The present disclosure relates generally to hemodynamic monitoring, and in particular, to detecting nociception in a patient using monitored hemodynamic data.
Nociception is the process in which nerve endings called nociceptors detect noxious stimuli and send a signal to the central nervous system, which is interpreted as pain. Noxious insult initiates a “sharp” signal from the source of the insult. Locally, nociception initiates an inflammation response. The signal then travels through neurons to the spinal column where a muscle reflex is triggered. The signal continues to the brain, where, upon reaching the lower brain, the nociception signal triggers a sympathetic nervous system response. Nociception can cause a sympathetic nervous system response without reaching consciousness or before reaching consciousness; thus, an unconscious patient in surgery or in intensive care can experience pain. To prevent a patient from awaking out of surgery or intensive care in pain, medical workers administer analgesics to the patient before and/or during surgery and at various times in the intensive care. However, knowing the amount of analgesic to administer can be difficult as pain thresholds and tolerances vary from patient to patient, and the patient is unable to verbally communicate or signal feedback while unconscious. Administering too little analgesic to the patient during surgery can result in the patient awaking in pain after the surgery. Administering too much analgesic to the patient during surgery can result in the patient experiencing nausea, drowsiness, impaired thinking skills, and impaired function.
In view of the negative consequences of administering too little analgesic to the patient and the negative consequences of administering too much analgesic to the patient, a solution is needed that will allow medical workers the ability to detect or predict nociception of an unconscious patient during surgery. Accurately detecting nociception of a patient during surgery can help medical workers know the appropriate amount of analgesic to administer to the patient so that the patient does not awake from surgery with significant pain, without providing too much analgesic to the patient.
In one example, a method is disclosed for monitoring arterial pressure of a patient and identifying nociception of the patient. The method includes receiving, by a hemodynamic monitor, sensed hemodynamic data representative of an arterial pressure waveform of the patient. The hemodynamic monitor performs waveform analysis of the sensed hemodynamic data to calculate a plurality of signal measures of the sensed hemodynamic data. Input features are extracted by the hemodynamic monitor from the plurality of signal measures that are indicative of a nociception event of the patient and a stable episode of the patient. A change detection algorithm of the hemodynamic monitor monitors at least two signal measures of the plurality of signal measures for a simultaneous change in the at least two signal measures. The hemodynamic monitor determines a nociception score representing a probability of the nociception event of the patient based on the input features. The hemodynamic monitor adjusts or maintains the nociception score based on an output of the change detection algorithm. An adjusted nociception score is displayed by the hemodynamic monitor.
In another example, a system is disclosed for monitoring arterial pressure of a patient and providing a warning to medical personnel of nociception of the patient. The system includes a hemodynamic sensor that produces hemodynamic data representative of an arterial pressure waveform of the patient. The system further includes system memory that stores nociception detection software code. A user interface includes a sensory alarm that provides a sensory signal to warn the medical personnel of a nociception event of the patient. A hardware processor is configured to execute the nociception detection software code to perform waveform analysis of the hemodynamic data to determine a plurality of signal measures. The hardware processor is further configured to execute the nociception detection software code to extract detection input features from the plurality of signal measures that are indicative of the nociception event of the patient. The hardware processor is further configured to execute the nociception detection software code to monitor at least two signal measures from the plurality of signal measures for a simultaneous change in the at least two signal measures. The hardware processor also is configured to execute the nociception detection software code to determine, based on the detection input features and a presence of the simultaneous change in the at least two signal measures, a nociception score representing a probability of the nociception event of the patient. The sensory alarm of the user interface is invoked in response to the nociception score satisfying a predetermined detection criterion.
In another example, a method is disclosed for monitoring arterial pressure of a patient and identifying nociception of the patient. The method includes receiving, by a hemodynamic monitor, sensed hemodynamic data representative of an arterial pressure waveform of the patient. Waveform analysis is performed by the hemodynamic monitor of the sensed hemodynamic data to calculate a first signal measure and a second signal measure of the sensed hemodynamic data. Both the first signal measure and the second signal measure are processed by the hemodynamic monitor through a cumulative sum (CUSUM) algorithm to acquire a first CUSUM output for the first signal measure and a second CUSUM output for the second signal measure. The first CUSUM output and the second CUSUM output are monitored by the hemodynamic monitor for a change in both the first CUSUM output and the second CUSUM output. A nociception event of the patient is detected when the change in the first CUSUM output overlaps in time with the change in the second CUSUM output. A sensory signal is outputted to a user interface of the hemodynamic monitor to warn medical personnel of the nociception event of the patient.
