

[0010] FIG. 1 is a perspective view of a hemodynamic monitor 110 capable of detecting aortic stenosis in a patient. Although hemodynamic monitor 110 is discussed below as detecting aortic stenosis in a patient, in other embodiments, hemodynamic monitor 110 may be used to detect additional valvular heart diseases. For example, hemodynamic monitor 110 may be used to detect aortic stenosis, mitral stenosis, mitral regurgitation, mitral valve prolapse, aortic regurgitation, and hypertrophic cardiomyopathy. As shown in FIG. 1 , hemodynamic monitor 110 includes a display 112 that, in the example of FIG. 1 , presents a graphical user interface including control elements (e.g., graphical control elements) that enable user interaction with hemodynamic monitor 110. As described further below, hemodynamic monitor 110 may also include multiple input and / or output (I / O) connectors configured for wired connection (e.g., electrical and / or communication connection) with one or more peripheral components, such as one or more hemodynamic sensors. For example, as shown in Figure 1, hemodynamic monitor 110 may include I / O connectors 114. While the example of Figure 1 shows five separate I / O connectors 114, it should be understood that in other examples, hemodynamic monitor 110 may include fewer than five I / O connectors or more than five I / O connectors. In still other examples, hemodynamic monitor 110 may not include I / O connectors 114, but rather may communicate wirelessly with various peripheral devices.

[0011] As described further below, hemodynamic monitor 110 includes one or more processors and a computer-readable memory storing aortic stenosis software code executable to determine a patient's aortic stenosis score based on the patient's sensed hemodynamic data. Hemodynamic monitor 110 can receive sensed hemodynamic data representing the patient's arterial pressure waveform, such as via one or more hemodynamic sensors connected to hemodynamic monitor 110 via I / O connector 114. As described further below, hemodynamic monitor 110 executes the aortic stenosis software code to use the sensed hemodynamic data to obtain a plurality of aortic stenosis profiling parameters (e.g., input features), which may include one or more vital sign parameters characterizing the patient's vital sign data, as well as difference parameters and composite parameters derived from the one or more vital sign parameters.

[0012] 1, hemodynamic monitor 10 can present a graphical user interface on display 112. Display 112 can 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 format. In some examples, such as the example of FIG. 1, display 112 can 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.

[0013] 2 is a perspective view of a hemodynamic sensor 116 that may be attached to a patient for sensing hemodynamic data representative of the patient's arterial blood pressure. The hemodynamic sensor 116 shown in FIG. 2 is an example of a minimally invasive hemodynamic sensor that may be attached to the patient via, for example, a radial artery catheter inserted in the patient's arm. In another example, the hemodynamic sensor 116 may be attached to the patient via a femoral artery catheter inserted in the patient's leg.

[0014] As shown in FIG. 2 , hemodynamic sensor 116 includes a housing 118, a fluid input port 120, a catheter-side fluid port 122, and an I / O cable 124. Fluid input port 120 is configured to be connected to a fluid source, such as a saline bag or other fluid input source, via tubing or other hydraulic connection. Catheter-side fluid port 122 is configured to be connected to a catheter (e.g., a radial artery catheter or a femoral artery catheter) inserted into a patient's arm (i.e., a radial artery catheter) or a patient's leg (i.e., a femoral artery catheter) via tubing or other hydraulic connection. I / O cable 124 is configured to connect to hemodynamic monitor 110, for example, via one or more of I / O connectors 114 ( FIG. 1 ). The housing 118 of the hemodynamic sensor 116 houses one or more pressure transducers, communication circuitry, processing circuitry, and corresponding electrical components for sensing fluid pressure corresponding to the patient's arterial pressure, which is transmitted via I / O cable 124 to the hemodynamic monitor 10 (FIG. 1).

[0015] In operation, a column of fluid (e.g., saline) is introduced from a fluid source (e.g., a saline bag) through hemodynamic sensor 116 via fluid input port 120 to catheter-side fluid port 122 toward a catheter inserted into a patient. Arterial pressure is transmitted through the fluid column to a pressure sensor located within housing 116, which senses the pressure of the fluid column. Hemodynamic sensor 116 converts the sensed pressure of the fluid column into an electrical signal via a pressure transducer and outputs a corresponding electrical signal to hemodynamic monitor 110 ( FIG. 1 ) via I / O cable 124. Hemodynamic sensor 116 thus transmits analog sensor data (or a digital representation of the analog sensor data) to hemodynamic monitor 110 ( FIG. 1 ) that represents substantially continuous beat-to-beat monitoring of the patient's arterial pressure.

[0016] FIG. 3 is a perspective view of a hemodynamic sensor 126 for sensing hemodynamic data representative of a patient's arterial pressure. The hemodynamic sensor 126 shown in FIG. 3 is an example of a non-invasive hemodynamic sensor that may be attached to a patient via one or more finger cuffs to sense data representative of the patient's arterial pressure. As shown in FIG. 3, the hemodynamic sensor 126 includes an inflatable finger cuff 128 and a cardiac reference sensor 130. The inflatable finger cuff 128 includes an inflatable blood pressure bladder configured to inflate and deflate as controlled by a pressure controller (not shown) whose air is connected to the inflatable finger cuff 128. The inflatable finger cuff 128 also includes an optical (e.g., infrared) transmitter and an optical receiver electrically connected to the pressure controller (not shown). The optical transmitter and optical receiver can measure the changing volume of the artery below the finger cuff. The optical transmitter and optical receiver may be positioned to transmit and receive light between them through the inflatable blood pressure bladder.

[0017] In operation, the pressure controller continuously adjusts the pressure within the finger cuff to maintain a constant arterial volume of the finger (i.e., the arterial unloaded volume) as measured via the optical transmitter and receiver of the inflatable finger cuff 128. The pressure applied by the pressure controller to continuously maintain the unloaded volume represents the finger's blood pressure and is communicated by the pressure controller to the hemodynamic monitor 110 shown in FIG. 1. The cardiac reference sensor 130 measures the hydrostatic height difference between the height at which the finger is held and the reference height for pressure measurement, which is typically the height of the heart. Thus, the hemodynamic sensor 126 transmits sensor data representing a substantially continuous beat-to-beat monitoring of the patient's arterial pressure waveform.

[0018] 4 is a block diagram of a hemodynamic monitoring system 132 that determines an aortic stenosis score for a patient 136 based on a set of aortic stenosis profiling parameters (also referred to as input features) derived from the patient's 136 arterial pressure. The hemodynamic monitoring system 132 monitors the patient's 136 arterial pressure and provides an aortic stenosis score to a healthcare professional 138. If the patient's 136 aortic stenosis score is mild, moderate, or severe, the healthcare professional 138 can respond to the aortic stenosis score by recommending further testing or treatment for the patient 136. If the patient's 136 aortic stenosis score is normal, the healthcare professional 138 can respond to the aortic stenosis score by informing the patient 136 that the aortic valve is healthy.

[0019] As shown in FIG. 4 , hemodynamic monitoring system 132 includes hemodynamic monitor 110 and hemodynamic sensor 134. Hemodynamic monitoring system 132 may be implemented in a primary care physician's office during a routine physical exam or checkup, or in another patient care setting, such as an ICU, OR, or any other patient care setting. Similarly, hemodynamic monitor 110 may be used at home by patient 136 for self-screening to determine whether patient 136 needs to see a physician or specialist. As shown in FIG. 4 , the patient care setting may include patient 136 and healthcare worker 138 who are trained to utilize hemodynamic monitoring system 132.

[0020] Hemodynamic monitor 110, as described above with respect to FIG. 1, may be an integrated hardware unit including, for example, system processor 140, system memory 142, display 112, analog-to-digital converter (ADC) 144, and digital-to-analog converter (DAC) 146. In other examples, any one or more components and / or described functionality of hemodynamic monitor 110 may be distributed across multiple hardware units. For example, in some examples, display 112 may be a separate display device separate from and operably coupled to hemodynamic monitor 110. While generally shown and described as an integrated hardware unit in the example of FIG. 4, it should be understood that hemodynamic monitor 110 may include any combination of devices and components electrically, communicatively, or otherwise operably connected to achieve the functionality attributed to hemodynamic monitor 110 herein.

[0021] As shown in FIG. 4 , the system memory 142 stores aortic stenosis software code 148. The aortic stenosis software code 148 includes a first module 150 for extracting and calculating waveform features from the arterial pressure of the patient 136, a second module 151 for extracting input features from the waveform features, and a third module 152 for determining an aortic stenosis score for the patient 136 based on the input features. The display 112 provides a user interface 154, which includes control elements 156 that enable user interaction with the hemodynamic monitor 110 and / or other components of the hemodynamic monitoring system 132. The user interface 154 as shown in FIG. 4 also provides a sensory alarm 158 to alert a medical professional if the patient 136 has a mild, moderate, or severe aortic stenosis score. The sensory alert 158 ​​may be embodied as one or more of a visual alert, an audible alert, a tactile alert, or other type of sensory alert. For example, the sensory alert 158 ​​may be invoked as any combination of flashing and / or colored graphics presented by the user interface 154 on the display 112, a display of an aortic stenosis score via the user interface 154 on the display 112, an audible warning such as a siren or repeating tone, and a tactile alert configured to vibrate the hemodynamic monitor 110 or to cause the hemodynamic monitor 110 to convey a physical stimulus that is perceptible to the medical professional 138 or other user.

[0022] Hemodynamic sensor 134 may be attached to patient 136 to sense hemodynamic data representative of the patient's 136 arterial pressure waveform. Hemodynamic sensor 134 is operably connected (e.g., electrically and / or communicatively connected via a wired or wireless connection or both) to hemodynamic monitor 110 to provide the sensed hemodynamic data to hemodynamic monitor 110. In some examples, hemodynamic sensor 134 provides the hemodynamic data representative of the patient's 136 arterial pressure waveform to hemodynamic monitor 110 as an analog signal, which is converted by ADC 144 into digital hemodynamic data representative of the arterial pressure waveform. In other examples, hemodynamic sensor 134 may provide the sensed hemodynamic data to hemodynamic monitor 110 in digital form, in which case hemodynamic monitor 110 may not include or utilize ADC 144. In yet another example, the hemodynamic sensor 134 can provide hemodynamic data representing the arterial pressure waveform of the patient 136 to the hemodynamic monitor 110 as an analog signal, which is analyzed in analog form by the hemodynamic monitor 110.

[0023] Hemodynamic sensor 134 may be a non-invasive or minimally invasive sensor attached to patient 136. For example, hemodynamic sensor 134 may take the form of minimally non-invasive hemodynamic sensor 116 ( FIG. 2 ), non-invasive hemodynamic sensor 126 ( FIG. 3 ), or other minimally invasive or non-invasive hemodynamic sensor. In some examples, hemodynamic sensor 134 may be non-invasively attached at an extremity of patient 136, such as the wrist, arm, finger, ankle, toe, or other extremity of patient 136. Hemodynamic sensor 134 may thus take the form of a small, lightweight, and comfortable hemodynamic sensor suitable for extended wear by patient 136 to provide substantially continuous beat-to-beat monitoring of arterial blood pressure of patient 136 over extended periods of time, such as several minutes or even several hours. Although the hemodynamic sensor 134 can observe the arterial pressure of the patient 136 over an extended period of time, the hemodynamic sensor 134 only needs to observe the arterial pressure of the patient 136 for a few minutes (e.g., 5 minutes) to provide the hemodynamic monitor 110 with enough data to determine the aortic stenosis score of the patient 136.

[0024] In some examples, the hemodynamic sensor 134 may be configured to sense the arterial pressure of the patient 136 in a minimally invasive manner. For example, the hemodynamic sensor 134 may be attached to the patient 136 via a radial artery catheter inserted in the patient's 136 arm. In other examples, the hemodynamic sensor 134 may be attached to the patient 136 via a femoral artery catheter inserted in the patient's 136 leg. Such minimally invasive techniques may also enable the hemodynamic sensor 134 to provide substantially continuous beat-to-beat monitoring of the patient's 136 arterial pressure over an extended period of time, such as several minutes or hours. While the hemodynamic sensor 134 can observe the patient's 136 arterial pressure over an extended period of time, the hemodynamic sensor 134 only needs to observe the patient's 136 arterial pressure for a few minutes (e.g., 5 minutes) to provide the hemodynamic monitor 110 with sufficient data to determine the patient's 136 aortic stenosis score.

[0025] System processor 140 is a hardware processor configured to execute aortic stenosis software code 148, which implements a first module 150, a second module 151, and a third module 152 to generate an aortic stenosis score for patient 136. Examples of system processor 140 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 circuitry.

