System for detecting asynchronous breathing events and related methods and components

Abstract

The method includes extracting a lung volume waveform from an electrical impedance tomography device. The method further includes extracting at least one of a flow waveform and a pressure waveform. The method also includes aligning the lung volume waveform and at least one of the flow waveform and the pressure waveform with respect to time. The method further includes comparing the lung volume waveform and at least one of the flow waveform and the pressure waveform. The method also includes determining whether an asynchronous breathing event has occurred based on comparing the lung volume waveform and at least one of the flow waveform and the pressure waveform. The method further includes classifying the asynchronous breathing event. The method also includes providing a warning identifying the asynchronous breathing event. The system includes a receiver, a processor, and a memory device configured to execute the method.

Description

Technical Field

[0001] Claims of Priority This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application No. 63 / 367,807, filed on July 6, 2022, the disclosure of which is hereby incorporated by reference in its entirety.

[0002] Embodiments of the present disclosure generally relate to medical support systems. In particular, embodiments of the present disclosure relate to systems for detecting asynchronous breathing events and related methods and components.

Background Art

[0003] Mechanical ventilation is commonly used in hospitals to assist a patient's breathing during treatment of respiratory failure, anesthesia, and other respiratory diseases such as acute respiratory distress syndrome (ARDS), and to maintain or restore the patient's breath. Such control of human breathing involves complex feedback mechanisms among the nervous system - chemoreceptors (central and peripheral), mechanoreceptors - and pulmonary vagal inputs, the lungs themselves, the chest wall, and the respiratory muscles. When mechanical ventilation is used, synchronization between the ventilator and the patient's breathing rhythm makes it possible to effectively and safely reduce the load on the inspiratory muscles and ensure the supply of oxygen to the body. Furthermore, asynchronous events between the mechanical ventilator and the patient's breathing rhythm can reduce the efficiency of the mechanical ventilator and cause harm to the patient, such as by overinflating or underinflating the patient's lungs.

Summary of the Invention

Means for Solving the Problems

[0004] Embodiments of the present disclosure include a system configured to detect asynchronous breathing events. The system includes a receiver configured to receive ventilation data from at least one of a mechanical ventilator, an airway flow sensor, or an airway pressure sensor, and to receive impedance data from an electrical impedance tomography device. The system further includes a processor, a memory device configured to store the ventilation data and the impedance data, and a non-transitory computer-readable medium storing instructions. The instructions cause the processor to extract a plethysmograph from the impedance data. The instructions further cause the processor to extract at least one of a flow waveform and a pressure waveform from the ventilation data. The instructions also cause the processor to compare the plethysmograph with at least one of the flow waveform and the pressure waveform. The instructions further cause the processor to determine whether an asynchronous event has occurred based on comparing the plethysmograph with at least one of the flow waveform and the pressure waveform. The instructions also cause the processor to classify the asynchronous event. The instructions further cause the processor to provide a classification of the asynchronous event.

[0005] Other embodiments of the present disclosure include a method of detecting asynchronous breathing events. The method includes extracting an impedance data waveform from an electrical impedance tomography device. The method further includes extracting one or more of a flow waveform and a pressure waveform. The method also includes aligning the impedance data waveform with one or more of the flow waveform and the pressure waveform with respect to time. The method further includes comparing the impedance data waveform with one or more of the flow waveform and the pressure waveform. The method also includes determining whether an asynchronous breathing event has occurred based on comparing the impedance data waveform with one or more of the flow waveform and the pressure waveform. The method further includes classifying the asynchronous breathing event. The method also includes providing information identifying the asynchronous breathing event.

[0006] Another embodiment of the present disclosure includes a method for detecting asynchronous breathing events. The method includes retrieving lung impedance data from an electrical impedance tomography device that includes a plethysmogram from at least one region of interest. The method further includes retrieving one or more of a flow waveform and a pressure waveform. The method also includes comparing the impedance data with one or more of the flow waveform and the pressure waveform. The method further includes identifying a combined cycle based on at least the impedance data, the flow waveform, and the pressure waveform. The method also includes classifying the combined cycle. The method also includes providing a recommended adjustment for a mechanical ventilator.

[0007] To facilitate the identification of any discussion of a particular element or operation, one or several of the most significant digits in the reference numbers refer to the drawing number in which the element is first introduced.

Brief Description of the Drawings

[0008]

Figure 1

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DETAILED DESCRIPTION OF THE INVENTION

[0009] The drawings presented in this specification are not meant to be actual diagrams of any particular system, method, or their components, but rather are merely ideal representations used to illustrate exemplary embodiments. The drawings are not necessarily to scale.