In another example, a system is disclosed for monitoring arterial pressure of a patient and providing a warning to medical personnel of nociception of the patient. The system includes a hemodynamic sensor that produces hemodynamic data representative of an arterial pressure waveform of the patient. Nociception detection software code is stored in a system memory. The system further includes a user interface that includes a sensory alarm that provides a sensory signal to warn the medical personnel of a nociception event of the patient. The system also includes a hardware processor that is configured to execute the nociception detection software code to perform waveform analysis of the hemodynamic data to determine a first signal measure and a second signal measure. Both the first signal measure and the second signal measure are processed through a cumulative sum (CUSUM) algorithm to acquire a first CUSUM output for the first signal measure and a second CUSUM output for the second signal measure. The first CUSUM output and the second CUSUM output are monitored over time for a change in both the first CUSUM output and the second CUSUM output. A nociception event of the patient is detected when the change in the first CUSUM output overlaps in time with the change in the second CUSUM output. A sensory signal is outputted to a user interface of the hemodynamic monitor to warn medical personnel of the nociception event of the patient.
As described herein, a hemodynamic monitoring system implements a predictive model that calculates a risk score representing a probability of a current nociception event for a patient. The model's risk score can be refined using a change detection algorithm to analyze selected hemodynamic parameters most closely correlated with nociception events.
The machine learning of the predictive models of the hemodynamic monitoring system are trained using a clinical dataset containing arterial pressure waveforms labeled with clinical annotations of administration of analgesics, vasopressors, inotropes, fluids, and other medication that alter cardiovascular hemodynamics. The hemodynamic monitoring system is described in detail below with reference to
As further described below, hemodynamic monitor 10 includes one or more processors and computer-readable memory that stores nociception detection and prediction software code which is executable to produce a score representing a probability of a present (i.e., current) nociception event for a patient. Hemodynamic monitor 10 can receive sensed hemodynamic data representative of an arterial pressure waveform of the patient, such as via one or more hemodynamic sensors connected to hemodynamic monitor 10 via I/O connectors 14. Hemodynamic monitor 10 executes the nociception prediction software code to obtain, using the received hemodynamic data, multiple nociception profiling parameters (e.g., input features), which can include one or more vital sign parameters characterizing vital sign data of the patient, as well as differential and combinatorial parameters derived from the one or more vital sign parameters, as is further described below.
As illustrated in
As illustrated in
In operation, a column of fluid (e.g., saline solution) is introduced from a fluid source (e.g., a saline bag) through hemodynamic sensor 16 via fluid input port 20 to catheter-side fluid port 22 toward the catheter inserted into the patient. Arterial pressure is communicated through the fluid column to pressure sensors located within housing 16 which sense the pressure of the fluid column. Hemodynamic sensor 16 translates the sensed pressure of the fluid column to an electrical signal via the pressure transducers and outputs the corresponding electrical signal to hemodynamic monitor 10 (
In operation, the pressure controller continually adjusts pressure within the finger cuff to maintain a constant volume of the arteries in the finger (i.e., the unloaded volume of the arteries) as measured via the optical transmitter and optical receiver of inflatable finger cuff 28. The pressure applied by the pressure controller to continuously maintain the unloaded volume is representative of the blood pressure in the finger, and is communicated by the pressure controller to hemodynamic monitor 10 shown in
As illustrated in
Hemodynamic sensor 34 can be attached to patient 36 to sense hemodynamic data representative of the arterial pressure waveform of patient 36. Hemodynamic sensor 34 is operatively connected to hemodynamic monitor 10 (e.g., electrically and/or communicatively connected via wired or wireless connection, or both) to provide the sensed hemodynamic data to hemodynamic monitor 10. In some examples, hemodynamic sensor 34 provides the hemodynamic data representative of the arterial pressure waveform of patient 36 to hemodynamic monitor 10 as an analog signal, which is converted by ADC 44 to digital hemodynamic data representative of the arterial pressure waveform. In other examples, hemodynamic sensor 34 can provide the sensed hemodynamic data to hemodynamic monitor 10 in digital form, in which case hemodynamic monitor 10 may not include or utilize ADC 44. In yet other examples, hemodynamic sensor 34 can provide the hemodynamic data representative of the arterial pressure waveform of patient 36 to hemodynamic monitor 10 as an analog signal, which is analyzed in its analog form by hemodynamic monitor 10.