[0026] The system memory 142 may be configured to store information within the hemodynamic monitor 110 during operation. In some examples, the system memory 142 is described as a computer-readable storage medium. In some examples, the computer-readable storage medium may include a non-transitory medium. The term "non-transitory" may indicate that the storage medium is not embodied in a carrier wave or propagated signal. In some examples, the non-transitory storage medium may store data that may change over time (e.g., in RAM or cache). The system memory 142 may include volatile and non-volatile computer-readable memory. Examples of volatile memory may include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), and other forms of volatile memory. Examples of non-volatile memory may include, for example, a magnetic hard disk, an optical disk, flash memory, or forms of electrically programmable memory (EPROM) or electrically erasable programmable memory (EEPROM).

[0027] Display 112 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 presenting information graphically to a user. User interface 154 may include graphical and / or physical control elements that enable user input to interact with hemodynamic monitor 110 and / or other components of hemodynamic monitoring system 132. In some examples, user interface 154 may take the form of a graphical user interface (GUI) that presents graphical control elements presented on a touch-sensitive and / or presence-sensitive display screen of display 112, for example. In such examples, user input may be received in the form of gesture input, such as touch gestures, scrolling gestures, zoom gestures, or other gesture input. In some examples, user interface 154 may take the form of and / or include physical control elements, such as physical buttons, keys, knobs, or other physical control elements configured to receive user input to interact with components of hemodynamic monitoring system 132.

[0028] In operation, hemodynamic sensor 134 senses hemodynamic data representing the arterial pressure waveform of patient 136. Hemodynamic sensor 134 provides the hemodynamic data (e.g., as analog sensor data) to hemodynamic monitor 110. ADC 144 converts the analog hemodynamic data into digital hemodynamic data representing the patient's arterial pressure waveform.

[0029] The system processor 140 executes aortic stenosis software code 148 to determine an aortic stenosis score for the patient 136 using the received hemodynamic data. For example, the system processor 140 can execute a first module 150 to perform waveform analysis of the hemodynamic data to determine a plurality of signal measurements. The plurality of signal measurements are waveform features and hemodynamic effects that characterize individual cardiac cycles of the patient's arterial pressure waveform. The plurality of signal measurements are discussed in more detail below in the discussion of FIG. 8 . The system processor 140 executes a second module 151 to extract input features from the plurality of signal measurements that determine the aortic stenosis score for the patient 136. The system processor 140 executes a third module 152 to determine the aortic stenosis score for the patient 136 based on the input features.

[0030] If the patient's 136 aortic stenosis score is within a first range (e.g., 0 to 40), the system processor 140 invokes a sensory alert 158 ​​on the user interface 154 to send a first sensory signal to inform the medical professional 138 that the patient's 136 aortic valve is healthy. When the hemodynamic monitor 110 determines that the patient 136 has a normal aortic stenosis score, further screening or testing of the patient 136 for aortic stenosis is likely not to occur.

[0031] If the system processor 140 and the third module 152 determine that the aortic stenosis score of the patient 136 is within a second range (e.g., 41 to 60), the system processor 140 may determine that the aortic stenosis score is within a 1.5 cm 2 Invoke a sensory alert 158 ​​on the user interface 154 to send a second sensory signal to alert the medical professional 138 that the patient 136 has a mild aortic stenosis score, indicating an aortic valve area greater than or equal to 100 mm Hg. The medical professional 138 can respond to the patient's 136 mild aortic stenosis score by recommending that the patient 136 undergo further testing and investigation to confirm the health of the patient's 136 aortic valve.

[0032] If the system processor 140 and the third module 152 determine that the aortic stenosis score of the patient 136 is within a third range (e.g., 61 to 80), the system processor 140 may determine that the aortic stenosis score is within a 1.0 cm 2 ~1.5cm 2 Invoke a sensory alert 158 ​​on the user interface 154 to send a third sensory signal to alert the medical professional 138 that the patient 136 has a moderate aortic stenosis score, indicating an aortic valve area in the range of 0.05 mm. The medical professional 138 may respond to the patient's 136 moderate aortic stenosis score by encouraging the patient 136 to undergo further testing and investigations sooner to ascertain the health of the patient's 136 aortic valve.

[0033] If the system processor 140 and the third module 152 determine that the aortic stenosis score of the patient 136 is within a fourth range (e.g., 81 to 100), the system processor 140 may determine a value of 1.0 cm 2 The hemodynamic monitor 110 may then invoke a sensory alert 158 ​​on the user interface 154 to send a fourth sensory signal to alert the medical professional 138 that the patient 136 has a severe aortic stenosis score, indicating an aortic valve area less than 100 mm Hg. The medical professional 138 may respond to the patient 136's severe aortic stenosis score by encouraging the patient 136 to immediately undergo further testing and investigations to determine the health of the patient's 136's aortic valve and seek treatment and / or repair of the aortic valve. In this manner, the hemodynamic monitor 110 functions as a screening tool that may be used in a primary care physician's office to detect and identify mild, moderate, or severe aortic stenosis in the patient 136 during a routine physical exam. Similarly, the hemodynamic monitor 110 may be used at home by the patient 136 for self-screening to determine whether the patient 136 needs to see a doctor or specialist.

[0034] In some embodiments, the system processor 140 can determine multiple subsets of input features, each subset of input features relating to a different level or range of aortic stenosis scores. For example, the system processor 140 can execute a first module 150 to perform waveform analysis of hemodynamic data to determine a plurality of signal measurements. The system processor 140 can execute a second module 151 to extract a first subset, a second subset, a third subset, and a fourth subset of input features from the plurality of signal measurements of the patient 136. The first subset of input features are input features used by a third module 152 to determine whether the patient 136 has a normal aortic stenosis score. The second subset of input features are input features used by the third module 152 to determine whether the patient 136 has a mild aortic stenosis score. The third subset of input features are input features used by the third module 152 to determine whether the patient 136 has a moderate aortic stenosis score. The fourth set of input features are input features used by the third module 152 to determine whether the patient 136 has a severe aortic stenosis score. The system processor 140 can execute the first module 150 to extract a single batch of signal measurements for a given unit of time, which can be used by the second module 151 to extract all of the first, second, third, and fourth subsets of input features for that unit of time. The second module 151 can extract all of the first, second, third, and fourth subsets of input features simultaneously from the multiple signal measurements. The system processor 140 can execute a third module 152 to simultaneously calculate the probability of a normal aortic stenosis score, a mild aortic stenosis score, a moderate aortic stenosis score, and a severe aortic stenosis score for the patient 136.

[0035] The aortic stenosis software code 148 of the hemodynamic monitor 110, in some examples, may utilize a multi-classification machine learning model with four labels: normal aortic valve function, mild aortic stenosis, moderate aortic stenosis, and severe aortic stenosis. For example, the processor 140 may output the patient's 136 normal aortic stenosis score to the display 112 along with the patient's 136 mild, moderate, and severe aortic stenosis scores, so that all four probabilities are compared together: the probability that the patient 136 has normal aortic valve function, the probability that the patient 136 has mild aortic stenosis, the probability that the patient 136 has moderate aortic stenosis, and the probability that the patient 136 has severe aortic stenosis. The normal, mild, moderate, and severe aortic stenosis scores of the patient 136, displayed together on the display 112 of the hemodynamic monitor 110, allow the medical professional 138 to better understand and ascertain whether the patient 136 has normal aortic valve function, mild aortic stenosis, moderate aortic stenosis, or severe aortic stenosis. As discussed below with respect to FIG. 5, the hemodynamic monitor 110 is a fast and efficient tool for screening and triaging the patient 136 before referring the patient 136 for more time-consuming and costly testing.

[0036] FIG. 5 is a perspective view of a hemodynamic monitoring system 132 and a schematic diagram of a method for triaging a patient 136 based on the patient's 136 aortic stenosis score. As shown in FIG. 5, the hemodynamic monitoring system 132 includes a hemodynamic monitor 110 and a hemodynamic sensor 134. In the embodiment of FIG. 5, the hemodynamic sensor 134 is a non-invasive hemodynamic sensor 126 (described in detail above with respect to FIG. 3 ) that can be attached to the patient 136 via one or more finger cuffs to sense data representative of the patient's 136 arterial blood pressure. In the embodiment of FIG. 5, the hemodynamic monitor 110 is a small, wearable unit that can be strapped to the patient's 136 arm and connected to the hemodynamic sensor 134 to receive sensed data representative of the patient's 136 arterial blood pressure. The embodiment of the hemodynamic monitoring system 132 of FIG. 5 can operate and function as described above with respect to FIG. 4 to determine the patient's 136 aortic stenosis score. During a routine medical checkup at a primary care physician's office, hemodynamic monitoring system 132 may be connected to the hand and arm of patient 136 and sensed hemodynamic data of patient 136 may be provided to hemodynamic monitor 110, thereby quickly screening patient 136 for aortic stenosis and triaging patient 136. A few minutes (e.g., 5 minutes) after providing the sensed hemodynamic data of patient 136 to hemodynamic monitor 110, hemodynamic monitor 110 outputs an aortic stenosis score for patient 136 to display 112. In some embodiments, hemodynamic monitor 110 may also output the aortic stenosis score for patient 136 to a mobile device of patient 136.

[0037] Hemodynamic monitor 110 may color-code and / or mark patient 136's aortic stenosis score on display 112 depending on whether the aortic stenosis score is normal, mild, moderate, or severe. As discussed above with respect to FIG. 4 , a normal aortic stenosis score may range from 0 to 40, a mild aortic stenosis score may range from 40 to 60, a moderate aortic stenosis score may range from 61 to 80, and a severe aortic stenosis score may range from 81 to 100. If hemodynamic monitor 110 determines that patient 136 has a normal aortic stenosis score, hemodynamic monitor 110 may output a numeric green score ranging from 0 to 40 on display 112. If hemodynamic monitor 110 determines that patient 136 has a mild aortic stenosis score, the hemodynamic monitor may output a yellow score on display 112, with a numerical value ranging from 41 to 60. If hemodynamic monitor 110 determines that patient 136 has a moderate aortic stenosis score, the hemodynamic monitor may output a orange score on display 112, with a numerical value ranging from 61 to 80. If hemodynamic monitor 110 determines that patient 136 has a severe aortic stenosis score, the hemodynamic monitor 110 may output a red score on display 112, with a numerical value ranging from 81 to 100. The aortic stenosis scores listed above are given as examples and may be modified or adjusted in other embodiments while remaining within the scope of the present disclosure.

[0038] The medical professional 138 (shown in FIG. 4 ) in this scenario may be a primary care physician or nurse conducting a routine physical examination of the patient 136. After the hemodynamic monitor 110 observes and processes the patient's 136 sensed hemodynamic data for several minutes, the hemodynamic monitor 110 outputs the patient's 136 aortic stenosis score on the display 112. The medical professional 138 can triage the patient 136 based on the patient's 136 aortic stenosis score. If the patient's 136 aortic stenosis score is shown as severe on the display 112, the medical professional 138 can inform the patient 136 that the patient 136 should immediately seek further testing, investigation, and treatment by a cardiovascular specialist. The medical professional 138 can then refer the patient 136 to the cardiovascular specialist for a more detailed examination, such as an echocardiogram or electrocardiogram, on a prioritized schedule. If the patient 136's aortic stenosis score on the display 112 is mild or moderate, the medical professional 138 can inform the patient 136 that the patient 136 should seek further testing, investigation, and treatment from a cardiovascular specialist sooner, and can refer the patient 136 to the specialist on a schedule that is less prioritized than a patient with a severe aortic stenosis score. If the patient 136's aortic stenosis score on the display 112 is normal, the medical professional 138 can inform the patient 136 that their aortic valve is healthy and no additional testing is necessary. As discussed below with respect to Figures 6-8, the machine learning model of the hemodynamic monitor 110 can be trained using a clinical dataset to recognize input features in the patient's 136's arterial pressure waveform and use those input features to determine the patient's 136's aortic stenosis score.

[0039] 6 is a diagram of clinical data 160 used for data mining and machine training of hemodynamic monitor 110 of hemodynamic monitoring system 132. Clinical data 160 includes a first clinical data set 161, a second clinical data set 162, a third clinical data set 163, and a fourth clinical data set 164.

[0040] First clinical dataset 161 includes a collection of arterial pressure waveforms recorded from a first group of individuals each confirmed to have normal aortic valve function. First clinical dataset 161 may be collected from the first group of individuals by an invasive hemodynamic sensor, such as hemodynamic sensor 116 shown in FIG. 2, or by a non-invasive hemodynamic sensor, such as hemodynamic sensor 126 shown in FIG. 3. As each individual in first clinical dataset 161 is connected to a hemodynamic sensor, the hemodynamic sensor records that individual's arterial pressure waveform, and that arterial pressure waveform is tagged with a first label so that that individual's arterial pressure waveform can ultimately be added to first clinical dataset 161. Adding the first label to the arterial pressure waveforms of the individuals in the first group also allows the first clinical dataset 161 to be collected and stored in a common location with the second clinical dataset 162, the third clinical dataset 163, and the fourth clinical dataset 164 without the arterial pressure waveforms of the first clinical dataset 161 being lost or confused with the arterial pressure waveforms of the second clinical dataset 162, the third clinical dataset 163, and the fourth clinical dataset 164.