[0010] As used herein, the term "substantially" with respect to a given parameter means that the given parameter, characteristic, or state is satisfied with a small difference, such as within acceptable manufacturing tolerances, to the extent that would be understood by one of ordinary skill in the art, and includes that. For example, a parameter that is substantially satisfied can be satisfied at least about 90%, at least about 95%, at least about 99%, or even at least about 100%.

[0011] As used herein, "about" referring to a numerical value for a particular parameter includes that numerical value, and the degree of variation from the numerical value that would be understood by one of ordinary skill in the art is within the acceptable tolerance for the particular parameter. For example, "about" referring to a numerical value can include additional numerical values within the range of 90.0 percent to 110.0 percent of the numerical value, such as within the range of 95.0 percent to 105.0 percent of the numerical value, within the range of 97.5 percent to 102.5 percent of the numerical value, within the range of 99.0 percent to 101.0 percent of the numerical value, within the range of 99.5 percent to 100.5 percent of the numerical value, or within the range of 99.9 percent to 100.1 percent of the numerical value.

[0012] As used herein, any relative terms such as "first", "second", "top", "bottom", "upper", "lower", "right", "left", etc. are used for clarification and convenience in understanding the disclosure and the accompanying drawings, and do not imply or depend on any particular preference, orientation, or order, except where the context clearly indicates a different meaning.

[0013] As used herein, the term "and / or" means any and all combinations of one or more of the associated listed items, and includes them.

[0014] As used herein, the terms "vertical" and "horizontal" refer to the orientation as shown in the figures.

[0015] Mechanical ventilation (MV) is a treatment commonly used in hospitals to maintain and / or assist a patient's breathing. When assisted mechanical ventilation is used, the synchrony between the ventilator and the patient's respiratory rhythm makes it possible to effectively and safely reduce the load on the inspiratory muscles and ensure the supply of oxygen to the body. Patient-ventilator asynchrony (PVA) can be defined as a mismatch between the patient's needs and the time, flow rate, tidal volume, and / or pressure waveform delivered according to the ventilator settings. PVA can be a subtle change that can be seen in the time, flow rate, tidal volume, and / or pressure waveform, even during monitoring of the respiratory mechanism or during monitoring of the patient's clinical condition. However, their detection requires trained professionals to pay attention in a specialized real-time manner at the bedside. Identifying PVA when there are no trained professionals can reduce the occurrence of PVA by shortening the time before PVA is detected and adjustments are made to the mechanical ventilation system, which can reduce the harmful effects of PVA on the patient.

[0016] Embodiments of the present disclosure can include an electrical impedance tomography (EIT) device configured to measure the regional distribution of changes in lung impedance or a patient, where the regional distribution of these changes correlates with the regional distribution of lung volume and the regional distribution of tidal volume, and the tidal volume is the amount or air that a patient receives during a respiratory cycle. A plethysmogram is a waveform of the sum of impedance changes in a region of interest plotted over time. Thus, the plethysmogram waveform correlates with local lung volume. In some embodiments, the patient's lung volume and ventilation distribution are measured using other known processes and / or devices such as ultrasonic flow measurement, MRI, tracer gas, etc.

[0017] EIT is an imaging technique that includes positioning electrodes via an electrode belt placed around a region of a patient's body (e.g., around the patient's chest for imaging the lungs), injecting an electrical excitation signal through a pair of electrodes, and measuring the induced response signals detected by other electrodes of the electrode belt. As a result, an EIT system can generate an image based on voltage measurements indicating estimated impedance values. In contrast to other imaging techniques, EIT is non-invasive and has no specific exposure risks that can limit the number and frequency of monitoring operations (as in the case of techniques such as X-rays). As a result, EIT is suitable for continuously monitoring a patient's condition, particularly for determining a patient's respiratory and hemodynamic parameters using the measurements and monitoring real-time two-dimensional images, and is thus suitable for use in monitoring a patient's lungs.

[0018] FIG. 1 is a schematic diagram of a portion of an EIT system 100 showing a plurality of electrodes 110 positioned around an area of interest (e.g., the thoracic cavity) of a patient 105. The electrodes 110 of the EIT system 100 may be physically held in place by an electrode belt 103. The arrangement of the electrodes 110 may be transverse to the patient's cranio-caudal axis 104. Although the electrodes 110 are shown in FIG. 1 as being arranged only partially around the patient 105, the electrodes 110 may be arranged around the entire patient 105 depending on the particular area of interest that is available or desirable for measurement. The electrodes 110 may be coupled to a computing system configured to control the operation of the electrodes 110 and perform reconstruction of the EIT image.