Hemodynamic sensor 34 can be a non-invasive or minimally invasive sensor attached to patient 36. For instance, hemodynamic sensor 34 can take the form of minimally invasive hemodynamic sensor 16 (
In certain examples, hemodynamic sensor 34 can be configured to sense an arterial pressure of patient 36 in a minimally invasive manner. For instance, hemodynamic sensor 34 can be attached to patient 36 via a radial arterial catheter inserted into an arm of patient 36. In other examples, hemodynamic sensor 34 can be attached to patient 36 via a femoral arterial catheter inserted into a leg of patient 36. Such minimally invasive techniques can similarly enable hemodynamic sensor 34 to provide substantially continuous beat-to-beat monitoring of the arterial pressure of patient 36 over an extended period of time, such as minutes or hours.
System processor 40 is a hardware processor configured to execute nociception software code 48, which implements first module 50, second module 51, and third module 52 to produce a nociception score representing a probability of a current nociception event for patient 36. Examples of system processor 40 can 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.
System memory 42 can be configured to store information within hemodynamic monitor 10 during operation. System memory 42, in some examples, is described as computer-readable storage media. In some examples, a computer-readable storage medium can include a non-transitory medium. The term “non-transitory” can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). System memory 42 can include volatile and non-volatile computer-readable memories. Examples of volatile memories can include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories. Examples of non-volatile memories can include, e.g., magnetic hard discs, optical discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.
Display 12 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 users in graphical form. User interface 54 can include graphical and/or physical control elements that enable user input to interact with hemodynamic monitor 10 and/or other components of hemodynamic monitoring system 32. In some examples, user interface 54 can take the form of a graphical user interface (GUI) that presents graphical control elements presented at, e.g., a touch-sensitive and/or presence sensitive display screen of display 12. In such examples, user input can be received in the form of gesture input, such as touch gestures, scroll gestures, zoom gestures, or other gesture input. In certain examples, user interface 54 can take the form of and/or include physical control elements, such as a physical buttons, keys, knobs, or other physical control elements configured to receive user input to interact with components of hemodynamic monitoring system 32.
In operation, hemodynamic sensor 34 senses hemodynamic data representative of an arterial pressure waveform of patient 36. Hemodynamic sensor 34 provides the hemodynamic data (e.g., as analog sensor data), to hemodynamic monitor 10. ADC 44 converts the analog hemodynamic data to digital hemodynamic data representative of the arterial pressure waveform of the patient.
Hemodynamic monitoring system 32 can discern when patient 36 is experiencing a current nociception event and when patient 36 is merely reacting to a hemodynamic drug previously administered to patient 36 by medical worker 38 (hereinafter referred to as a hemodynamic drug administration event). The hemodynamic drug administration event is defined as an event where patient 36 experiences an increase in heart rate and an increase in blood pressure due to the administration of a compound that alters cardiovascular hemodynamics (e.g., analgesics, vasopressors, inotropes, fluids, and/or other medication) and is not a nociception event of patient 36. Nociception software code 48 includes hemodynamic drug detection software code for detecting the presence of hemodynamic drug administration event of patient 36. System processor 40 executes the hemodynamic drug detection software code of nociception software code 48 to determine, using the received hemodynamic data, a hemodynamic drug detection score representing a probability that the hemodynamic drug administration event is responsible for increasing a heart rate and a blood pressure of patient 36. For instance, system processor 40 can execute first module 50 to perform waveform analysis of the hemodynamic data to determine the plurality of signal measures. System processor 40 executes second module 51 to extract hemodynamic drug detection input features from the plurality of signal measures that detect the current effects of the hemodynamic drug administration event of patient 36. System processor 40 executes third module 52 to determine, based on the hemodynamic drug detection input features, the hemodynamic drug detection score of patient 36. If the hemodynamic drug detection score satisfies a predetermined hemodynamic detection criterion, system processor 40 invokes sensory alarm 58 of user interface 54 to send a third sensory signal to alert medical worker 38 that patient 36 is experiencing a hemodynamic drug administration event and not a current nociception event. The hemodynamic drug detection score and the third sensory signal help to prevent medical worker 38 from confusing the hemodynamic drug administration event with a nociception event and prevent medical worker 38 from unnecessarily administering analgesics to patient 36.