[0041] After the arterial pressure waveforms of the first clinical dataset 161 are collected and labeled with a first label, the arterial pressure waveforms of the first clinical dataset 161 are ready for use in data mining and machine training of the hemodynamic monitor 110. The arterial pressure waveforms of the first clinical dataset 161 are data mined and used to machine train the hemodynamic monitor 110 to determine a first subset of input features. As discussed above with respect to FIG. 4, the first subset of input features are input features used by the third module 152 to determine whether the patient 136 has a normal aortic stenosis score. As discussed further below with respect to FIGS. 7-8, waveform analysis is performed on the first clinical dataset 161 to calculate a plurality of signal measurements, which are then used to calculate the first subset of input features that best detect and measure normal aortic valve function from the arterial pressure waveform.

[0042] The second clinical data set 162 includes a collection of arterial pressure waveforms recorded from a second group of individuals each with a confirmed case of mild aortic stenosis, where mild aortic stenosis is defined as 1.5 cm 2 A second clinical dataset 162 may be collected from a second group of individuals by an invasive hemodynamic sensor, such as the hemodynamic sensor 116 shown in FIG. 2, or by a non-invasive hemodynamic sensor, such as the hemodynamic sensor 126 shown in FIG. 3. As each individual in the second clinical dataset 162 is connected to a hemodynamic sensor, the hemodynamic sensor records that individual's arterial waveform, and that arterial pressure waveform is tagged with a second label so that the individual's arterial pressure waveform can ultimately be added to the second clinical dataset 162. Adding a second label to the arterial pressure waveforms of individuals in the second group also allows the second clinical dataset 162 to be collected and stored in a common location with the first clinical dataset 161, the third clinical dataset 163, and the fourth clinical dataset 164 without the arterial pressure waveforms of the second clinical dataset 162 being lost or confused with the arterial pressure waveforms of the first clinical dataset 161, the third clinical dataset 163, and the fourth clinical dataset 164.

[0043] After the arterial pressure waveforms of the second clinical dataset 162 have been collected and labeled with the second label, the arterial pressure waveforms of the second clinical dataset 162 are ready for use in data mining and machine training of the hemodynamic monitor 110. The arterial pressure waveforms of the second clinical dataset 162 are data mined and used to machine train the hemodynamic monitor 110 to determine a second subset of input features. As discussed above with respect to FIG. 4, the second subset of input features are input features that the third module 152 uses to determine whether the patient 136 has a mild aortic stenosis score. As discussed further below with respect to FIGS. 7-8, waveform analysis is performed on the second clinical dataset 162 to calculate a plurality of signal measurements, which are then used to identify mild aortic stenosis (1.5 cm) from the arterial pressure waveform. 2 The second subset of input features is used to calculate the best detection and measurement of the aortic valve area (larger aortic valve area).

[0044] A third clinical data set 163 includes a collection of arterial pressure waveforms recorded from a third group of individuals each with a confirmed case of moderate aortic stenosis, where moderate aortic stenosis is defined as 1.0 cm 2 and 1.5cm 2A third clinical dataset 163 may be collected from a third group of individuals by an invasive hemodynamic sensor, such as hemodynamic sensor 116 shown in FIG. 2 , or by a non-invasive hemodynamic sensor, such as hemodynamic sensor 126 shown in FIG. 3 . As each individual in the third clinical dataset 163 is connected to a hemodynamic sensor, the hemodynamic sensor records that individual's arterial pressure waveform, and that arterial pressure waveform is tagged with a third label so that the individual's arterial pressure waveform can ultimately be added to the third clinical dataset 163. Adding a third label to the arterial pressure waveforms of individuals in the third group also allows the third clinical dataset 163 to be collected and stored in a common location with the first clinical dataset 161, the second clinical dataset 162, and the fourth clinical dataset 164 without the arterial pressure waveforms of the third clinical dataset 163 being lost or confused with the arterial pressure waveforms of the first clinical dataset 161, the second clinical dataset 162, and the fourth clinical dataset 164.

[0045] After the arterial pressure waveforms of the third clinical dataset 163 have been collected and labeled with the third label, the arterial pressure waveforms of the third clinical dataset 163 are ready for use in data mining and machine training of the hemodynamic monitor 110. The arterial pressure waveforms of the third clinical dataset 163 are data mined and used to machine train the hemodynamic monitor 110 to determine a third subset of input features. As discussed above with respect to FIG. 4 , the third subset of input features are input features that the third module 152 uses to determine whether the patient 136 has a moderate aortic stenosis score. As discussed further below with respect to FIGS. 7-8 , waveform analysis is performed on the third clinical dataset 163 to calculate a plurality of signal measurements, which are then used to identify a moderate aortic stenosis score (1.0 cm) from the arterial pressure waveform. 2 from 1.5cm 2 is used to calculate a third subset of input features that best detects and measures the aortic valve area (area of ​​the aortic valve in the range

[0046] A fourth clinical data set 164 includes a collection of arterial pressure waveforms recorded from a fourth group of individuals each with a confirmed case of severe aortic stenosis, with severe aortic stenosis being 1.0 cm 2 A fourth clinical dataset 164 may be collected from a fourth group of individuals by an invasive hemodynamic sensor, such as hemodynamic sensor 116 shown in FIG. 2, or by a non-invasive hemodynamic sensor, such as hemodynamic sensor 126 shown in FIG. 3. As each individual in the fourth clinical dataset 164 is connected to a hemodynamic sensor, the hemodynamic sensor records that individual's arterial pressure waveform, and that arterial pressure waveform is tagged with a fourth label so that the individual's arterial pressure waveform can ultimately be added to the fourth clinical dataset 164. Adding a fourth label to the arterial pressure waveforms of individuals in the fourth group also allows the fourth clinical dataset 164 to be collected and stored in a common location with the first clinical dataset 161, the second clinical dataset 162, and the third clinical dataset 163 without the arterial pressure waveforms of the fourth clinical dataset 164 being lost or confused with the arterial pressure waveforms of the first clinical dataset 161, the second clinical dataset 162, and the third clinical dataset 163.

[0047] After the arterial pressure waveforms of the fourth clinical dataset 164 have been collected and labeled with the fourth label, the arterial pressure waveforms of the fourth clinical dataset 164 are ready for use in data mining and machine training of the hemodynamic monitor 110. The arterial pressure waveforms of the fourth clinical dataset 164 are data mined and used to machine train the hemodynamic monitor 110 to determine a fourth subset of input features. As discussed above with respect to FIG. 4, the fourth subset of input features are input features that the third module 152 uses to determine whether the patient 136 has a severe aortic stenosis score. As discussed further below with respect to FIGS. 7-8, waveform analysis is performed on the fourth clinical dataset 164 to calculate a plurality of signal measurements, which are then used to identify an indication of severe aortic stenosis (1.0 cm) from the arterial pressure waveform. 2 is used to calculate a fourth subset of input features that best detects and measures the aortic valve area (area of ​​the aortic valve less than 100 m).

[0048] FIG. 7 is a flow diagram of a method 170 for data mining the clinical data 160 from FIG. 6 to machine train a machine learning model of the hemodynamic monitor 110. The method 170 of FIG. 7 is also discussed with reference to FIG. 8. The method 170 is applied to each of the first clinical data set 161, the second clinical data set 162, the third clinical data set 163, and the fourth clinical data set 164 to train the hemodynamic monitor 110 to find the input features (including the first subset, the second subset, the third subset, and the fourth subset of the input features) previously described with respect to FIGS. 4 and 6. The method 170 is described as being applied to the arterial pressure waveform of the first clinical data set 161.

[0049] To machine train the hemodynamic monitor 110 to identify the first subset of input features illustrated in FIG. 4 , the first subset of input features is first determined by applying method 170 to arterial pressure waveforms of a first clinical dataset 161 of the clinical data 160. A first step 172 of method 170 performs waveform analysis of the arterial pressure waveforms collected in the first clinical dataset 161 to calculate a plurality of signal measurements for the first clinical dataset 161. Performing waveform analysis of the arterial pressure waveforms of the first clinical dataset 161 may include identifying individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset 161. FIG. 8 provides an exemplary graph showing an exemplary trajectory of the arterial pressure waveform with the individual cardiac cycles identified and zoomed in. Next, performing waveform analysis of the arterial pressure waveforms of the first clinical dataset 161 may include identifying a dicrotic notch in each of the individual cardiac cycles of each of the arterial pressure waveforms of the first clinical dataset 161, similar to the example shown in FIG. 8 . Next, waveform analysis of the arterial pressure waveforms of the first clinical data set 161 includes identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each individual cardiac cycle of each of the arterial pressure waveforms of the first clinical data set 161, similar to the example shown in FIG.

[0050] Signal measurements are extracted from each of the systolic rise phase, systolic decay phase, and diastolic phase from each individual cardiac cycle of each of the arterial pressure waveforms of the first clinical data set 161. The signal measurements may correspond to hemodynamic effects from each of the systolic rise phase, systolic decay phase, and diastolic phase from each individual cardiac cycle. These hemodynamic effects may include contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle. The signal measurements calculated or extracted by the waveform analysis of the first step 172 of method 170 include mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic rise phase, systolic decay phase, and diastolic phase from each individual cardiac cycle. The signal measurements may also include heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle of each arterial pressure waveform of the first clinical data set 161.

[0051] After the signal measurements have been determined for the first clinical dataset 161, step 174 of method 170 is performed on the signal measurements of the first clinical dataset 161. Step 174 of method 170 calculates composite measurements between the signal measurements of the first clinical dataset 161. Calculating composite measurements between the signal measurements of the first clinical dataset 161 may include performing steps 176, 178, 180, and 182 shown in FIG. 7 for all signal measurements of the first clinical dataset 161. Step 176 is performed by arbitrarily selecting a subset of signal measurements (e.g., a subset of signal measurements) from the signal measurements of the first clinical dataset 161. Next, as shown in step 178 of FIG. 7, powers of different orders are calculated for each signal measurement of the subset of signal measurements to generate a power of the subset of signal measurements. In step 180 of FIG. 7, the powers of the subset of signal measurements are then multiplied together to generate a product of the powers of the subset of signal measurements. Step 182 involves performing a receiver operating characteristic (ROC) analysis of the product to obtain a composite measure for the subset of signal measures. Steps 176, 178, 180, and 182 are repeated until all of the composite measures have been calculated between all of the signal measures of the first clinical data set 161. The final step 184 involves selecting the signal measures with the most predictive top composite measures (i.e., composite measures that meet a threshold predictive criterion) as the top signal measures for the first clinical data set 161, labeled as the first subset of input features. Once the first subset of input features has been determined, the hemodynamic monitor 110 is trained or programmed to perform waveform analysis on the arterial pressure waveform of the patient 136 (shown in FIG. 4 ), extract the first subset of input features from the arterial pressure waveform of the patient 136, and use the first subset of input features to determine whether the patient 136 has a normal aortic stenosis score.

[0052] Just as method 170 was applied to the arterial pressure waveform of first clinical dataset 161 to determine a first subset of input features, method 170 is applied to second clinical dataset 162 to determine a second subset of input features. Method 170 is also applied to third clinical dataset 163 to determine a third subset of input features. Method 170 is also applied to fourth clinical dataset 164 to determine a fourth subset of input features.

[0053] Discussion of Possible Embodiments The following is a comprehensive description of possible embodiments of the present invention.

[0054] In one example, a method for triaging a patient for aortic stenosis is disclosed. The method includes receiving, by a hemodynamic monitor, sensed hemodynamic data representing the patient's arterial pressure waveform. The hemodynamic monitor performs waveform analysis of the sensed hemodynamic data to calculate a plurality of signal measurements of the sensed hemodynamic data. The method further includes extracting, by the hemodynamic monitor, input features from the plurality of signal measurements indicative of the patient's aortic stenosis score. The hemodynamic monitor determines the patient's aortic stenosis score based on the input features and outputs the patient's aortic stenosis score to a display and / or a mobile device.