[0019] FIG. 2 is a schematic diagram showing a cross-section of the thoracic cavity of the patient 105 along the plane of the electrodes. A voltage can be applied to a pair of electrodes 110 (indicated by electrodes having + and - symbols) to inject an excitation current between the electrode pair into the patient. As a result, voltages (e.g., V1, V2, V3... V n ) can be detected by the other electrodes and measured by the EIT system 100. The current injection can be performed for the measurement cycle according to a circular pattern using different electrode pairs to generate the excitation current.

[0020] FIG. 3 is a schematic block diagram of an EIT system 300 according to one embodiment of the present disclosure. The EIT system 300 can include an electrode belt 310 operably coupled to a data processing system 320. The electrode belt 310 and the data processing system 320 may be coupled together via a wired connection (e.g., a cable) and / or may have communication modules for communicating wirelessly with each other. The data processing system 320 can include a processor 322 operably coupled to an electronic display 324, an input device 326, and a memory device 328. The electronic display 324 may be constructed in a single form factor for an EIT device coupled to the electrode belt 310, together with the data processing system 320. In some embodiments, the electronic display 324 and the data processing system 320 may be separate units of an EIT device coupled to the electrode belt 310. In still other embodiments, the EIT system 300 may be integrated within another host system configured to perform additional medical measurements and / or procedures, and the electrode belt 310 may be coupled to a port of the host system that already has its own input device, memory device, and electronic display. Thus, the host system can have EIT processing software installed therein. Such software may be incorporated into the host system prior to use in the field or may be updated after installation.

[0021] Processor 322 can coordinate communications between various devices and execute instructions stored in the computer-readable medium of memory device 328 to direct current excitation, data acquisition, data analysis, and / or image reconstruction. As an example, memory device 328 can include a library of finite element meshes used by processor 322 to model a patient's body within the region of interest for performing image reconstruction. Input device 326 can include devices such as a keyboard, touch screen interface, computer mouse, remote control, mobile device, or other device configured to receive information that can be used by processor 322 to receive input from an operator of EIT system 300. Thus, in the case of a touch screen interface, electronic display 324 and input device 326 for receiving user input can be integrated within the same device. Electronic display 324 can be configured to receive data and output an EIT image reconstructed by the processor for viewing by the operator. Additional data (e.g., numerical data, graphs, trend information, and other information considered useful to the operator) can also be generated by processor 322 from only the measured EIT data or in combination with other non-EIT data from other devices coupled thereto. Such additional data may be displayed on electronic display 324.

[0022] Although not shown, EIT system 300 may include components that may be included to facilitate communication with and / or current excitation of electrode belt 310, as would be understood by one of ordinary skill in the art, and may include, for example, one or more analog-to-digital converters, signal processing circuits, demodulation circuits, power supplies, and the like.

[0023] FIG. 4 shows a schematic diagram of a system 400 for mechanically ventilating a patient. For example, system 400 includes a ventilator system 402, an EIT system 404, and a controller 406 for operating system 400. Ventilator system 402 and EIT system 404 are operably coupled to controller 406.

[0024] In some embodiments, ventilator system 402 includes a mechanical ventilator 408 that provides respiratory support or assistance to patient 410. For example, mechanical ventilator 408 may provide a flow of medical gas that may include one or more of air, oxygen, nitrogen, and helium. In some embodiments, the flow of medical gas can further include additives such as aerosolized drugs or anesthetics. Ventilator system 402 may further include a breathing circuit 412, an inhalation limb 414, a patient limb 416, and a patient connector 418. In some embodiments, mechanical ventilator 408 may provide a flow of medical gas to breathing circuit 412 through inhalation limb 414 connected to an intake port 420 of mechanical ventilator 408. The medical gas can flow (e.g., circulate) through inhalation limb 414 and into patient limb 416 of breathing circuit 412. Thus, mechanical ventilator 408 can provide medical gas to patient 410 through patient connector 418.

[0025] Exhaled gas from patient 410 may be returned to mechanical ventilator 408 through patient connector 418 and patient limb 416. In some embodiments, the exhaled gas can be directed through one or more valves (e.g., check valves) to an exhalation limb 422 of breathing circuit 412. For example, ventilator system 402 can further include a plurality of check valves that can be placed at various points along breathing circuit 412 to allow only the desired direction of medical gas flow along the appropriate path towards or away from patient 410.