System memory 42 of hemodynamic monitor 10 can also include stable detection software code for detecting a stable episode of patient 36. The stable episode is defined as a period during which patient 36 does not experience a nociception event or a hemodynamic drug administration event. The stable detection software code can be a subpart of nociception software code 48. System processor 40 executes stable detection software code to extract stable detection input features from the plurality of signal measures. Stable detection software code can extract the stable detection input features from the plurality of signal measures using second module 51. The stable detection input features detect the stable episode of patient 36. System processor 40 executes third module 52 to determine, based on the stable detection input features, a stable score of patient 36. System processor 40 outputs the stable score of patient 36 to user interface 54 of display 12.
System processor 40 can execute first module 50 to extract a single batch of the plurality of signal measures for a given unit of time, and that single batch of signal measures can be used by second module 51 to extract all of nociception detection input features, hemodynamic drug detection input features, and stable detection input features for that unit of time. Second module 51 can extract all of nociception detection input features, hemodynamic drug detection input features, and stable detection input features concurrently from the plurality of signal measures. System processor 40 can execute third module 52 to concurrently determine the nociception detection score, the hemodynamic drug detection score, and the stable score. Nociception software code 48 of hemodynamic monitor 10 can utilize, in some examples, a classification-type machine learning model with binary positive versus negative labels to label selected data segments for training of machine learning models, as is discussed in greater detail below. Processor 40 can, in certain examples, output the nociception detection score and the hemodynamic drug detection score together to display 12 to compare and contrast the two probabilities and help medical worker 38 better understand whether a nociception event or a hemodynamic drug administration event is causing the increase in blood pressure and heart rate in patient 36.
As shown in
To machine train hemodynamic monitor 10 to identify the nociception detection input features described in
Signal measures are extracted from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles of each of the arterial pressure waveforms of nociception data segments 66. The signal measures can correspond to hemodynamic effects from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles. Those hemodynamic effects can include contractility, aortic compliance, stroke volume, vascular tone, afterload, and full cardiac cycle. The signal measures calculated or extracted by the waveform analysis of first step 88 of method 86 includes a mean, a maximum, a minimum, a duration, an area, a standard deviation, derivatives, and/or morphological measures from each of the systolic rise phase, the systolic decay phase, the systolic phase and the diastolic phase from each of the individual cardiac cycles. The signal measures can also include heart rate, respiratory rate, stroke volume, pulse pressure, pulse pressure variation (PPV), stroke volume variation (SVV), mean arterial pressure (MAP), systolic pressure (SYS), diastolic pressure (DIA), heart rate variability, cardiac output, peripheral resistance, vascular compliance, and/or left-ventricular contractility extracted from each of the individual cardiac cycles of each of the arterial pressure waveforms of nociception data segments 66.
After the signal measures are determined for nociception data segments 66, step 90 of method 86 is performed on the signal measures of nociception data segments 66. Step 90 of method 86 is to compute combinatorial measures between each of the signal measures of nociception data segments 66. Computing the combinatorial measures between the signal measures of nociception data segments 66 can include performing steps 92, 94, 96, and 98 shown in
Step 92 of method 86 is to arbitrarily select three signal measures from the signal measures of the nociception data segments. Next, different orders of power are calculated for each of the three signal measures to generate powers of the three signal measures in step 94 of method 86, shown in
The signal measures with the most predictive top combinatorial measures (i.e., combinatorial measures satisfying a threshold prediction criteria) are selected, in step 100 of method 86, to perform machine learning. The signal measures with the most predictive top combinatorial measures are top signal measures for nociception data segments 66 and are labeled as the nociception detection input features. With the nociception detection input features determined, hemodynamic monitor 10 is trained or programmed to perform waveform analysis on the arterial pressure waveform of patient 36 (shown in
A change detection algorithm, such as a cumulative sum (CUSUM) algorithm can be used to refine model outputs derived from the various input features determined by method 86. More specifically, a CUSUM algorithm can be used to independently determine nociception events and stable episodes in the clinical dataset 60 using arterial pressure waveform heart rate and blood pressure. The CUSUM algorithm can be designed to detect changes beyond a particular threshold (e.g., 10 mmHg) in a signal measure (e.g., blood pressure) over time. Other signal measures, such as contractility and heart rate variability, could alternatively be used as the at least two signal measures. Simultaneous changes in both heart rate and blood pressure, as detected by the CUSUM algorithm, can be a reliable indication of nociception or non-nociception in patient 36, as is discussed in greater detail below.