[0055] The method of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0056] Further embodiments of the above-described method further comprise the steps of informing the patient and / or medical professional that the aortic stenosis score is normal when the aortic stenosis score is within a first range, alerting the patient and / or medical professional that the aortic stenosis score is mild when the aortic stenosis score is within a second range, alerting the patient and / or medical professional that the aortic stenosis score is moderate when the aortic stenosis score is within a third range, and alerting the patient and / or medical professional that the aortic stenosis score is severe when the aortic stenosis score is within a fourth range.

[0057] A further embodiment of the above method further comprises training a hemodynamic monitor to determine an aortic stenosis score for a patient, the training of the hemodynamic monitor comprising collecting a first clinical data set comprising arterial pressure waveforms from a first group of individuals with normal aortic valve function and collecting a second clinical data set comprising arterial pressure waveforms from a second group of individuals with mild aortic stenosis, wherein mild aortic stenosis is determined to be 1.5 cm or less. 2 and collecting a third clinical data set including arterial pressure waveforms from a third group of individuals with moderate aortic stenosis, wherein moderate aortic stenosis is defined as an aortic valve area greater than 1 cm. 2 and 1.5cm 2 and collecting a fourth clinical data set comprising arterial pressure waveforms from a fourth group of individuals with severe aortic stenosis, wherein severe aortic stenosis is defined as the area of ​​the aortic valve between 1 cm 2performing waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate a plurality of waveform signal measurements; and determining input features by calculating a composite measurement between the plurality of waveform signal measurements, selecting a top signal measurement from the plurality of waveform signal measurements having the most predictive composite measurement, and labeling the top signal measurement as an input feature.

[0058] In another example, a system for triaging patients for aortic stenosis includes a hemodynamic sensor that produces hemodynamic data representative of the patient's arterial pressure waveform. The system further includes a system memory that stores aortic stenosis software code. A user interface of the system includes a display for displaying the patient's aortic stenosis score to a medical professional. The system includes a processor configured to execute the aortic stenosis software code to perform waveform analysis of the hemodynamic data to determine a plurality of signal measurements, extract input features indicative of the patient's aortic stenosis score from the plurality of signal measurements, determine the patient's aortic stenosis score based on the input features, and output the patient's aortic stenosis score to a display of the user interface.

[0059] The system of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0060] The input features of the aortic stenosis software code are determined by machine training, the machine training comprising collecting a first clinical data set including arterial pressure waveforms from a first group of individuals with normal aortic valve function and collecting a second clinical data set including arterial pressure waveforms from a second group of individuals with mild aortic stenosis, wherein mild aortic stenosis is defined as 1.5 cm 2and collecting a third clinical dataset including arterial pressure waveforms from a third group of individuals with moderate aortic stenosis, wherein moderate aortic stenosis is defined as an aortic valve area greater than 1 cm. 2 and 1.5cm 2 and collecting a fourth clinical dataset comprising arterial pressure waveforms from a fourth group of individuals with severe aortic stenosis, wherein severe aortic stenosis is defined as the area of ​​the aortic valve between 1 cm 2 aortic valve area smaller than the aortic valve area; performing waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate a plurality of waveform signal measurements; and determining input features by calculating a composite measurement among the plurality of waveform signal measurements, selecting a top signal measurement from the plurality of waveform signal measurements having a most predictive composite measurement, and labeling the top signal measurement as the input feature.

[0061] A further embodiment of the above system, wherein performing waveform analysis of the labeled arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate a plurality of waveform signal measurements comprises identifying individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set; identifying a dicrotic notch in each of the individual cardiac cycles; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles; and extracting a plurality of waveform signal measurements from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles.

[0062] A further embodiment of the above system, wherein the plurality of waveform signal measurements correspond to hemodynamic effects from each of a systolic upstroke phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

[0063] A further embodiment of the above system, wherein the plurality of waveform signal measurements comprises a mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each individual cardiac cycle.

[0064] A further embodiment of the above system, wherein the plurality of waveform signal measurements comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle.

[0065] a first clinical data set, a second clinical data set, a third clinical data set, and a fourth clinical data set, and wherein calculating a composite measure between the plurality of waveform signal measures of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set comprises: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures; performing step 2 by calculating powers of different orders for each signal measure of the subset of signal measures to generate a power of the subset of signal measures; performing step 3 by multiplying together powers of the signal measures of the subset of signal measures to generate a product of the powers of the subset of signal measures; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the product to obtain a composite measure for the subset of signal measures; and repeating steps 1, 2, 3, and 4 until all of the composite measures have been calculated between all of the plurality of waveform signal measures.

[0066] A further embodiment of the above system, wherein the hemodynamic sensor is a non-invasive hemodynamic sensor attachable to an extremity of the patient.

[0067] A further embodiment of the above system, wherein the hemodynamic sensor is a minimally non-invasive aortic catheter-based hemodynamic sensor.

[0068] A further embodiment of the above system, wherein the hemodynamic sensor produces the hemodynamic data as an analog hemodynamic sensor signal representative of the patient's arterial pressure waveform.

[0069] A further embodiment of the above system, further comprising an analog-to-digital converter that converts the analog hemodynamic sensor signal into digital hemodynamic data representative of the patient's arterial pressure waveform.

[0070] In another example, a method for triaging a patient for aortic stenosis is disclosed. The method includes receiving, by a hemodynamic monitor, sensed hemodynamic data representing the patient's arterial pressure waveform. The hemodynamic monitor performs waveform analysis of the sensed hemodynamic data to calculate a plurality of signal measures of the sensed hemodynamic data. The method further includes extracting, by the hemodynamic monitor, input features from the plurality of signal features indicative of the patient's aortic stenosis score. The hemodynamic monitor determines the patient's aortic stenosis score based on the input features and outputs the patient's aortic stenosis score to a display. The hemodynamic monitor notifies the patient and / or a medical professional that the aortic stenosis score is normal when the aortic stenosis score is within a first range. The hemodynamic monitor alerts the patient and / or a medical professional that the aortic stenosis score is mild when the aortic stenosis score is within a second range. The hemodynamic monitor alerts the patient and / or healthcare professional that the aortic stenosis score is moderate when the aortic stenosis score is within a third range, and alerts the patient and / or healthcare professional that the aortic stenosis score is severe when the aortic stenosis score is within a fourth range.

[0071] The method of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0072] a first subset of input features by collecting a first clinical dataset including arterial pressure waveforms from a first group of individuals having normal aortic valve function; labeling each of the arterial pressure waveforms of the first clinical dataset with a first label; performing waveform analysis of the labeled arterial pressure waveforms of the first clinical dataset to calculate a plurality of waveform signal measures of the first clinical dataset; and determining a first subset of input features by calculating a composite measure among the plurality of waveform signal measures of the first clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the first clinical dataset having the most predictive composite measure, and labeling the top signal measures of the first clinical dataset as the first subset of input features.

[0073] The step of training the hemodynamic monitor to determine the patient's aortic stenosis score comprises collecting a second clinical data set including arterial pressure waveforms from a second group of individuals with mild aortic stenosis, wherein mild aortic stenosis is defined as 1.5 cm 2aortic valve area greater than 100%; labeling each of the arterial pressure waveforms of the second clinical dataset with a second label; performing waveform analysis of the labeled arterial pressure waveforms of the second clinical dataset to calculate a plurality of waveform signal measures of the second clinical dataset; and determining a second subset of input features by calculating a composite measure among the plurality of waveform signal measures of the second clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the second clinical dataset having the most predictive composite measure, and labeling the top signal measures of the second clinical dataset as the second subset of input features.

[0074] The step of training the hemodynamic monitor to determine the patient's aortic stenosis score comprises collecting a third clinical data set including arterial pressure waveforms from a third group of individuals with moderate aortic stenosis, wherein moderate aortic stenosis is between 1 cm and 2 cm. 2 and 1.5cm 2 a third label; performing waveform analysis of the labeled arterial pressure waveforms of the third clinical dataset to calculate a plurality of waveform signal measures of the third clinical dataset; and determining a third subset of input features by calculating a composite measure between the plurality of waveform signal measures of the third clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the third clinical dataset having the most predictive composite measure, and labeling the top signal measures of the third clinical dataset as the third subset of input features.

[0075] The step of training the hemodynamic monitor to determine the patient's aortic stenosis score may include collecting a fourth clinical data set including arterial pressure waveforms from a fourth group of individuals with severe aortic stenosis, the fourth group comprising individuals with severe aortic stenosis, the fourth group comprising individuals with severe aortic stenosis greater than 1 cm. 2a fourth subset of input features by: labeling each of the arterial pressure waveforms of the fourth clinical dataset with a fourth label; performing waveform analysis of the labeled arterial pressure waveforms of the fourth clinical dataset to calculate a plurality of waveform signal measures of the fourth clinical dataset; and determining a fourth subset of input features by calculating a composite measure among the plurality of waveform signal measures of the fourth clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the fourth clinical dataset having the most predictive composite measure, and labeling the top signal measures of the fourth clinical dataset as the fourth subset of input features.

[0076] A further embodiment of the above-described method, wherein the step of performing waveform analysis of the labeled arterial pressure waveforms of the first clinical dataset to calculate a plurality of waveform signal measurements of the first clinical dataset comprises the steps of: identifying individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset; and extracting a plurality of waveform signal measurements of the first clinical dataset from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset.

[0077] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the first clinical dataset correspond to hemodynamic effects from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the first clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

[0078] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the first clinical data set comprise mean, maximum, minimum, time length, area, standard deviation, derivative, and / or morphological measurements from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the first clinical data set.

[0079] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the first clinical dataset comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle in each of the arterial pressure waveforms of the first clinical dataset.

[0080] The step of calculating composite measures between the plurality of waveform signal measures of the first clinical data set includes: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the first clinical data set; performing step 2 by calculating powers of different orders for each signal measure of the subset of signal measures from the plurality of waveform signal measures of the first clinical data set to generate powers of the subset of signal measures from the plurality of waveform signal measures of the first clinical data set; and performing step 3 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the first clinical data set to generate products of the powers of the subset of signal measures from the plurality of waveform signal measures of the first clinical data set. a first clinical data set of a plurality of waveform signal measurements; a second clinical data set of a plurality of waveform signal measurements; a third clinical data set of a plurality of waveform signal measurements; a fourth clinical data set of a plurality of waveform signal measurements; a fourth clinical data set of a plurality of waveform signal measurements; a fourth clinical data set of a plurality of waveform signal measurements; a fifth clinical data set of a plurality of waveform signal measurements; a fifth clinical data set of a plurality of waveform signal measurements; a fifth clinical data set of a plurality of waveform signal measurements; a sixth ...

[0081] A further embodiment of the above-described method, wherein the step of performing waveform analysis of the labeled arterial pressure waveforms of the second clinical dataset to calculate a plurality of waveform signal measurements of the second clinical dataset comprises the steps of: identifying individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset; and extracting a plurality of waveform signal measurements of the second clinical dataset from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset.

[0082] A further embodiment of the above method, wherein the plurality of waveform signal measurements of the second clinical dataset correspond to hemodynamic effects from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the second clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

[0083] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the second clinical dataset comprises mean, maximum, minimum, time length, area, standard deviation, derivative, and / or morphological measurements from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the second clinical dataset.

[0084] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the second clinical dataset comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle in each of the arterial pressure waveforms of the second clinical dataset.

[0085] The step of calculating composite measures between the plurality of waveform signal measures of the second clinical data set includes: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the second clinical data set; performing step 2 by calculating powers of different orders for each of the signal measures of the subset of signal measures from the plurality of waveform signal measures of the second clinical data set to generate powers of the subset of signal measures from the plurality of waveform signal measures of the second clinical data set; and performing step 3 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the second clinical data set to generate products of the powers of the subset of signal measures from the plurality of waveform signal measures of the second clinical data set. and performing step 4 by performing a receiver operating characteristic (ROC) analysis of the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set to obtain a composite measurement for the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set, and repeating steps 1, 2, 3, and 4 until all of the composite measurements have been calculated between all of the plurality of waveform signal measurements of the second clinical data set.

[0086] A further embodiment of the above-described method, wherein the step of performing waveform analysis of the labeled arterial pressure waveforms of the third clinical dataset to calculate a plurality of waveform signal measurements of the third clinical dataset comprises the steps of: identifying individual cardiac cycles in each of the arterial pressure waveforms of the third clinical dataset; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical dataset; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical dataset; and extracting a plurality of waveform signal measurements of the third clinical dataset from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical dataset.

[0087] A further embodiment of the above method, wherein the plurality of waveform signal measurements of the third clinical dataset correspond to hemodynamic effects from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the third clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

[0088] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the third clinical dataset comprises mean, maximum, minimum, time length, area, standard deviation, derivative, and / or morphological measurements from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the third clinical dataset.

[0089] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the third clinical dataset comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle in each of the arterial pressure waveforms of the third clinical dataset.