[0026] Furthermore, the exhaled gas may be returned to the mechanical ventilator 408 through the expiratory port 424 of the mechanical ventilator 408.

[0027] In some embodiments, the expiratory port 424 includes a controllable flow valve that is adjustable to control the pressure within the breathing circuit 412. By adjusting the flow valve, a back pressure can be created that is applied to the patient 410 during exhalation to create positive end expiratory pressure (PEEP). Thus, the system 400 can include any conventional system for providing PEEP therapy to the patient 410. Additionally, other systems and configurations as recognized by those skilled in the art are within the scope of the present disclosure.

[0028] The ventilator system 402 may further include one or more gas monitoring sensors 426. In some embodiments, the one or more gas monitoring sensors 426 may be disposed within the patient connection portion 418 of the breathing circuit 412. In alternative embodiments, the one or more gas monitoring sensors 426 may be fluidly connected to the breathing circuit 412 or any other component of the ventilator system 402. In some embodiments, the gas monitoring sensor 426 includes one or more of a pressure sensor, a flow sensor, and a gas concentration sensor. As will be described in more detail below, the controller 406 and the mechanical ventilator 408 can utilize the one or more gas monitoring sensors 426 to monitor, ultimately control, and provide information (e.g., feedback to a user (e.g., a clinician)) regarding the operation of the mechanical ventilator 408. The one or more gas monitoring sensors 426 can include, without limitation, any conventional gas sensor.

[0029] The EIT system 404 can include any of the EIT systems described above with reference to one or more of FIGS. 1-3 and can operate according to any of the above-described embodiments. The EIT system 404 can include, without limitation, conventional EIT systems. Additionally, as described above, the EIT system 404 may be operably coupled to the controller 406 and may provide information regarding measurements performed by the EIT system 404 to the controller 406. In some embodiments, the EIT system 404 is completely independent of the ventilator system 402. In one or more embodiments, the EIT system 404 also has a respective electronic display and input device separate from the display and / or input device of the ventilator system 402.

[0030] The controller 406 can include a processor 428 coupled to a memory 430 and input / output components 432. The processor 428 can comprise a microprocessor, a field programmable gate array, and / or other suitable logic device. The memory 430 can include volatile and / or non-volatile media (e.g., ROM, RAM, magnetic disk storage media, optical storage media, flash memory devices, and / or other suitable storage media), and / or other types of computer-readable storage media configured to store data. The memory 430 can store algorithms and / or instructions for operating the ventilator system 402 and the EIT system 404 to be executed by the processor 428. For example, the controller 406 may include the data processing system 320 described above with reference to FIG. 3. In some embodiments, the processor 428 is operably coupled to a computing device (e.g., via the Internet) operably coupled to the controller 406, such as a server or a personal computer, to transmit data thereto. The input / output components 432 can include a display, a touch screen, a keyboard, a mouse, and / or other suitable types of input / output devices configured to receive input from an operator and provide output to the operator.

[0031] Still referring to FIG. 4, as will be described in more detail below with reference to FIGS. 5 - 13, the system 400 may apply a level of PEEP to a patient when determining the potential mobilization value of the patient's vital capacity (e.g., depleted parenchyma) in the RAM using the mechanical ventilator system 402. PEEP increases the baseline pressure within the patient's respiratory system such that the patient's natural exhalation maintains a higher airway pressure than breathing without PEEP treatment. Conventional PEEP pressures range up to 40 cmH2O, although higher PEEP pressures can also be used. High PEEP refers to PEEP treatment applied above 10 cmH2O, more specifically between 10 - 30 cmH2O. Low PEEP refers to PEEP pressures below 10 cmH2O, often applied between 5 - 8 cmH2O.

[0032] In some embodiments, the effect of applying PEEP to a patient is measured by measuring the patient's lung volume in response to the application of PEEP. After PEEP application, the patient's lung volume is measured as the end expiratory lung volume (EELV) and is measured for a particular PEEP pressure applied to the patient. In some embodiments, the EELV is measured at 0 PEEP (zero PEEP, "ZEEP"). The measured value of EELV at ZEEP is referred to herein as the functional residual capacity (FRC) and is a measure of the amount of air remaining in the lungs at the end of a natural exhalation. In some embodiments, when using the EIT system 404 to measure and / or determine the EELV, the FRC may, in some cases, be considered to be 0 so that the FRC does not affect a particular calculation. Considering the above, the EELV is the FRC plus the amount of lung volume increased by the applied PEEP.