In general, any overlap between SBP event 110 and HR event 116 represents a simultaneous change (i.e., simultaneous increase) indicative of nociception. Where simultaneous change is detected, the earlier starting point of either SBP event 110 or HR event 116 is considered to be the starting point of nociception event 122. The earlier ending point of either SBP event 110 or HR event 116 is considered to be the ending point of nociception event 122. As shown in
The CUSUM algorithm can use an instantaneous change value s to calculate the cumulative sum for the plot A of signal measure X. The instantaneous change value s can be represented by plot B in
where i is a current time point and t is how far back in time in the plot A of signal measure X the CUSUM algorithm goes to make a comparison. For example, t can be fifteen minutes. In Equation 1, X is a value of the signal measure X plot at an indicated time, Δ is a predetermined change value (such as 10 mmHg delta for systolic blood pressure or a ten-percent change decrease), σ is a standard deviation of X, and s is an instantaneous change value for the plot A of signal measure X. The instantaneous change value s will only be positive when the difference between Xi−Xi-t in Equation 1 is greater than Δ/2 in Equation 1. The instantaneous change value s is used by the CUSUM algorithm to calculate a cumulative sum S of the plot A of signal measure X, which is represented by plot C in
The cumulative sum S shown in plot C and determined by Equation 2 is used to calculate change signal G of signal measure X, which is represented by plot D in
where i is the current time point from Equation 1 and t is how far back in time the CUSUM algorithm goes to make a comparison in Equation 1. The change signal Gi in Equation 3 only becomes positive if the value of the cumulative sum Si increases in time. Also, change signal Gi becomes larger as the difference between Xi−Xi-t in Equation 1 increases. If the value of the cumulative sum Si decreases or remains constant with time, the change signal Gi will be zero. Before outputting plot D of the change signal Gi to display 12 of hemodynamic monitor 10, the CUSUM algorithm can take the logarithm of the change signal Gi to further accentuate the positive values of the change signal Gi. Plot E in
The CUSUM algorithm can additionally and/or alternatively be used to identify stable episodes where no significant changes in systolic blood pressure or heart rate are detected. For example, a stable episode plotted by the CUSUM algorithm would have an output 106 where the output plot for systolic blood pressure 102 can be a flat line with a zero value and the output plot for heart rate 104 can be a flat line with a zero value. Such an episode can be represented in plots substantially similar to those shown in
Method 128 proceeds to step 132 to determine if the CUSUM algorithm detects a simultaneous change in systolic blood pressure and heart rate, as illustrated in
Returning to step 132, if the CUSUM algorithm does not detect a simultaneous change in systolic blood pressure and heart rate, method 128 proceeds to step 140 to determine if the machine learning model probability is greater than 50%. If the probability is greater than 50%, method 128 proceeds to step 142 where the probability is adjusted downward to less than 50%. Similar to step 136, this adjustment in the model probability reflects the confidence of the lack of detection of a simultaneous change by the CUSUM algorithm and corrects the machine learning model from predicting a non-nociception event when the CUSUM algorithm is predicting a nociception event. If the probability is not greater than 50% at step 140, method 128 instead proceeds to step 144 where the machine learning model's probability is not adjusted. The probability determined from any of steps 136, 138, 142, and 144 can be displayed by hemodynamic monitor 10. It should be understood that probability maximum and minimum values other than 100% and 50%, respectively, (e.g., 85% and 35%) can be used without departing from the scope of the invention.
The following are non-exclusive descriptions of possible embodiments of the present invention.
A method for monitoring arterial pressure of a patient and identifying nociception of the patient includes receiving, by a hemodynamic monitor, sensed hemodynamic data representative of an arterial pressure waveform of the patient; performing, by the hemodynamic monitor, waveform analysis of the sensed hemodynamic data to calculate a plurality of signal measures of the sensed hemodynamic data; extracting input features for the hemodynamic monitor from the plurality of signal measures that are indicative of a nociception event of the patient and a stable episode of the patient; monitoring, by a change detection algorithm of the hemodynamic monitor, at least two signal measures of the plurality of signal measures for a simultaneous change in the at least two signal measures; determining, by the hemodynamic monitor based on the input features, a nociception score representing a probability of the nociception event of the patient; adjusting or maintaining, by the hemodynamic monitor, the nociception score based on an output of the change detection algorithm; and displaying, by the hemodynamic monitor, an adjusted nociception score.