[0090] The step of calculating composite measures between the plurality of waveform signal measures of the third clinical data set includes: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the third clinical data set; performing step 2 by calculating powers of different orders for each of the signal measures of the subset of signal measures from the plurality of waveform signal measures of the third clinical data set to generate powers of the subset of signal measures from the plurality of waveform signal measures of the third clinical data set; and performing step 3 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the third clinical data set to generate powers of the subset of signal measures from the plurality of waveform signal measures of the third clinical data set. and performing step 4 by performing a receiver operating characteristic (ROC) analysis of the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set to obtain a composite measurement for the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set, and repeating steps 1, 2, 3, and 4 until all of the composite measurements have been calculated between all of the plurality of waveform signal measurements of the third clinical data set.

[0091] A further embodiment of the above-described method, wherein the step of performing waveform analysis of the labeled arterial pressure waveforms of the fourth clinical dataset to calculate a plurality of waveform signal measurements of the fourth clinical dataset comprises the steps of: identifying individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical dataset; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical dataset; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical dataset; and extracting a plurality of waveform signal measurements of the fourth clinical dataset from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical dataset.

[0092] A further embodiment of the above method, wherein the plurality of waveform signal measurements of the fourth clinical dataset correspond to hemodynamic effects from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the fourth clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

[0093] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the fourth clinical data set comprise mean, maximum, minimum, time length, area, standard deviation, derivative, and / or morphological measurements from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the fourth clinical data set.

[0094] A further embodiment of the above-described method, wherein the plurality of waveform signal measurements of the fourth clinical dataset comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle in each of the arterial pressure waveforms of the fourth clinical dataset.

[0095] The step of calculating composite measures between the plurality of waveform signal measures of the fourth clinical data set includes: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the fourth clinical data set; performing step 2 by calculating powers of different orders for each of the signal measures of the subset of signal measures from the plurality of waveform signal measures of the fourth clinical data set to generate powers of the subset of signal measures from the plurality of waveform signal measures of the fourth clinical data set; and performing step 3 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the fourth clinical data set to generate powers of the subset of signal measures from the plurality of waveform signal measures of the fourth clinical data set. and performing step 4 by performing a receiver operating characteristic (ROC) analysis of the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set to obtain a composite measurement for the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set, and repeating steps 1, 2, 3, and 4 until all of the composite measurements have been calculated between all of the plurality of waveform signal measurements of the fourth clinical data set.

[0096] In another example, a method for training a hemodynamic monitor to determine an aortic stenosis score for a patient is disclosed. The method for training the hemodynamic monitor includes collecting a first clinical data set including arterial pressure waveforms from a first group of individuals with normal aortic valve function. A second clinical data set including arterial pressure waveforms from a second group of individuals with mild aortic stenosis is collected. Mild aortic stenosis is defined as a stenosis score of 1.5 cm. 2 The method further includes collecting a third clinical data set including arterial pressure waveforms from a third group of individuals with moderate aortic stenosis. Moderate aortic stenosis is defined as an aortic valve area greater than 1 cm. 2 and 1.5cm 2A fourth clinical data set is collected, including arterial pressure waveforms from a fourth group of individuals with severe aortic stenosis. Severe aortic stenosis is defined as the area of ​​the aortic valve between 1 cm 2 The aortic valve area is defined as a smaller aortic valve area. The method further includes performing a waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate a plurality of waveform signal measurements. The input features are determined by calculating a composite measurement among the plurality of waveform signal measurements, selecting a top signal measurement from the plurality of waveform signal measurements having a most predictive composite measurement, and labeling the top signal measurement as the input feature. The method further includes storing the input features in a memory of the hemodynamic monitor.

[0097] The method of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0098] A further embodiment of the above method, further comprising the steps of connecting a hemodynamic sensor to the hemodynamic monitor and the patient for inputting the patient's sensed arterial pressure waveform into the hemodynamic monitor; extracting, by a processor of the hemodynamic monitor, values ​​of input features of the patient's sensed arterial pressure waveform; determining, by the processor of the hemodynamic monitor, an aortic stenosis score for the patient based on the values ​​of the input features of the sensed arterial pressure waveform; and outputting the patient's aortic stenosis score to a display and / or mobile device.

[0099] A further embodiment of the above method, further comprising the step of notifying the patient and / or medical personnel by the hemodynamic monitor that the aortic stenosis score is normal when the aortic stenosis score is within the first range.

[0100] A further embodiment of the above method, further comprising the step of alerting the patient and / or medical personnel by the hemodynamic monitor that the aortic stenosis score is mild when the aortic stenosis score is within a second range.

[0101] A further embodiment of the above method, further comprising the step of alerting the patient and / or medical personnel by the hemodynamic monitor that the aortic stenosis score is moderate when the aortic stenosis score is within a third range.

[0102] A further embodiment of the above method, further comprising the step of alerting the patient and / or medical personnel by the hemodynamic monitor that the aortic stenosis score is severe when the aortic stenosis score is within a fourth range.

[0103] In another example, a method for triaging a patient for valvular heart disease is disclosed. The method includes receiving, by a hemodynamic monitor, sensed hemodynamic data representing the patient's arterial pressure waveform. The hemodynamic monitor includes performing waveform analysis of the sensed hemodynamic data to calculate a plurality of signal measurements of the sensed hemodynamic data. The method further includes extracting, by the hemodynamic monitor, input features from the plurality of signal measurements indicative of the patient's valvular heart disease score. The hemodynamic monitor determines a valvular heart disease score for the patient based on the input features and outputs the patient's valvular heart disease score to a display and / or a mobile device.

[0104] The method of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0105] Further embodiments of the above method, wherein the valvular heart disease comprises at least one of aortic stenosis, mitral stenosis, mitral regurgitation, mitral valve prolapse, aortic regurgitation, and hypertrophic cardiomyopathy.

[0106] In another example, a system for triaging patients for valvular heart disease includes a hemodynamic sensor that produces hemodynamic data representative of the patient's arterial pressure waveform. The system further includes a system memory that stores valvular heart disease software code. A user interface of the system includes a display for displaying the patient's valvular heart disease score. The system further includes a processor configured to execute the valvular heart disease software code to perform waveform analysis of the hemodynamic data to determine a plurality of signal measurements, extract input features from the plurality of signal measurements that are indicative of the patient's valvular heart disease score, determine the patient's valvular heart disease score based on the input features, and output the patient's valvular heart disease score to the display of the user interface.

[0107] The system of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0108] Further embodiments of the above system, wherein the valvular heart disease comprises at least one of aortic stenosis, mitral stenosis, mitral regurgitation, mitral valve prolapse, aortic regurgitation, and hypertrophic cardiomyopathy.

[0109] In another example, a hemodynamic monitor for detecting aortic valve stenosis is disclosed. The hemodynamic monitor includes a noninvasive blood pressure sensor with an inflatable blood pressure bladder, a pressure controller pneumatically connected to the inflatable blood pressure bladder, and an optical transmitter and receiver electrically connected to the pressure controller. The hemodynamic monitor further includes an integrated hardware unit with a system processor, a system memory, and a display with a user interface. The system memory includes instructions that, when executed by the system processor, are configured to adjust, by the pressure controller, the pressure in the inflatable blood pressure bladder to maintain a constant arterial volume of the patient for a period of time based on a feedback signal generated by the optical transmitter and receiver. The instructions, when executed by the system processor, are further configured to generate arterial pressure waveform data for the patient based on the adjusted pressure in the inflatable blood pressure bladder over the period of time and extract a plurality of signal measurements from the arterial pressure waveform data for the patient. When executed by the system processor, the instructions are further configured to: extract input features indicative of the patient's aortic stenosis score from the plurality of signal measurements; determine the patient's aortic stenosis score based on the extracted input features; generate a first sensory alert signal configured to generate a first sensory alert indicating that the patient has severe aortic stenosis when the aortic stenosis score exceeds a threshold score, or generate a second sensory alert signal configured to generate a second sensory alert indicating that the patient does not have severe aortic stenosis when the aortic stenosis score is below the threshold score; transmit the first sensory alert signal or the second sensory alert signal to a user interface; and output the first sensory alert or the second sensory alert through the user interface.

[0110] The hemodynamic monitor of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0111] The input features of the aortic stenosis software code are determined by machine training, the machine training comprising collecting a first clinical dataset including arterial pressure waveforms from a first group of individuals with normal aortic valve function and collecting a second clinical dataset including arterial pressure waveforms from a second group of individuals with severe aortic stenosis, wherein severe aortic stenosis is defined as 1 cm 2 and collecting a third clinical dataset including arterial pressure waveforms from a third group of individuals with mild aortic stenosis, wherein mild aortic stenosis is defined as an aortic valve area smaller than 1.5 cm. 2 and collecting a fourth clinical dataset including arterial pressure waveforms from a fourth group of individuals with moderate aortic stenosis, wherein moderate aortic stenosis is defined as an aortic valve area greater than 1 cm. 2 and 1.5cm 2 aortic valve area between the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set; performing waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate a plurality of waveform signal measurements; and determining input features by calculating a composite measurement between the plurality of waveform signal measurements, selecting a top signal measurement from the plurality of waveform signal measurements having a most predictive composite measurement, and labeling the top signal measurement as the input feature.

[0112] a dicrotic notch in each of the individual cardiac cycles; a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles; and extracting a plurality of waveform signal measurements from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles.

[0113] A further embodiment of the hemodynamic monitor described above, wherein the plurality of waveform signal measurements correspond to hemodynamic effects from each of a systolic upstroke phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

[0114] A further embodiment of the hemodynamic monitor described above, wherein the plurality of waveform signal measurements comprise mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic upstroke, systolic decay, and diastolic phases from each individual cardiac cycle, and / or heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation, stroke volume variation, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle.

[0115] a first clinical data set, a second clinical data set, a third clinical data set, and a fourth clinical data set, and wherein calculating a composite measure between the plurality of waveform signal measures of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set comprises: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures; performing step 2 by calculating powers of different orders for each signal measure of the subset of signal measures to generate a power of the subset of signal measures; performing step 3 by multiplying together powers of the signal measures of the subset of signal measures to generate a product of the powers of the subset of signal measures; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the product to obtain a composite measure for the subset of signal measures; and repeating steps 1, 2, 3, and 4 until all of the composite measures have been calculated between all of the plurality of waveform signal measures.

[0116] A further embodiment of the hemodynamic monitor described above, wherein the input features comprise a first subset and a second subset, and the instructions, when executed by the system processor, are further configured to: extract the first subset and the second subset of input features simultaneously from the plurality of signal measurements; simultaneously determine the patient's normal aortic stenosis score from the first subset of input features and the patient's severe aortic stenosis score from the second subset of input features; and output the patient's normal aortic stenosis score and the patient's severe aortic stenosis score to a display of a user interface.

[0117] 10. A further embodiment of the hemodynamic monitor as described above, wherein the input features comprise a third subset and a fourth subset, and the instructions, when executed by a system processor, are further configured to: extract a first subset, a second subset, a third subset, and a fourth subset of the input features simultaneously from the plurality of signal measurements; simultaneously determine the patient's normal aortic stenosis score from the first subset of input features, the patient's severe aortic stenosis score from the second subset of input features, the patient's mild aortic stenosis score from the third subset of input features, and the patient's moderate aortic stenosis score from the fourth subset of input features; and output the patient's normal aortic stenosis score, the patient's mild aortic stenosis score, the patient's moderate aortic stenosis score, and the patient's severe aortic stenosis score to a display of a user interface.

[0118] In another example, a hemodynamic monitor for detecting aortic stenosis includes an arterial blood pressure sensor including a housing, a fluid input port connected to a fluid source via tubing, a catheter fluid port connected to a catheter inserted into a patient's arterial system, a pressure transducer in communication with the fluid source through the fluid port, and an I / O cable in electrical communication with the pressure transducer. The hemodynamic monitor further includes an integrated hardware unit with a system processor, system memory, a display with a user interface, and an analog-to-digital converter (ADC). The system memory includes instructions that, when executed by the system processor, are configured to: receive from a pressure transducer an electrical signal based on pressure in the patient's arterial system transmitted through a fluid source over a period of time; convert the electrical signal to a digital signal; generate arterial pressure waveform data for the patient based on the digital signal; extract a plurality of signal measurements from the arterial pressure waveform data; extract input features from the plurality of signal measurements indicative of the patient's aortic stenosis score; determine an aortic stenosis score for the patient based on the extracted input features; generate a first sensory alert signal configured to generate a first sensory alert indicating that the patient has severe aortic stenosis when the aortic stenosis score exceeds a threshold score, or generate a second sensory alert signal configured to generate a second sensory alert indicating that the patient does not have severe aortic stenosis when the aortic stenosis score is below the threshold score; transmit the first sensory alert signal or the second sensory alert signal to a user interface; and output the first sensory alert or the second sensory alert through the user interface.