[0033] The increase in EELV associated with the application of PEEP is derived from two physiological sources. The first physiological cause of the volume increase results from the application of additional pressure to the lung tissue. By applying additional pressure to the lung tissue, the lungs and already opened alveoli expand (e.g., bulge due to the pressure inside the lungs), generating more lung air volume. Expanding the lungs poses a risk to the patient in the form of volume trauma that damages the lungs (i.e., local overexpansion of normal alveoli). Volume trauma can lead to medical complications in patients similar to Acute Respiratory Distress Syndrome (ARDS). The second physiological cause of the increased lung air volume is the "recruitment" of alveoli in a process known as "pop open," where the internal volume of one alveolus suddenly jumps from a zero volume (e.g., collapse) to the volume achieved by adjacent alveoli (e.g., adjacent units). As is known in the art, alveoli are air sacs in the lungs that facilitate gas exchange with the patient's blood. Some alveoli, particularly diseased or weakened alveoli, collapse when the pressure in the lungs is too low, and the alveoli (e.g., units) reach or reach a zero volume. Applying PEEP to the patient (e.g., applying PEEP therapy) can maintain the minimum airway pressure in the lungs and, in some cases, keep the alveoli (e.g., collapsed alveoli) open.

[0034] Alveoli can generate some gas exchange only when they are open. Thus, to maintain some gas exchange when PEEP is insufficient, a higher driving inspiratory pressure is required to hyperventilate the already open alveoli (e.g., units) to compensate for the lack of function of the closed alveoli (e.g., units). The increased respiratory effort causes a greater expansion of the remaining open alveoli, which can lead to further damage to the lung tissue. In contrast, when PEEP is sufficient, gas exchange is shared among most alveoli (e.g., units), which reduces the driving inspiratory pressure and, in some cases, reduces lung injury. Thus, alveolar recruitment also increases EELV, which corresponds to the extra volume generated by the pop - open of multiple alveolar units. The extra volume produced by the opened alveolar units is known as the volume gain of the lung at a specific pressure (or the vertical displacement in a pressure - volume plot). In a graph with two pressure - volume plots representing lung inflation, the first plot represents lung inflation without recruitment, the second plot represents the conditional inflation of the lung when alveoli (e.g., units) are opened from the start of inflation, and the vertical distance between the two curves (see Figures 7 and 8) represents the volume gain caused by recruitment.

[0035] FIG. 5 shows a flowchart representing a process for evaluating a respiratory cycle 500. Each process action will be described in further detail below with respect to FIGS. 6A-11. The process actions may be performed by a controller such as controller 406 (FIG. 4) using mechanical ventilation data received from a mechanical ventilator (e.g., mechanical ventilator 408 (FIG. 4)), a gas flow or gas pressure sensor (e.g., gas monitoring sensor 426 (FIG. 4)), and an EIT system (e.g., EIT system 404 (FIG. 4)). The data may include gas flow values over time (e.g., gas flow waveform), gas pressure values over time (e.g., gas pressure waveform), ventilator cycles, inspiration signals (e.g., inspiration event trigger or inspiration event trigger point), expiration signals (e.g., expiration event trigger or expiration event trigger point), EIT images, and baseline data (e.g., plethysmograph data, lung impedance values over time, lung volume values over time, lung volume waveform, plethysmograph, local lung volume values over time), etc.

[0036] A combination of a mechanical ventilator and a gas monitoring sensor may provide the gas flow waveform and the gas pressure waveform to the controller. The gas monitoring sensor may also provide an inspiration trigger and an expiration trigger. The inspiration trigger and the expiration trigger may be trigger points indicating when the patient switches from inhaling to exhaling, or from exhaling to inhaling. In some embodiments, the inspiration trigger and the expiration trigger are transmitted to the mechanical ventilator to trigger the mechanical ventilator to provide pressurized air to the patient (inspiration event), or to remove pressure and allow air to exit the patient's lungs, or to provide negative pressure and draw air out of the patient's lungs (expiration event). The gas monitoring sensor may determine that there is an inspiration trigger point or an expiration trigger point based on a sudden change in one or more of gas flow, gas pressure, or flow volume in the breathing circuit between the mechanical ventilator and the patient. The inspiration trigger and the expiration trigger are used to define a breath cycle. The breath cycle may start at the beginning of an inspiration event and end at the end of a subsequent expiration event, or at the transition from an expiration event to the next inspiration event.