The method of the preceding paragraph can optionally include, additionally and/or alternatively, any one or more of the following features, configurations and/or additional components:
Determining, by the hemodynamic monitor based on an absence of a simultaneous change in the plurality of hemodynamic parameters, a stable score representing a probability of a stable episode where the patient is not experiencing a nociception event.
Training the hemodynamic monitor for determining the probability of the stable episode of the patient, wherein training the hemodynamic monitor for determining the probability of the stable episode comprises: identifying, using the change detection algorithm, stable data segments in the clinical dataset, wherein the stable data segments each comprise: stable blood pressure with no increase greater than a first threshold amount over a set period of time; and stable heart rate with no increase greater than a second threshold amount over the set period of time; identifying a starting point and an ending point of the stable blood pressure and the stable heart rate; labeling the stable data segments from the starting point to the ending point of the stable blood pressure and the stable heart rate; performing waveform analysis of the labeled stable data segments to calculate a plurality of stable signal measures of the stable data segments; and determining at least a portion of the input features by computing combinatorial measures between the plurality of stable signal measures and selecting stable signal measures from the plurality of stable signal measures with most predictive combinatorial measures as belonging to the input features.
Training the hemodynamic monitor for determining the probability of the current nociception event of the patient, wherein training the hemodynamic monitor comprises: collecting a clinical dataset including arterial pressure waveforms and clinical annotations of administration of a compound that alters cardiovascular hemodynamics; identifying, using the change detection algorithm, nociception data segments in the clinical dataset, wherein the nociception data segments each comprise: an increase in blood pressure of at least the first threshold amount compared to a prior time period; an increase in heart rate of at least the second threshold amount compared to the prior time period; and no infusion of the compound that alters cardiovascular hemodynamics started prior to the increase in blood pressure and the increase in heart rate; identifying a starting point and an ending point of the increase in the blood pressure and the increase in the heart rate; labeling the nociception data segments after the starting point and during the increase in the blood pressure and the increase in the heart rate; performing waveform analysis of the labeled nociception data segments to calculate a plurality of signal measures of the nociception data segments; and determining at least a portion of the input features by computing combinatorial measures between the plurality of signal measures and selecting signal measures from the plurality of signal measures with most predictive combinatorial measures as belonging to the input features.
The change detection algorithm comprises: processing, by the hemodynamic monitor, both the blood pressure and the heart rate through a cumulative sum (CUSUM) algorithm to acquire a first CUSUM output for the blood pressure and a second CUSUM output for the heart rate; monitoring, by the hemodynamic monitor, the first CUSUM output for an increase above the first threshold; monitoring, by the hemodynamic monitor, the second CUSUM output for an increase above the second threshold; and detecting a nociception data segment when the increase in the first CUSUM output over the first threshold overlaps in time with the increase in the second CUSUM output over the second threshold.
Adjusting the nociception score based on an output of the change detection algorithm comprises: identifying, using the change detection algorithm, an increase in systolic blood pressure in a time period; identifying, using the change detection algorithm, an increase in heart rate in the time period; and labeling an overlap in the increase in systolic blood pressure and heart rate as a simultaneous change indicative of a nociception event.
Adjusting the nociception score based on an output of the change detection algorithm further comprises adjusting the nociception score to a maximum value if the nociception score is below the maximum value and a simultaneous change is detected by the change detection algorithm.
Adjusting the nociception score based on an output of the change detection means further comprises adjusting the nociception score to a minimum value if the nociception score is above the minimum value and a simultaneous change is not detected by the change detection algorithm.
The at least two signal measures comprise blood pressure and heart rate.
A system for monitoring arterial pressure of a patient and providing a warning to medical personnel of nociception of the patient, the system comprising: a hemodynamic sensor that produces hemodynamic data representative of an arterial pressure waveform of the patient; a system memory that stores nociception detection software code; a user interface that includes a sensory alarm that provides a sensory signal to warn the medical personnel of a nociception event of the patient; and a hardware processor that is configured to execute the nociception detection software code to: perform waveform analysis of the hemodynamic data to determine a plurality of signal measures; extract detection input features from the plurality of signal measures that are indicative of the nociception event of the patient; monitor at least two signal measures from the plurality of signal measures for a simultaneous change in the at least two signal measures; determine, based on the detection input features and a presence of the simultaneous change in the at least two signal measures, a nociception score representing a probability of the nociception event of the patient; and invoke the sensory alarm of the user interface in response to the nociception score satisfying a predetermined detection criterion.
