US-12622844-B2 - Closed-loop system for cardiopulmonary resuscitation (CPR)

US12622844B2US 12622844 B2US12622844 B2US 12622844B2US-12622844-B2

Abstract

Systems, devices, and techniques for controlling a compression unit associated with cardiopulmonary resuscitation (CPR) are described herein. For example, a medical system may include a compression unit configured to apply pressure to a torso region of a patient. The compression unit may be configured to move within space according to at least one degree of freedom. The medical system may further include processing circuitry configured to receive one or more sets of data representative of one or more patient parameters of the patient. Additionally, the medical system may generate, using a deep learning model, an output data set representing a predicted trajectory of at least one patient parameter of the one or more patient parameters, determine a set of control parameters, and control the compression unit to apply the pressure to the torso region of the patient.

Inventors

Demetris Yannopoulos
Evangelos Theodorou
Manan Gandhi
Matthew Olson
Pierre Sebastian
Yunpeng Pan

Assignees

REGENTS OF THE UNIVERSITY OF MINNESOTA
GEORGIA TECH RESEARCH CORPORATION

Dates

Publication Date: 20260512
Application Date: 20190719

Claims (20)

1 . A medical system comprising: a compression mechanism configured to apply pressure to a torso region of a patient, wherein the compression mechanism is configured to move according to at least one degree of freedom; and processing circuitry configured to: receive, from a plurality of physiological sensors, one or more sets of data representative of a plurality of patient parameters over a period of time, wherein the plurality of patient parameters comprises a right atrium pressure and an aortic blood pressure; generate, using a deep learning model, output data sets over the period of time of predicted future trajectories of at least one patient parameter of the plurality of patient parameters, each predicted future trajectory of the predicted future trajectories representative of a predicted future state of the patient; iteratively determine, based on at least one output data set of the predicted future trajectories, different sets of values for a plurality of control parameters, wherein the plurality of control parameters comprises an oscillation frequency, a duty cycle of compression, and compression depth; and control, using the different sets of values for the plurality of control parameters over the period of time, the compression mechanism to apply the pressure to the torso region of the patient.
2 . The medical system of claim 1 , wherein the at least one patient parameter comprises coronary perfusion pressure (CPP).
3 . The medical system of claim 1 , wherein the plurality of physiological sensors comprises at least one of piezoelectric pressure sensors, intraosseous pressure sensors, flow meter sensors, electrocardiogram (ECG) electrodes, fluoroscopic imaging sensors, ultrasound imaging transducers, impedance mapping sensors, infrared imaging sensors, intrathoracic pressure sensors, or capnography sensors.
4 . The medical system of claim 1 , wherein the compression mechanism is configured to repeatedly move a piston along a longitudinal axis between a proximal end to a distal end according to a value of the duty cycle, and wherein the compression mechanism is further configured to: apply the pressure by moving the piston towards the distal end along the longitudinal axis and towards the torso region of the patient, and remove at least a portion of the pressure by moving the piston towards the proximal end along the longitudinal axis and away from the torso region of the patient.
5 . The medical system of claim 4 , wherein the one or more degrees of freedom comprise: a first linear degree of freedom allowing the compression mechanism to move perpendicular to a two-dimensional plane representing the torso of the patient; a second linear degree of freedom and a third linear degree of freedom allowing the compression mechanism to move parallel to the two-dimensional plane; a first rotational degree of freedom allowing the compression mechanism to rotate about a first axis within the two-dimensional plane; and a second rotational degree of freedom allowing the compression mechanism to rotate about a second axis within the two-dimensional plane, the first axis being perpendicular to the second axis, wherein the compression mechanism is configured to alter a direction in which the piston applies the pressure and removes at least the portion of the pressure by moving the piston within the one or more degrees of freedom.
6 . The medical system of claim 1 , wherein the processing circuitry is configured to determine the different sets of values for the plurality of control parameters in real time.
7 . The medical system of claim 1 , wherein the plurality of control parameters comprises at least one of: a maximum applied pressure of the compression mechanism, or one or more position parameters corresponding to the at least one degree of freedom, and wherein the one or more position parameters comprise at least one of a linear velocity of the compression mechanism, an angular velocity of the compression mechanism, a linear acceleration of the compression mechanism, or an angular acceleration of the compression mechanism.
8 . The medical system of claim 1 , wherein the processing circuitry is configured to update, based on the one or more sets of data, one or more model parameters of the deep learning model, wherein the one or more sets of data comprise real-time data sets and historical data sets.
9 . The medical system of claim 8 , wherein the processing circuitry is configured to train the deep learning model based on the historical data sets, and wherein the historical data sets represent data measured from a plurality of historical test patients.
10 . The medical system of claim 8 , wherein the processing circuitry is configured to update the one or more model parameters of the deep learning model by: calculating, using the deep learning model, a set of predicted values based on the one or more sets of data, wherein the deep learning model comprises a plurality of model parameters comprising the one or more model parameters; calculating, based on the set of predicted values and the one or more sets of data, a set of error values, wherein the set of error values represents an error of the set of predicted values relative to the one or more sets of data; and updating, based on the set of error values, the one or more model parameters of the deep learning model.