[0037] The process of evaluating the breathing cycle 500 may be initiated by determining whether any trigger event (e.g., an inhalation trigger or an exhalation trigger) has been missed, such as if a cycle has been missed or mis-identified in operation 502. An asynchronous event between the mechanical ventilator and the patient can result in one or more trigger events being missed by the gas monitoring sensor. Thus, the process can be initiated by analyzing the gas pressure waveform and the gas flow waveform and identifying trigger events not included in the inhalation and exhalation triggers from the gas monitoring sensor. The identification of missed trigger events is described in more detail below with reference to FIGS. 6A and 6B.

[0038] After identifying the missed trigger event, the process of evaluating the breathing cycle 500 may search for and search for artifacts in operation 504. As used herein, an "artifact" is an event that would cause (e.g., result in, achieve) inaccurate readings such as sensor disconnection, changes in parameters (e.g., pressure, PEEP, flow, etc.), purges, aspiration, etc. If an artifact is detected, the associated breath cycle is labeled as an invalid cycle. This can substantially prevent the controller from mis-identifying asynchrony during an artifact event. The detection of artifacts is described in more detail below with reference to FIG. 7.

[0039] After identifying artifacts in operation 504, the process of evaluating the respiratory cycle 500 searches for asynchrony and retrieves data in operation 506. As used herein, "asynchrony" is a situation where the mechanical ventilator and the patient are not in sync. Asynchrony may include coupled cycles, double triggering, reverse triggering, breath stacking, breath stacking volume, trap volume, and / or stack volume. Asynchrony can be identified by comparing the lung volume data from the EIT system with the gas pressure waveform and gas flow waveform from the gas monitoring sensor. The detection of different asynchronies will be described in more detail below with reference to FIGS. 8-11.

[0040] After identifying artifacts in operation 504 and asynchrony in operation 506, the controller combines the artifacts and asynchrony in operation 508. The controller can display the artifacts and asynchrony as a report on a display such as the input / output component 432 or the electronic display 324. The controller can also generate a patient index that includes one or more of the number of asynchronies, the number of artifacts, the type of each asynchrony and artifact, the frequency of each occurrence, etc.

[0041] In some embodiments, the controller provides recommended values for parameter adjustment, such as adjustments to PEEP, sensitivity adjustment, etc., to the mechanical ventilator. In other embodiments, the controller sends a signal to the mechanical ventilator configured to adjust one or more parameters of the mechanical ventilator based on the asynchrony detected in operation 506. Different types of asynchrony can be corrected through different adjustments to the mechanical ventilator settings such as PEEP and plateau pressure, inspiratory time and expiratory time, ventilation mode, sensitivity level, and respiratory rate.

[0042] Figures 6A and 6B show pressure waveform 602 and flow waveform 610 that are temporally aligned. Figure 6A shows pressure waveform 602 and flow waveform 610 before analyzing pressure waveform 602 and flow waveform 610 for missed triggers, and Figure 6B shows pressure waveform 602 and flow waveform 610 after inserting the missed triggers.

[0043] Pressure waveform 602 includes peaks 606 corresponding to high pressure points and valleys 604 corresponding to low pressure points. Trigger point 608 detected by the gas monitoring sensor is also plotted along pressure waveform 602. The trigger points 608 shown in Figures 6A and 6B correspond to inspiration trigger points, which occur close to valley 604 of pressure waveform 602 due to a pressure drop caused by the patient's inhalation.

[0044] Flow waveform 610 also includes peaks 614 corresponding to high gas flow and valleys 612 corresponding to low gas flow. Trigger point 608 corresponding to the flexion trigger point is also plotted on flow waveform 610.

[0045] As described above, the gas monitoring sensor may not be able to recognize trigger points. In some cases, the missed trigger points correspond to asynchronies between the mechanical ventilator and the patient, such as double trigger events or reverse trigger events. To detect the missed triggers, the controller can apply waveform filtering to pressure waveform 602 and flow waveform 610. The waveform filtering may be a moving average filter that averages each pressure waveform 602 or flow waveform 610 at each point over a limited number of frames, such as within a range of about 2 frames to about 20 frames, about 2 frames to about 10 frames, or about 5 frames.

[0046] After filtering the waveform, the controller can identify potentially unrecognized trigger points. For example, the controller may identify potentially unrecognized trigger points where the flow rate value is greater than the threshold flow rate, the flow rate difference between two adjacent points on the flow rate waveform 610 is greater than the threshold amount, and the pressure difference between two adjacent points on 602 is greater than the threshold amount. The threshold flow rate value may be in the range of about 0.2 L / s to about 0.7 L / s, for example about 1.5e -1 L / s. The threshold flow rate difference between adjacent points on the flow rate waveform 610 may be in the range of about 0.0 L / s to about 0.1 L / s. The threshold pressure difference between adjacent points on the pressure waveform 602 may be in the range of about 0.8 cmH2O to about 1.1 cmH2O, such as about 2.6e -1 cmH2O.