[0119] The hemodynamic monitor of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0120] The input features of the aortic stenosis software code are determined by machine training, the machine training comprising collecting a first clinical dataset including arterial pressure waveforms from a first group of individuals with normal aortic valve function and collecting a second clinical dataset including arterial pressure waveforms from a second group of individuals with severe aortic stenosis, wherein severe aortic stenosis is defined as 1 cm 2 and collecting a third clinical dataset including arterial pressure waveforms from a third group of individuals with mild aortic stenosis, wherein mild aortic stenosis is defined as an aortic valve area smaller than 1.5 cm. 2 and collecting a fourth clinical dataset including arterial pressure waveforms from a fourth group of individuals with moderate aortic stenosis, wherein moderate aortic stenosis is defined as an aortic valve area greater than 1 cm. 2 and 1.5cm 2 aortic valve area between the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set; performing waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate a plurality of waveform signal measurements; and determining input features by calculating a composite measurement between the plurality of waveform signal measurements, selecting a top signal measurement from the plurality of waveform signal measurements having a most predictive composite measurement, and labeling the top signal measurement as the input feature.

[0121] a dicrotic notch in each of the individual cardiac cycles; a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles; and extracting a plurality of waveform signal measurements from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles.

[0122] A further embodiment of the hemodynamic monitor described above, wherein the plurality of waveform signal measurements correspond to hemodynamic effects from each of a systolic upstroke phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

[0123] A further embodiment of the hemodynamic monitor described above, wherein the plurality of waveform signal measurements comprise mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic upstroke, systolic decay, and diastolic phases from each individual cardiac cycle, and / or heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation, stroke volume variation, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle.

[0124] a first clinical data set, a second clinical data set, a third clinical data set, and a fourth clinical data set, and wherein calculating a composite measure between the plurality of waveform signal measures of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set comprises: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures; performing step 2 by calculating powers of different orders for each signal measure of the subset of signal measures to generate a power of the subset of signal measures; performing step 3 by multiplying together powers of the signal measures of the subset of signal measures to generate a product of the powers of the subset of signal measures; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the product to obtain a composite measure for the subset of signal measures; and repeating steps 1, 2, 3, and 4 until all of the composite measures have been calculated between all of the plurality of waveform signal measures.

[0125] A further embodiment of the hemodynamic monitor described above, wherein the input features comprise a first subset and a second subset, and the instructions, when executed by the system processor, are further configured to: extract the first subset and the second subset of input features simultaneously from the plurality of signal measurements; simultaneously determine the patient's normal aortic stenosis score from the first subset of input features and the patient's severe aortic stenosis score from the second subset of input features; and output the patient's normal aortic stenosis score and the patient's severe aortic stenosis score to a display of a user interface.

[0126] 10. A further embodiment of the hemodynamic monitor as described above, wherein the input features comprise a third subset and a fourth subset, and the instructions, when executed by a system processor, are further configured to: extract a first subset, a second subset, a third subset, and a fourth subset of the input features simultaneously from the plurality of signal measurements; simultaneously determine the patient's normal aortic stenosis score from the first subset of input features, the patient's severe aortic stenosis score from the second subset of input features, the patient's mild aortic stenosis score from the third subset of input features, and the patient's moderate aortic stenosis score from the fourth subset of input features; and output the patient's normal aortic stenosis score, the patient's mild aortic stenosis score, the patient's moderate aortic stenosis score, and the patient's severe aortic stenosis score to a display of a user interface.

[0127] In another example, a method for triaging a patient for aortic stenosis is disclosed. The method includes receiving, by a hemodynamic monitor, sensed hemodynamic data representing the patient's arterial pressure waveform. The hemodynamic monitor performs waveform analysis of the sensed hemodynamic data to calculate a plurality of signal measurements of the sensed hemodynamic data. The method further includes extracting, by the hemodynamic monitor, input features from the plurality of signal measurements indicative of the patient's aortic stenosis score. The extracting input features includes extracting a first subset of input features and extracting a second subset of input features simultaneously with the first subset of input features. The hemodynamic monitor simultaneously determines the patient's normal aortic stenosis score from the first subset of input features and the patient's severe aortic stenosis score from the second subset of input features. The hemodynamic monitor outputs the patient's normal aortic stenosis score and the patient's severe aortic stenosis score to a display and / or a mobile device.

[0128] The method of the preceding paragraph may optionally include, in addition and / or in the alternative, any one or more of the following features, configurations, and / or additional components listed below.

[0129] 10. A further embodiment of the above-described method, wherein the step of extracting input features further comprises extracting a third subset of input features simultaneously with the first subset and the second subset of input features, and extracting a fourth subset of input features simultaneously with the first, second, and third subset of input features; wherein the hemodynamic monitor simultaneously determines the patient's normal aortic stenosis score from the first subset of input features, the patient's severe aortic stenosis score from the second subset of input features, the patient's mild aortic stenosis score from the third subset of input features, and the patient's moderate aortic stenosis score from the fourth subset of input features; and wherein the hemodynamic monitor outputs the patient's normal aortic stenosis score, the patient's mild aortic stenosis score, the patient's moderate aortic stenosis score, and the patient's severe aortic stenosis score to a display and / or mobile device.

[0130] A further embodiment of the above method, further comprising the steps of informing the patient and / or healthcare professional that the aortic stenosis score is normal when the aortic stenosis score is within a first range; alerting the patient and / or healthcare professional that the aortic stenosis score is mild when the aortic stenosis score is within a second range; alerting the patient and / or healthcare professional that the aortic stenosis score is moderate when the aortic stenosis score is within a third range; and alerting the patient and / or healthcare professional that the aortic stenosis score is severe when the aortic stenosis score is within a fourth range.

[0131] The method further comprises the step of training a hemodynamic monitor to determine an aortic stenosis score for a patient, wherein training the hemodynamic monitor comprises the steps of: collecting a first clinical dataset including arterial pressure waveforms from a first group of individuals with normal aortic valve function; labeling each of the arterial pressure waveforms of the first clinical dataset with a first label; performing waveform analysis of the labeled arterial pressure waveforms of the first clinical dataset to calculate a plurality of waveform signal measures of the first clinical dataset; determining a first subset of input features by calculating a composite measure among the plurality of waveform signal measures of the first clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the first clinical dataset having a most predictive composite measure, and labeling the top signal measures of the first clinical dataset as a first subset of input features; and collecting a second clinical dataset including arterial pressure waveforms from a second group of individuals with severe aortic stenosis, wherein the severe aortic stenosis is greater than 1 cm. 2 aortic valve area smaller than the first subset of input features; labeling each of the arterial pressure waveforms of the second clinical dataset with a second label; performing waveform analysis of the labeled arterial pressure waveforms of the second clinical dataset to calculate a plurality of waveform signal measures of the second clinical dataset; and determining a second subset of input features by calculating a composite measure among the plurality of waveform signal measures of the second clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the second clinical dataset having the most predictive composite measure, and labeling the top signal measures of the second clinical dataset as the second subset of input features.

[0132] The step of training the hemodynamic monitor to determine the patient's aortic stenosis score comprises collecting a third clinical data set including arterial pressure waveforms from a third group of individuals with mild aortic stenosis, wherein mild aortic stenosis is defined as 1.5 cm 2labeling each of the arterial pressure waveforms of the third clinical dataset with a third label; performing waveform analysis of the labeled arterial pressure waveforms of the third clinical dataset to calculate a plurality of waveform signal measures of the third clinical dataset; determining a third subset of input features by calculating a composite measure among the plurality of waveform signal measures of the third clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the third clinical dataset having the most predictive composite measure, and labeling the top signal measures of the third clinical dataset as a third subset of input features; collecting a fourth clinical dataset including arterial pressure waveforms from a fourth group of individuals with moderate aortic stenosis, wherein the moderate aortic stenosis is greater than 1 cm 2 and 1.5cm 2 a fourth subset of input features by calculating a composite measure between the plurality of waveform signal measures of the fourth clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the fourth clinical dataset that have the most predictive composite measure, and labeling the top signal measures of the fourth clinical dataset as the fourth subset of input features.

[0133] The step of performing waveform analysis of the labeled arterial pressure waveforms of the first clinical dataset to calculate a plurality of waveform signal measurements of the first clinical dataset comprises the steps of: identifying individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset; and extracting a plurality of waveform signal measurements of the first clinical dataset from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset; and the step of performing waveform analysis of the labeled arterial pressure waveforms of the second clinical dataset to calculate a plurality of waveform signal measurements of the second clinical dataset comprises the steps of: identifying individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each individual cardiac cycle in each of the arterial pressure waveforms of the clinical data set; and extracting a plurality of waveform signal measurements of the second clinical data set from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the second clinical data set, wherein performing waveform analysis of the labeled arterial pressure waveforms of the third clinical data set to calculate a plurality of waveform signal measurements of a third clinical data set comprises: identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical data set; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical data set; and extracting a plurality of waveform signal measurements of the third clinical data set from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical data set;A further embodiment of the above method, wherein performing waveform analysis of the labeled arterial pressure waveforms of the fourth clinical dataset to calculate a plurality of waveform signal measurements of the fourth clinical dataset comprises: identifying individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical dataset; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical dataset; identifying a systolic rise phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical dataset; and extracting a plurality of waveform signal measurements of the fourth clinical dataset from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical dataset.

[0134] the plurality of waveform signal measurements of the first clinical dataset correspond to hemodynamic effects from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the first clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle; the plurality of waveform signal measurements of the second clinical dataset correspond to hemodynamic effects from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the second clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle; A further embodiment of the above-described method, wherein the plurality of waveform signal measurements correspond to hemodynamic effects from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the third clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle; and / or wherein the plurality of waveform signal measurements of the fourth clinical dataset correspond to hemodynamic effects from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the fourth clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

[0135] The plurality of waveform signal measurements of the first clinical dataset comprise mean, maximum, minimum, time length, area, standard deviation, derivative, and / or morphological measurements from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the first clinical dataset; the plurality of waveform signal measurements of the second clinical dataset comprise mean, maximum, minimum, time length, area, standard deviation, derivative, and / or morphological measurements from each of a systolic rise phase, a systolic decay phase, and a diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the second clinical dataset; A further embodiment of the above-described method, wherein the plurality of waveform signal measurements comprises mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic rise phase, systolic decay phase, and diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the third clinical dataset; and / or wherein the plurality of waveform signal measurements of the fourth clinical dataset comprises mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic rise phase, systolic decay phase, and diastolic phase from each individual cardiac cycle in each of the arterial pressure waveforms of the fourth clinical dataset.

[0136] The plurality of waveform signal measurements of the first clinical dataset comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle in each of the arterial pressure waveforms of the first clinical dataset; the plurality of waveform signal measurements of the second clinical dataset comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle in each of the arterial pressure waveforms of the second clinical dataset; A further embodiment of the above-described method, wherein the plurality of waveform signal measurements comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle in each of the arterial pressure waveforms of the third clinical data set; and / or wherein the plurality of waveform signal measurements of a fourth clinical data set comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each individual cardiac cycle in each of the arterial pressure waveforms of the fourth clinical data set.

[0137] The step of calculating composite measures between the plurality of waveform signal measures of the first clinical data set includes: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the first clinical data set; performing step 2 by calculating powers of different orders for each signal measure of the subset of signal measures from the plurality of waveform signal measures of the first clinical data set to generate powers of the subset of signal measures from the plurality of waveform signal measures of the first clinical data set; and performing step 3 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the first clinical data set to generate products of the powers of the subset of signal measures from the plurality of waveform signal measures of the first clinical data set. a first clinical data set of a plurality of waveform signal measurements; a second clinical data set of a plurality of waveform signal measurements; a third clinical data set of a plurality of waveform signal measurements; a fourth clinical data set of a plurality of waveform signal measurements; a fourth clinical data set of a plurality of waveform signal measurements; a fourth clinical data set of a plurality of waveform signal measurements; a fifth clinical data set of a plurality of waveform signal measurements; a fifth clinical data set of a plurality of waveform signal measurements; a fifth clinical data set of a plurality of waveform signal measurements; a sixth ...