11 . The medical system of claim 1 , wherein the processing circuitry is configured to determine each set of values of the different sets of values for the plurality of control parameters by: creating a plurality of sets of possible control parameter values based on the at least one output data set; calculating, using a cost function, a cost value for each set of possible control parameter values of the plurality of sets of possible control parameter values; and identifying a lowest cost value set of the plurality of sets of possible control parameter values for controlling the compression mechanism.
12 . The medical system of claim 1 , further comprising the plurality of physiological sensors.
13 . A method comprising: receiving, by processing circuitry and from a plurality of physiological sensors, one or more sets of data representative of a plurality of patient parameters over a period of time, wherein the plurality of patient parameters comprises a right atrium pressure and an aortic blood pressure; generating, by the processing circuitry and using a deep learning model, output data sets over the period of time of predicted future trajectories of at least one patient parameter of the plurality of patient parameters, each predicted future trajectory of the predicted future trajectories representative of a predicted future state of the patient; iteratively determining, by the processing circuitry and based on at least one output data set of the predicted future trajectories, different sets of values for a plurality of control parameters, wherein the plurality of control parameters comprises an oscillation frequency, a duty cycle of compression, and compression depth; and controlling, by the processing circuitry and using the different sets of values for the plurality of control parameters over the period of time, a compression mechanism to apply pressure to a torso region of the patient, wherein the compression mechanism is configured to move according to at least one degree of freedom.
14 . The method of claim 13 , wherein the at least one patient parameter comprises coronary perfusion pressure (CPP).
15 . The method of claim 13 , wherein the plurality of physiological sensors comprises at least one of piezoelectric pressure sensors, intraosseous pressure sensors, flow meter sensors, electrocardiogram (ECG) electrodes, fluoroscopic imaging sensors, ultrasound imaging transducers, impedance mapping sensors, infrared imaging sensors, intrathoracic pressure sensors, or capnography sensors.
16 . The method of claim 13 , wherein controlling the compression mechanism comprises controlling the compression mechanism to repeatedly move a piston along a longitudinal axis between a proximal end to a distal end according to a value of the duty cycle, and wherein the method further comprises: applying the pressure by moving the piston towards the distal end along the longitudinal axis and towards the torso region of the patient, and removing at least a portion of the pressure by moving the piston towards the proximal end along the longitudinal axis and away from the torso region of the patient.
17 . The method of claim 13 , wherein the one or more degrees of freedom comprise: a first horizontal degree of freedom allowing the compression mechanism to move perpendicular to a two-dimensional plane representing the torso of the patient; a second horizontal degree of freedom and a third horizontal degree of freedom allowing the compression mechanism to move parallel to the two-dimensional plane; a first rotational degree of freedom allowing the compression mechanism to rotate about a first axis within the two-dimensional plane; and a second rotational degree of freedom allowing the compression mechanism to rotate about a second axis within the two-dimensional plane, the first axis being perpendicular to the second axis, wherein the compression mechanism is configured to alter a direction in which the piston applies the pressure and removes at least the portion of the pressure by moving the piston within the one or more degrees of freedom.
18 . The method of claim 13 , wherein the set of one or more control parameters comprises at least one of: a maximum applied pressure of the compression mechanism, or one or more position parameters corresponding to the at least one degree of freedom, and wherein the one or more position parameters comprise at least one of a linear velocity of the compression mechanism, an angular velocity of the compression mechanism, a linear acceleration of the compression mechanism, and an angular acceleration of the compression mechanism.
19 . The method of claim 13 , further comprising updating, based on the one or more sets of data, one or more model parameters of the deep learning model, wherein the one or more sets of data comprise real-time data sets and historical data sets.
20 . A system comprising: a memory comprising a deep learning model; and processing circuitry configured to: receive, from a plurality of physiological sensors, one or more sets of data representative of a plurality of patient parameters over a period of time, wherein the plurality of patient parameters comprises a right atrium pressure and an aortic blood pressure; generate, using the deep learning model, output data sets over the period of time of predicted future trajectories of at least one patient parameter of the plurality of patient parameters, each predicted future trajectory of the predicted future trajectories representative of a predicted future state of the patient; iteratively determine, based on at least one output data set of the predicted future trajectories, different sets of values for a plurality of control parameters, wherein the plurality of control parameters comprises an oscillation frequency, a duty cycle of compression, and compression depth; and control, using the different sets of values for the plurality of control parameters over the period of time, the compression mechanism to apply the pressure to the torso region of the patient.