[0047] The controller can then compare the potentially unrecognized trigger points with the trigger points 608 previously identified by the gas monitoring sensor. The controller can then remove potentially unrecognized trigger points that are close to the previously identified trigger points 608. For example, potentially unrecognized trigger points that occur within a short time from one of the previously identified trigger points 608 are likely to be the same trigger point. Thus, potentially unrecognized trigger points that occur within less than about 200 milliseconds (ms), such as less than about 100 ms from the previously identified trigger point 608, can be excluded from consideration. The controller may also remove potentially unrecognized trigger points that occur in temporal proximity to other potentially unrecognized trigger points. For example, potentially unrecognized trigger points that occur within about 200 ms, such as within about 100 ms of another unrecognized trigger point, can be excluded from consideration.

[0048] After comparing potentially unrecognized trigger points with previously recognized trigger point 608, the potentially unrecognized trigger points that remain can then be traced to the closest local minimum pressure (e.g., the closest valley 604 within pressure waveform 602). The rate of flow at each of the potentially unrecognized triggers can also be considered. For example, at a trigger point, the flow should change direction. Thus, the flow should be low at the trigger point. Accordingly, the controller can eliminate potentially unrecognized trigger points where the gas flow is greater than, for example, greater than about 0.1 L / s, such as greater than about 0.2 L / s. In some embodiments, the controller also considers the flow volume at potentially unrecognized trigger points. For example, the controller may eliminate potentially unrecognized trigger points where the flow volume is below an expected amount, such as less than about 1.2 milliliters per kilogram (mL / kg), or less than about 1 mL / kg.

[0049] Next, as shown in FIG. 6B, the remaining potentially unrecognized trigger points can be plotted on pressure waveform 602 and flow waveform 610 as missed trigger points 616.

[0050] The controller can similarly detect unrecognized exhalation trigger points corresponding to unrecognized inhalation trigger points. The controller can follow a process similar to the process used to detect unrecognized inhalation trigger points to detect unrecognized exhalation trigger points.

[0051] FIG. 7 shows a flowchart 700 representing a determination process for determining whether an identified cycle including both an inhalation trigger point and an exhalation trigger point is a valid cycle. As described above, artifacts such as sensor disconnection, changes in parameters (e.g., pressure, PEEP, flow rate, etc.), purge, and aspiration can cause incorrect readings. Therefore, the controller can determine whether an artifact is associated with a cycle and determine whether a portion of the pressure waveform 602, a portion of the flow waveform 610, and other data associated with the cycle are valid. If an artifact is detected, the associated breath cycle is labeled as an invalid cycle through the determination process shown in FIG. 7.

[0052] In determination 702, the controller can determine whether a recent purge has occurred. For example, the controller can determine whether a purge has occurred during the cycle in question or the immediately preceding cycle before the cycle in question. If the controller determines that a recent purge event has occurred, in operation 704, the cycle in question is determined to be invalid.

[0053] In determination 706, the controller can determine whether the sensor has been recently disconnected. For example, the controller can determine whether the sensor has been disconnected during the cycle in question or the immediately preceding cycle before the cycle in question. If the controller determines that the sensor has been recently disconnected, in operation 708, the cycle in question is determined to be invalid.

[0054] In determination 710, the controller can determine whether the patient is under mechanical ventilation support. For example, when the mechanical ventilator is operating at a higher pressure, the ventilator is breathing for the patient rather than assisting a breathing patient. For example, if the average pressure is greater than 4 cmH2O, the patient can be determined to be under mechanical ventilation support. When the ventilator is breathing for the patient, since the mechanical ventilator does not depend on signals from the patient to move through the breathing cycle, asynchrony does not occur. If the controller determines that the patient is under mechanical ventilation support, in operation 712, the cycle is determined to be invalid.

[0055] In determination 714, the controller can determine whether there is a leak in the system. The controller can monitor the gas flow rate for each cycle. The controller can analyze a plurality of cycles, such as within a range of about 4 cycles to about 16 cycles, or about 8 cycles to about 12 cycles. If the controller determines that a change in gas flow rate has occurred over a plurality of cycles, the controller can determine that a leak has been detected. If a leak is detected, in operation 716, the cycle is determined to be invalid.