[0138] The step of calculating composite measures between the plurality of waveform signal measures of the second clinical data set includes: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the second clinical data set; performing step 2 by calculating powers of different orders for each of the signal measures of the subset of signal measures from the plurality of waveform signal measures of the second clinical data set to generate powers of the subset of signal measures from the plurality of waveform signal measures of the second clinical data set; and performing step 3 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the second clinical data set to generate products of the powers of the subset of signal measures from the plurality of waveform signal measures of the second clinical data set. and performing step 4 by performing a receiver operating characteristic (ROC) analysis of the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set to obtain a composite measurement for the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set, and repeating steps 1, 2, 3, and 4 until all of the composite measurements have been calculated between all of the plurality of waveform signal measurements of the second clinical data set.

[0139] The step of calculating a composite measure among the plurality of waveform signal measures of the third clinical dataset includes: performing step 1 by arbitrarily selecting a subset of signal measures from the plurality of waveform signal measures of the third clinical dataset; performing step 2 by calculating powers of different orders for each signal measure of the subset of signal measures from the plurality of waveform signal measures of the third clinical dataset to generate powers of the subset of signal measures from the plurality of waveform signal measures of the third clinical dataset; and performing step 3 by multiplying together the powers of the subset of signal measures from the plurality of waveform signal measures of the third clinical dataset to generate products of the powers of the subset of signal measures from the plurality of waveform signal measures of the third clinical dataset; and calculating a receiver operating characteristic (R) of the product of the powers of the subset of signal measures from the plurality of waveform signal measures of the third clinical dataset to obtain a composite measure for the product of the powers of the subset of signal measures from the plurality of waveform signal measures of the third clinical dataset. and repeating steps 1, 2, 3, and 4 until all of the composite measurements have been calculated between all of the plurality of waveform signal measurements of the third clinical dataset, wherein calculating the composite measurements between the plurality of waveform signal measurements of the fourth clinical dataset comprises: performing step 1 by arbitrarily selecting a subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical dataset; performing step 2 by calculating powers of different orders for each signal measurement of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical dataset to generate a power of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical dataset; and performing step 3 by multiplying together the powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical dataset to generate a product of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical dataset.A further embodiment of the above method, comprising: performing step 4 by performing a receiver operating characteristic (ROC) analysis of products of powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set to obtain a composite measurement for a product of powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set; and repeating steps 1, 2, 3, and 4 until all of the composite measurements have been calculated between all of the plurality of waveform signal measurements of the fourth clinical data set.

[0140] While the invention has been described in terms of exemplary embodiments, it will be understood that various changes may be made and equivalents may be substituted for those elements without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from essential scope thereof. Therefore, it is not intended that the invention be limited to the particular embodiments disclosed, but rather that the invention will include all embodiments falling within the scope of the appended claims. [Explanation of symbols]

[0141] 110 Health Monitoring System 112 Display 114 I / O connectors 116 Hemodynamic Sensor 118 Housing 120 fluid input port 122 Fluid port on catheter side 124 I / O cable 126 Hemodynamic Sensor 128 Inflatable Finger Cuff 130 Cardiac Reference Sensor 134 Hemodynamic Sensor 136 patients 138 Healthcare workers 140 system processors 142 system memory 144 ADC 146 DAC 148 Aortic Stenosis Software Code 150 First Module 151 Second Module 152 Third Module 154 User Interface 156 Control Elements 158 Perceptual Information 161 First Clinical Data Set 162 Second Clinical Data Set 163 Third Clinical Data Set 164 Fourth Clinical Data Set

Claims

1. 1. A hemodynamic monitor for detecting aortic valve stenosis, comprising: a non-invasive blood pressure sensor comprising an inflatable blood pressure bladder, a pressure controller pneumatically connected to the inflatable blood pressure bladder, and an optical transmitter and an optical receiver electrically connected to the pressure controller; An integrated hardware unit comprising: a system processor; System memory; a display with a user interface; an integrated hardware unit comprising: Equipped with The system memory comprises instructions that, when executed by the system processor, the pressure controller adjusting the pressure in the inflatable blood pressure bladder to maintain a constant arterial volume of the patient for a period of time based on a feedback signal generated by the optical transmitter and the optical receiver; generating arterial pressure waveform data for the patient based on the adjusted pressure in the inflatable blood pressure bladder over the period of time; extracting a plurality of signal measurements from the arterial pressure waveform data of the patient; extracting input features from the plurality of signal measurements indicative of an aortic stenosis score for the patient; determining the aortic stenosis score for the patient based on the extracted input features; and generating a first sensory alert signal configured to generate a first sensory alert indicating that the patient has severe aortic stenosis when the aortic stenosis score exceeds a threshold score, or generating a second sensory alert signal configured to generate a second sensory alert indicating that the patient does not have severe aortic stenosis when the aortic stenosis score is below the threshold score; transmitting the first sensory alert signal or the second sensory alert signal to the user interface; outputting the first sensory alert or the second sensory alert through the user interface; a hemodynamic monitor configured to:

2. the input features are determined by machine training, the machine training comprising: collecting a first clinical data set comprising arterial pressure waveforms from a first group of individuals having normal aortic valve function; collecting a second clinical data set comprising arterial pressure waveforms from a second group of individuals with severe aortic stenosis, the second group comprising individuals with severe aortic stenosis between 1 cm and 3 cm; 2 collecting, defined as a smaller aortic valve area; collecting a third clinical data set comprising arterial pressure waveforms from a third group of individuals with mild aortic stenosis, wherein the mild aortic stenosis is between 1.5 cm and 2.5 cm; 2 collecting, defined as a larger aortic valve area; collecting a fourth clinical data set including arterial pressure waveforms from a fourth group of individuals with moderate aortic stenosis, wherein the moderate aortic stenosis is between 1 cm and 2 cm; 2 and 1.5cm 2 The aortic valve area between the collecting and performing a waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate a plurality of waveform signal measurements; determining the input features by calculating a composite measure between the plurality of waveform signal measures, selecting a top signal measure from the plurality of waveform signal measures having a most predictive composite measure, and labeling the top signal measure as the input feature; 10. The hemodynamic monitor of claim 1, comprising:

3. performing a waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate the plurality of waveform signal measurements; identifying individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set; identifying a dicrotic notch in each of the individual cardiac cycles; identifying a systolic upstroke phase, a systolic downstroke phase, and a diastolic phase in each of said individual cardiac cycles; extracting the plurality of waveform signal measurements from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles; 3. The hemodynamic monitor of claim 2, comprising:

4. 4. The hemodynamic monitor of claim 3, wherein the plurality of waveform signal measurements correspond to hemodynamic effects from each of the systolic upstroke phase, the systolic downstroke phase, and the diastolic downstroke phase from each of the individual cardiac cycles, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

5. the plurality of waveform signal measurements the mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic upstroke, systolic decay, and diastolic phases from each of the individual cardiac cycles; and / or heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation, stroke volume variation, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each of the individual cardiac cycles.

5. The hemodynamic monitor of claim 4, comprising:

6. calculating the composite measure between the plurality of waveform signal measures of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set; performing step 1 by arbitrarily selecting a subset of signal measurements from said plurality of waveform signal measurements; performing step 2 by calculating powers of different orders for each signal measurement of said subset of signal measurements to generate a power of said subset of signal measurements; performing step 3 by multiplying together the powers of the signal measurements of the subset of signal measurements to generate a product of the powers of the subset of signal measurements; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the product to obtain a composite measurement for the subset of signal measurements; repeating steps 1, 2, 3, and 4 until all of said composite measurements are calculated between all of said plurality of waveform signal measurements; 6. The hemodynamic monitor of claim 5, comprising:

7. the input features comprise a first subset and a second subset, and the instructions, when executed by the system processor, extracting the first and second subsets of input features simultaneously from the plurality of signal measurements; simultaneously determining a normal aortic stenosis score for the patient from the first subset of input features and a severe aortic stenosis score for the patient from the second subset of input features; outputting the normal aortic stenosis score and the severe aortic stenosis score for the patient to the display of the user interface; 7. The hemodynamic monitor of claim 6, further configured to:

8. the input features comprise a third subset and a fourth subset, and the instructions, when executed by the system processor, extracting the first subset, the second subset, the third subset, and the fourth subset of the input features simultaneously from the plurality of signal measurements; simultaneously determining the patient's normal aortic stenosis score from the first subset of input features, the patient's severe aortic stenosis score from the second subset of input features, the patient's mild aortic stenosis score from the third subset of input features, and the patient's moderate aortic stenosis score from the fourth subset of input features; outputting the normal aortic stenosis score of the patient, the mild aortic stenosis score of the patient, the moderate aortic stenosis score of the patient, and the severe aortic stenosis score of the patient on the display of the user interface; 8. The hemodynamic monitor of claim 7, further configured to:

9. 1. A hemodynamic monitor for detecting aortic valve stenosis, comprising: an arterial blood pressure sensor comprising a housing, a fluid input port connected to a fluid source via tubing, a catheter fluid port connected to a catheter inserted into the patient's arterial system, a pressure transducer in communication with the fluid source through the fluid port, and an I / O cable in electrical communication with the pressure transducer; An integrated hardware unit comprising: a system processor; System memory; a display having a user interface; Analog-to-digital converter (ADC) and an integrated hardware unit comprising: Equipped with The system memory comprises instructions that, when executed by the system processor, receiving, over a period of time, from the pressure transducer, an electrical signal based on pressure in the arterial system of the patient transmitted through the fluid source; converting the electrical signal into a digital signal; generating arterial pressure waveform data for the patient based on the digital signal; extracting a plurality of signal measurements from the arterial pressure waveform data; extracting input features from the plurality of signal measurements indicative of an aortic stenosis score for the patient; determining the aortic stenosis score for the patient based on the extracted input features; and generating a first sensory alert signal configured to generate a first sensory alert indicating that the patient has severe aortic stenosis when the aortic stenosis score exceeds a threshold score, or generating a second sensory alert signal configured to generate a second sensory alert indicating that the patient does not have severe aortic stenosis when the aortic stenosis score is below the threshold score; transmitting the first sensory alert signal or the second sensory alert signal to the user interface; outputting the first sensory alert or the second sensory alert through the user interface; a hemodynamic monitor configured to:

10. the input features are determined by machine training, the machine training comprising: collecting a first clinical data set comprising arterial pressure waveforms from a first group of individuals having normal aortic valve function; collecting a second clinical data set comprising arterial pressure waveforms from a second group of individuals with severe aortic stenosis, the second group comprising individuals with severe aortic stenosis, the second group comprising individuals with severe aortic stenosis between 1 cm and 3 cm; 2 collecting, defined as a smaller aortic valve area; collecting a third clinical data set comprising arterial pressure waveforms from a third group of individuals with mild aortic stenosis, wherein the mild aortic stenosis is between 1.5 cm and 2.5 cm; 2 collecting, defined as a larger aortic valve area; collecting a fourth clinical data set including arterial pressure waveforms from a fourth group of individuals with moderate aortic stenosis, wherein the moderate aortic stenosis is between 1 cm and 2 cm; 2 and 1.5cm 2 The aortic valve area between the collecting and performing a waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate a plurality of waveform signal measurements; determining the input features by calculating a composite measure between the plurality of waveform signal measures, selecting a top signal measure from the plurality of waveform signal measures having a most predictive composite measure, and labeling the top signal measure as the input feature; 10. The hemodynamic monitor of claim 9, comprising:

11. performing a waveform analysis of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set to calculate the plurality of waveform signal measurements; identifying individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set; identifying a dicrotic notch in each of the individual cardiac cycles; identifying a systolic upstroke phase, a systolic downstroke phase, and a diastolic phase in each of said individual cardiac cycles; extracting the plurality of waveform signal measurements from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles; 11. The hemodynamic monitor of claim 10, comprising:

12. 12. The hemodynamic monitor of claim 11, wherein the plurality of waveform signal measurements correspond to hemodynamic effects from each of the systolic upstroke phase, the systolic downstroke phase, and the diastolic downstroke phase from each of the individual cardiac cycles, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and the entire cardiac cycle.

13. the plurality of waveform signal measurements the mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic upstroke, systolic decay, and diastolic phases from each of the individual cardiac cycles; and / or heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation, stroke volume variation, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each of the individual cardiac cycles.