The system of the preceding paragraph can optionally include, additionally and/or alternatively, any one or more of the following features, configurations and/or additional components:
A change detection algorithm monitors the plurality of hemodynamic parameters for a simultaneous change in the plurality of hemodynamic parameters.
The detection input features of the nociception detection software code are determined by detection machine training, wherein the detection machine training comprises: collecting a clinical dataset containing arterial pressure waveforms and clinical annotations of administrations of a compound that alters cardiovascular hemodynamics; identifying nociception data segments in the clinical dataset, wherein the nociception data segments each comprise: an increase in blood pressure of at least a first threshold amount compared to a prior time period; an increase in heart rate of at least a second threshold amount compared to the prior time period; and no infusion of the compound that alters cardiovascular hemodynamics started prior to the increase in blood pressure and the increase in heart rate; identifying a starting point and an ending point of the increase in the blood pressure and the increase in the heart rate; labeling the nociception data segments after the starting point and during the increase in the blood pressure and the increase in the heart rate; performing waveform analysis of the labeled nociception data segments to calculate a plurality of signal measures of the nociception data segments; and determining the detection input features by computing combinatorial measures between the plurality of signal measures of the nociception data segments and selecting top signal measures from the plurality of signal measures of the nociception data segments with most predictive combinatorial measures and labeling the top signal measures as the detection input features.
The system memory stores stable detection software code for determining a probability of a stable episode of the patient, the hardware processor being configured to execute the stable detection software code to: extract stable detection input features from the plurality of signal measures that are indicative of the stable episode of the patient; and determine, based on the stable detection input features, a stable score representing a probability of the stable episode where the patient is not experiencing a nociception event.
The stable software detection code is further configured to determine the stable score based on an absence of a simultaneous change in the plurality of hemodynamic parameters, as determined by the change detection algorithm.
The stable detection input features of the stable detection software code are determined by stable detection machine training, wherein the stable machine training comprises: identifying stable data segments in the clinical dataset, wherein the stable data segments each comprise: stable blood pressure with no increase greater than the first threshold amount over a set period of time; stable heart rate with no increase greater than the second threshold amount over the set period of time; and no infusion performed of a compound that alters cardiovascular hemodynamics; identifying a starting point and an ending point of the stable blood pressure and the stable heart rate; labeling the stable data segments from the starting point to the ending point of the stable blood pressure and the stable heart rate; performing waveform analysis of the labeled stable data segments to calculate a plurality of stable signal measures of the stable data segments; and determining the stable detection input features by computing combinatorial measures between the plurality of stable signal measures and selecting stable signal measures from the plurality of stable signal measures with most predictive combinatorial measures as being the stable detection input features.
Performing waveform analysis of the labeled nociception data segments to calculate a plurality of signal measures of the nociception data segments comprises: identifying individual cardiac cycles in the arterial pressure waveform of the clinical dataset; 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 signal measures from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles.
The signal measures correspond to hemodynamic effects from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles, and wherein the hemodynamic effects comprise contractility, aortic compliance, stroke volume, vascular tone, afterload, and full cardiac cycle.
The signal measures comprise a mean, a maximum, a minimum, a duration, an area, a standard deviation, derivatives, and/or morphological measures from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles.
The signal measures 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 compliance, and/or left-ventricular contractility extracted from each of the individual cardiac cycles.
The at least two signal measures comprise blood pressure and heart rate.
Computing the combinatorial measures between the plurality of signal measures of the nociception data segments comprises: performing step one by arbitrarily selecting three signal measures from the plurality of signal measures of the nociception data segments; performing step two by calculating different orders of power for each of the three signal measures to generate powers of the three signal measures; performing step three by multiplying the powers of the three signal measures together to generate the product of the powers of the three signal measures; performing step four by performing receiver operating characteristic (ROC) analysis of the product of the powers to arrive at a combinatorial measure for the three signal measures; and repeating steps one, two, three, and four until all combinatorial measures have been computed between all of the plurality of signal measures of the nociception data segments.
The hemodynamic sensor is a noninvasive hemodynamic sensor that is attachable to an extremity of the patient.
The hemodynamic sensor is a minimally invasive arterial catheter based hemodynamic sensor.
The hemodynamic sensor produces the hemodynamic data as an analog hemodynamic sensor signal representative of the arterial pressure waveform of the patient.
An analog-to-digital converter that converts the analog hemodynamic sensor signal to digital hemodynamic data representative of the arterial pressure waveform of the patient.