Description

This application is a National Stage application under 35 U.S.C. § 371 of PCT Application No. PCT/US2019/042634, entitled “CLOSED-LOOP SYSTEM FOR CARDIOPULMONARY RESUSCITATION (CPR)” and filed on Jul. 19, 2019, which claims the benefit of U.S. Provisional Patent Application No. 62/701,289, titled “CLOSED-LOOP SYSTEM FOR CARDIOPULMONARY RESUSCITATION (CPR)” and filed Jul. 20, 2018. The entire contents of application nos. PCT/US2019/042634 and 62/701,289 are incorporated herein by reference. TECHNICAL FIELD The disclosure relates to systems and techniques for delivery of cardiopulmonary resuscitation (CPR). BACKGROUND Cardiopulmonary resuscitation (CPR) is an emergency procedure for treating, among other things, serious heart conditions (e.g., heart failure). CPR may include chest compressions and artificial respiration intended to induce circulation in a patient's body and deliver oxygen to the patient's organs such as the brain. Chest compressions and rescue breaths may be delivered at a predetermined frequency. In some cases, a human actor (e.g., bystander, paramedic, clinician, or the like) may perform CPR on a patient experiencing cardiac arrest. SUMMARY Systems, devices, and techniques are described for controlling cardiopulmonary resuscitation (CPR) on a patient using a medical device. In some examples, the medical device (e.g., a compression unit) may deliver CPR based on one or more sets of data generated by one or more physiological sensors, the one or more sets of data being representative of physiological signals (e.g., physiological parameters) sensed from the patient. Additionally, or alternatively, the medical device may deliver CPR based on a historical database including physiological data from a plurality of test subjects. In this manner, the medical device may deliver CPR in a closed-loop system (e.g. CPR is administered to bring the one or more sets of data to a target state). In some examples, a medical system may use a deep learning model to represent the internal cardiovascular function of the patient, and apply an a controller to control the medical device, such as the compression unit, to provide CPR to the patient based on one or more sets of data generated from sensed physiological signals of the patient, and a plurality of test subjects. The system may apply the one or more sets of data to a deep learning model that outputs a data set that represents a predicted trajectory of a patient parameter, such as coronary perfusion pressure or other parameter indicative of patient physiology. In one example, the deep learning model in combination with a controller may map the one or more sets of data to a set of control parameters, where the set of control parameters defines operation of the medical device to perform CPR on the patient. The set of control parameters may cause the medical device to move within one or more available degrees of freedom. For example, the compression unit may be configured to move horizontally within a three-dimensional space, and the compression unit may additionally be configured to rotate within the three-dimensional space about an axis or a reference point. In some examples, the medical device may define a single degree of freedom (e.g., the compression unit is configured to move along one axis). In other examples, the medical device may have five degrees of freedom (e.g., the compression unit may move horizontally parallel to) three axes and rotate about two axes. Additionally, or alternatively, the medical system may implement a heuristic operation in order to determine the set of control parameters for causing the medical device to move within the one or more available degrees of freedom. For example, the medical system may include one or more sensors configured to generate one or more sets of data being representative of physiological signals. In turn, the medical system may output the one or more sets of data, or some subset thereof, for display via a user interface. Subsequently, the medical system may receive, via the user interface, input representative of a user selection of a one or more values for respective control parameters of a set of control parameters. The one or more values of the set of control parameters may at least partially control the medical device to move within one or more available degrees of freedom such that the medical device performs CPR on a patient. In this manner, the user selection associated with the set of control parameters may fully, or partially with input from the system, control the medical device to perform CPR. In one example, a medical system includes a compression unit configured to apply pressure to a torso region of a patient, the compression unit configured to move according to at least one degree of freedom. The medical system further includes processing circuitry configured to receive, from one or more physiological sensors configured to generate one or more sets of data representative of one or more patien