13. The hemodynamic monitor of claim 12, comprising:

14. calculating the composite measure between the plurality of waveform signal measures of the first clinical data set, the second clinical data set, the third clinical data set, and the fourth clinical data set; performing step 1 by arbitrarily selecting a subset of signal measurements from said plurality of waveform signal measurements; performing step 2 by calculating powers of different orders for each signal measurement of said subset of signal measurements to generate a power of said subset of signal measurements; performing step 3 by multiplying together the powers of the signal measurements of the subset of signal measurements to generate a product of the powers of the subset of signal measurements; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the product to obtain a composite measurement for the subset of signal measurements; repeating steps 1, 2, 3, and 4 until all of said composite measurements are calculated between all of said plurality of waveform signal measurements; 14. The hemodynamic monitor of claim 13, comprising:

15. the input features comprise a first subset and a second subset, and the instructions, when executed by the system processor, extracting the first and second subsets of input features simultaneously from the plurality of signal measurements; simultaneously determining a normal aortic stenosis score for the patient from the first subset of input features and a severe aortic stenosis score for the patient from the second subset of input features; determining the aortic stenosis score for the patient based on the normal aortic stenosis score for the patient and the severe aortic stenosis score for the patient; outputting the aortic stenosis score for the patient to the display of the user interface; 15. The hemodynamic monitor of claim 14, further configured to:

16. the input features comprise a third subset and a fourth subset, and the instructions, when executed by the system processor, extracting the first subset, the second subset, the third subset, and the fourth subset of the input features simultaneously from the plurality of signal measurements; simultaneously determining the patient's normal aortic stenosis score from the first subset of input features, the patient's severe aortic stenosis score from the second subset of input features, the patient's mild aortic stenosis score from the third subset of input features, and the patient's moderate aortic stenosis score from the fourth subset of input features; outputting the normal aortic stenosis score of the patient, the mild aortic stenosis score of the patient, the moderate aortic stenosis score of the patient, and the severe aortic stenosis score of the patient on the display of the user interface; 16. The hemodynamic monitor of claim 15, further configured to:

17. 1. A method for triaging a patient for aortic stenosis, comprising: receiving, by a hemodynamic monitor, sensed hemodynamic data representative of an arterial pressure waveform of the patient; the hemodynamic monitor performing waveform analysis of the sensed hemodynamic data to calculate a plurality of signal measurements of the sensed hemodynamic data; the hemodynamic monitor extracting input features from the plurality of signal measurements indicative of an aortic stenosis score for the patient, The step of extracting the input feature amount includes: extracting a first subset of the input features; extracting a second subset of the input features simultaneously with the first subset of the input features; and the hemodynamic monitor simultaneously determining a normal aortic stenosis score for the patient from the first subset of input features and a severe aortic stenosis score for the patient from the second subset of input features; outputting the normal aortic stenosis score and the severe aortic stenosis score for the patient to a display and / or mobile device; A method comprising:

18. The step of extracting the input feature amount includes: extracting a third subset of the input features simultaneously with the first and second subsets of the input features; extracting a fourth subset of the input features simultaneously with the first subset, the second subset, and the third subset of the input features; Furthermore, the hemodynamic monitor simultaneously determines the patient's normal aortic stenosis score from the first subset of input features, the patient's severe aortic stenosis score from the second subset of input features, the patient's mild aortic stenosis score from the third subset of input features, and the patient's moderate aortic stenosis score from the fourth subset of input features; 18. The method of claim 17, wherein the hemodynamic monitor outputs the patient's normal aortic stenosis score, the patient's mild aortic stenosis score, the patient's moderate aortic stenosis score, and the patient's severe aortic stenosis score to a display and / or mobile device.

19. when the aortic stenosis score is within a first range, informing the patient and / or a healthcare professional that the aortic stenosis score is normal; When the aortic stenosis score is within a second range, alerting the patient and / or the healthcare professional that the aortic stenosis score is mild; when the aortic stenosis score is within a third range, alerting the patient and / or the healthcare professional that the aortic stenosis score is moderate; when the aortic stenosis score is within a fourth range, alerting the patient and / or the healthcare professional that the aortic stenosis score is severe; 20. The method of claim 18, further comprising:

20. training the hemodynamic monitor to determine the aortic stenosis score for the patient, the training of the hemodynamic monitor comprising: collecting a first clinical data set comprising arterial pressure waveforms from a first group of individuals with normal aortic valve function; labeling each of the arterial pressure waveforms of the first clinical data set with a first label; performing waveform analysis of the labeled arterial pressure waveform of the first clinical data set to calculate a plurality of waveform signal measurements of the first clinical data set; determining the first subset of input features by calculating composite measures between the plurality of waveform signal measures of the first clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the first clinical dataset having the most predictive composite measures, and labeling the top signal measures of the first clinical dataset as the first subset of input features; collecting a second clinical data set comprising arterial pressure waveforms from a second group of individuals with severe aortic stenosis, wherein the severe aortic stenosis is greater than 1 cm; 2 a step, defined as a smaller aortic valve area; labeling each of the arterial pressure waveforms of the second clinical data set with a second label; performing waveform analysis of the labeled arterial pressure waveform of the second clinical data set to calculate a plurality of waveform signal measurements of the second clinical data set; determining the second subset of input features by calculating composite measures between the plurality of waveform signal measures of the second clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the second clinical dataset having the most predictive composite measures, and labeling the top signal measures of the second clinical dataset as the second subset of input features; 20. The method of claim 19, comprising:

21. training the hemodynamic monitor to determine the aortic stenosis score for the patient, collecting a third clinical data set comprising arterial pressure waveforms from a third group of individuals with mild aortic stenosis, wherein mild aortic stenosis is between 1.5 cm and 2.5 cm; 2 Step, defined as the larger aortic valve area; labeling each of the arterial pressure waveforms of the third clinical data set with a third label; performing waveform analysis of the labeled arterial pressure waveform of the third clinical data set to calculate a plurality of waveform signal measurements of the third clinical data set; determining the third subset of input features by calculating composite measures between the plurality of waveform signal measures of the third clinical dataset, selecting top signal measures from the plurality of waveform signal measures of the third clinical dataset having the most predictive composite measures, and labeling the top signal measures of the third clinical dataset as the third subset of input features; collecting a fourth clinical data set comprising arterial pressure waveforms from a fourth group of individuals with moderate aortic stenosis, wherein moderate aortic stenosis is between 1 cm and 2 cm; 2 and 1.5cm 2 The aortic valve area between the step labeling each of the arterial pressure waveforms of the fourth clinical data set with a fourth label; performing waveform analysis of the labeled arterial pressure waveform of the fourth clinical data set to calculate a plurality of waveform signal measurements of the fourth clinical data set; determining the fourth subset of input features by calculating composite measures between the plurality of waveform signal measures of the fourth clinical data set, selecting top signal measures from the plurality of waveform signal measures of the fourth clinical data set having the most predictive composite measures, and labeling the top signal measures of the fourth clinical data set as the fourth subset of input features; 21. The method of claim 20, further comprising:

22. performing a waveform analysis of the labeled arterial pressure waveform of the first clinical data set to calculate the plurality of waveform signal measurements of the first clinical data set, identifying individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set; identifying a systolic upstroke phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set; extracting the plurality of waveform signal measurements of the first clinical data set from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set; Equipped with performing a waveform analysis of the labeled arterial pressure waveform of the second clinical data set to calculate the plurality of waveform signal measurements of the second clinical data set, identifying individual cardiac cycles in each of the arterial pressure waveforms of the second clinical data set; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical data set; identifying a systolic upstroke phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical data set; extracting the plurality of waveform signal measurements of the second clinical data set from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical data set; Equipped with performing a waveform analysis of the labeled arterial pressure waveform of the third clinical data set to calculate the plurality of waveform signal measurements of the third clinical data set, identifying individual cardiac cycles in each of the arterial pressure waveforms of the third clinical data set; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical data set; identifying a systolic upstroke phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical data set; extracting the plurality of waveform signal measurements of the third clinical data set from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical data set; Equipped with performing a waveform analysis of the labeled arterial pressure waveform of the fourth clinical data set to calculate the plurality of waveform signal measurements of the fourth clinical data set, identifying individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical data set; identifying a dicrotic notch in each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical data set; identifying a systolic upstroke phase, a systolic decay phase, and a diastolic phase in each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical data set; extracting the plurality of waveform signal measurements of the fourth clinical data set from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical data set; 22. The method of claim 21, comprising:

23. the plurality of waveform signal measurements of the first clinical dataset correspond to hemodynamic effects from each of the systolic upstroke phase, the systolic downstroke phase, and the diastolic downstroke phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and an entire cardiac cycle; the plurality of waveform signal measurements of the second clinical dataset correspond to hemodynamic effects from each of the systolic upstroke phase, the systolic downstroke phase, and the diastolic downstroke phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and an entire cardiac cycle; the plurality of waveform signal measurements of the third clinical dataset correspond to hemodynamic effects from each of the systolic upstroke phase, the systolic downstroke phase, and the diastolic downstroke phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical dataset, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and an entire cardiac cycle; and / or 23. The method of claim 22, wherein the plurality of waveform signal measurements of the fourth clinical data set correspond to hemodynamic effects from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical data set, the hemodynamic effects comprising contractility, aortic elasticity, stroke volume, vascular tone, afterload, and an entire cardiac cycle.

24. the plurality of waveform signal measurements of the first clinical data set comprise a mean, a maximum, a minimum, a duration, an area, a standard deviation, a derivative, and / or a morphological measurement from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set; the plurality of waveform signal measurements of the second clinical dataset comprising a mean, a maximum, a minimum, a duration, an area, a standard deviation, a derivative, and / or a morphological measurement from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset; the plurality of waveform signal measurements of the third clinical dataset comprise a mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurement from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical dataset; and / or 24. The method of claim 23, wherein the plurality of waveform signal measurements of the fourth clinical data set comprise mean, maximum, minimum, duration, area, standard deviation, derivative, and / or morphological measurements from each of the systolic upstroke phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical data set.

25. the plurality of waveform signal measurements of the first clinical data set comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation, stroke volume variation, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each of the individual cardiac cycles in each of the arterial pressure waveforms of the first clinical data set; the plurality of waveform signal measurements of the second clinical dataset comprising heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation, stroke volume variation, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each of the individual cardiac cycles in each of the arterial pressure waveforms of the second clinical dataset; the plurality of waveform signal measurements of the third clinical data set comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variability, stroke volume variability, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each of the individual cardiac cycles in each of the arterial pressure waveforms of the third clinical data set; and / or 25. The method of claim 24, wherein the plurality of waveform signal measurements of the fourth clinical data set comprise heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation, stroke volume variation, mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular elasticity, and / or left ventricular contractility extracted from each of the individual cardiac cycles in each of the arterial pressure waveforms of the fourth clinical data set.

26. calculating the composite measure between the plurality of waveform signal measures of the first clinical data set, performing step 1 by arbitrarily selecting a subset of signal measurements from the plurality of waveform signal measurements of the first clinical data set; performing step 2 by calculating powers of different orders for each signal measurement of the subset of signal measurements from the plurality of waveform signal measurements of the first clinical data set to generate powers of the subset of signal measurements from the plurality of waveform signal measurements of the first clinical data set; performing step 3 by multiplying together the powers of the subset of signal measurements from the plurality of waveform signal measurements of the first clinical data set to generate a product of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the first clinical data set; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the first clinical data set to obtain a composite measurement for the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the first clinical data set; repeating steps 1, 2, 3, and 4 until all of said composite measurements are calculated between all of said plurality of waveform signal measurements of said first clinical data set; 26. The method of claim 25, comprising:

27. calculating the composite measure between the plurality of waveform signal measures of the second clinical data set, performing step 1 by arbitrarily selecting a subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set; performing step 2 by calculating powers of different orders for each of the signal measurements of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set to generate powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set; performing step 3 by multiplying together the powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set to generate a product of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set to obtain a composite measurement for the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the second clinical data set; repeating steps 1, 2, 3, and 4 until all of said composite measurements are calculated between all of said plurality of waveform signal measurements of said second clinical data set; 27. The method of claim 26, comprising:

28. calculating the composite measure between the plurality of waveform signal measures of the third clinical data set, performing step 1 by arbitrarily selecting a subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set; performing step 2 by calculating powers of different orders for each of the signal measurements of the subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set to generate powers of the subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set; performing step 3 by multiplying together the powers of the subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set to generate a product of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set to obtain a composite measurement for the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the third clinical data set; repeating steps 1, 2, 3, and 4 until all of said composite measurements are calculated between all of said plurality of waveform signal measurements of said third clinical data set; Equipped with calculating the composite measure between the plurality of waveform signal measures of the fourth clinical data set, performing step 1 by arbitrarily selecting a subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set; performing step 2 by calculating powers of different orders for each of the signal measurements of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set to generate powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set; performing step 3 by multiplying together the powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set to generate a product of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set; performing step 4 by performing a receiver operating characteristic (ROC) analysis of the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set to obtain a composite measurement for the products of the powers of the subset of signal measurements from the plurality of waveform signal measurements of the fourth clinical data set; repeating steps 1, 2, 3, and 4 until all of said composite measurements are calculated between all of said plurality of waveform signal measurements of said fourth clinical data set; 28. The method of claim 27, comprising:

Images