A method for monitoring arterial pressure of a patient and identifying nociception of the patient, the method comprising: receiving, by a hemodynamic monitor, sensed hemodynamic data representative of an arterial pressure waveform of the patient; performing, by the hemodynamic monitor, waveform analysis of the sensed hemodynamic data to calculate a first signal measure and a second signal measure of the sensed hemodynamic data; processing, by the hemodynamic monitor, both the first signal measure and the second signal measure through a cumulative sum (CUSUM) algorithm to acquire a first CUSUM output for the first signal measure and a second CUSUM output for the second signal measure; monitoring, by the hemodynamic monitor, the first CUSUM output and the second CUSUM output for a change in both the first CUSUM output and the second CUSUM output; detecting a nociception event of the patient when the change in the first CUSUM output overlaps in time with the change in the second CUSUM output; and outputting to a user interface of the hemodynamic monitor a sensory signal to warn medical personnel of the nociception event of the patient.
The method of the preceding paragraph can optionally include, additionally and/or alternatively, any one or more of the following features, configurations and/or additional components:
Performing waveform analysis of the sensed hemodynamic data to calculate the first signal measure and the second signal measure of the sensed hemodynamic data comprises: identifying individual cardiac cycles in the arterial pressure waveform of the sensed hemodynamic data of the patient; 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 signal measures from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles.
The signal measures correspond to hemodynamic effects from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles, and wherein the hemodynamic effects comprise contractility, aortic compliance, stroke volume, vascular tone, afterload, and full cardiac cycle.
The signal measures comprise a mean, a maximum, a minimum, a duration, an area, a standard deviation, derivatives, and/or morphological measures from each of the systolic rise phase, the systolic decay phase, and the diastolic phase from each of the individual cardiac cycles.
The signal measures 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 compliance, and/or left-ventricular contractility extracted from each of the individual cardiac cycles.
The first signal measure and the second signal measure are selected from the signal measures.
The first signal measure is systolic pressure and the second signal measure is heart rate.
The change in the first CUSUM output is an increase above a first threshold and the change in the second CUSUM output is an increase above a second threshold.
A system for monitoring of arterial pressure of a patient and providing a warning to medical personnel of nociception of the patient, the system comprising: a hemodynamic sensor that produces hemodynamic data representative of an arterial pressure waveform of the patient; a system memory that stores nociception detection software code; a user interface that includes a sensory alarm that provides a sensory signal to warn the medical personnel of a nociception event of the patient; and a hardware processor that is configured to execute the nociception detection software code to: perform waveform analysis of the hemodynamic data to determine a first signal measure and a second signal measure; process both the first signal measure and the second signal measure through a cumulative sum (CUSUM) algorithm to acquire a first CUSUM output for the first signal measure and a second CUSUM output for the second signal measure; monitor over time the first CUSUM output and the second CUSUM output for a change in both the first CUSUM output and the second CUSUM output; detect a nociception event of the patient when the change in the first CUSUM output overlaps in time the change in the second CUSUM output; and output to a user interface of the hemodynamic monitor a sensory signal to warn medical personnel of the nociception event of the patient.
While the invention has been described with reference to an exemplary embodiment(s), it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. For example, while the CUSUM algorithm can be used to adjust the machine learning model of hemodynamic monitor 10, hemodynamic monitor 10 can also use the CUSUM algorithm directly to detect a nociception event of patient 36 from the sensed hemodynamic data of patient 36. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment(s) disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
This application claims the benefit of International Application No. PCT/US2023/013831, filed Feb. 24, 2023, and entitled “NOCICEPTION PREDICTION AND DETECTION USING CUMULATIVE SUM ALGORITHM AND MACHINE LEARNING CLASSIFICATION,” the disclosure of which is hereby incorporated by reference in its entirety. International Application No. PCT/US2023/013831 claims the benefit of U.S. Provisional Application No. 63/314,978, filed Feb. 28, 2022, and entitled “NOCICEPTION PREDICTION AND DETECTION USING CUMULATIVE SUM ALGORITHM AND MACHINE LEARNING CLASSIFICATION,” the disclosure of which is hereby incorporated by reference in its entirety.
Number | Date | Country | |
---|---|---|---|
63314978 | Feb 2022 | US |
Number | Date | Country | |
---|---|---|---|
Parent | PCT/US2023/013831 | Feb 2023 | WO |
Child | 18818159 | US |