[0056] In determination 718, the controller can determine whether the pressure in the system, such as the PEEP pressure, has recently changed. For example, the controller can determine whether the pressure in the system has changed during the cycle of interest or in a previous cycle. The previous cycle may include cycles within a range from the immediately previous cycle to 10 previous cycles, such as from the previous 2 cycles to the previous 8 cycles. If a pressure change has occurred in the previous cycle, in operation 720, the cycle is determined to be invalid.

[0057] If none of the artifacts enumerated and described above are detected, in operation 722, the cycle in question may be determined to be valid. The valid cycle may be further analyzed in subsequent operations of the process of evaluating the respiratory cycle 500 (Figure 5). The controller may then determine whether the cycle includes asynchronous events such as a coupled cycle, double trigger, reverse trigger, breath stacking, breath stacking volume, trap volume, or stack volume.

[0058] Figure 8 shows an exemplary diagram of a coupled cycle in a lung volume waveform 800. The lung volume waveform 800 can be provided by an EIT system such as the EIT system 404 (Figure 4). A coupled cycle occurs when a single patient effort triggers two subsequent respiratory cycles with a shorter expiratory time in between (double trigger), or when the first cycle is triggered by a ventilator (without muscle effort) and the second cycle is triggered by the patient's effort (reverse trigger). A coupled cycle can result in breath stacking. Breath stacking is an unintentional high tidal volume that jeopardizes low tidal volume (VT) protection strategies and increases the risk of lung injury. Breath stacking can also cause overinflation of the lungs and thus result in hypoxemia and hypercapnia.

[0059] The lung volume waveform 800 of Figure 8 includes a first peak 802 and a second peak 804 separated by an intermediate valley 806. The intermediate valley 806 represents the short expiratory time between two respiratory cycles, characterized by the first peak 802 and the second peak 804. The intermediate valley 806 may define a trap volume 808. The trap volume 808 is the amount of air in the lungs that was not exhaled during the short expiratory time. The second peak 804 defines a stack volume 810, which is the additional amount of air added to the lungs beyond what a conventional cycle would add. The stack volume 810 is the amount of air that can potentially damage the lungs.

[0060] Figure 9 shows a lung volume waveform 902, a pressure waveform 904, and a flow waveform 906 that indicate a double trigger event 908 and an inverse trigger event 910. The lung volume waveform 902 shows the change over time in the volume of a patient's lungs measured by an EIT system. The pressure waveform 904 shows the increase and decrease in pressure within the breathing circuit measured by a gas monitoring sensor or a mechanical ventilator. The flow waveform 906 shows the change in the flow rate within the breathing circuit as measured by a gas monitoring sensor or a mechanical ventilator. The flow waveform 906 also indicates the direction of flow; for example, a flow rate less than 0 is in the exhalation flow direction away from the patient, and a flow rate greater than 0 is in the inhalation flow direction towards the patient.

[0061] As described above, a double trigger event 908 occurs when a single patient effort triggers two successive breathing cycles from a mechanical ventilator with a shorter expiratory time between them. As shown, the lung volume waveform 902 includes a smaller first peak 912 and a larger second peak 914 separated by an intermediate valley 916 that represents the short expiratory time. The double trigger event 908 can result in a significant increase in lung volume compared to a conventional cycle. The pressure waveform 904 shows a larger first peak 922 and a smaller second peak 926 separated by an intermediate valley 924. The flow waveform 906 shows a first peak 934 and a second peak 936 separated by a valley 938, and the flow changes from an inhalation flow at the first peak 934 to an exhalation flow at the valley 938 and back to an inhalation flow at 936.

[0062] The reverse trigger event 910 occurs when the first cycle is triggered by the ventilator without the patient's effort and the second cycle is triggered by the patient's effort. As shown, the tidal volume waveform 902 includes a first peak 918 and a second peak 920, but there is no expiratory time between the two inspirations, so there is no intermediate valley. Similar to the double trigger event 908, the reverse trigger event 910 can result in a significant increase in tidal volume compared to a conventional cycle due to two inspirations without an intervening expiration. The pressure waveform 904 shows a smaller first peak 928 and a larger second peak 932 separated by an intermediate valley 930. The flow waveform 906 shows a first peak 940 and a second peak 944 separated by a valley 942. In the reverse trigger event 910, the flow does not change from an inspiratory flow to an expiratory flow. Instead, the flow velocity decreases at the valley 942 but continues in the inspiratory direction.

[0063] To detect different asynchronous events, the controller may start by applying filters to the tidal volume waveform 902, the pressure waveform 904, and the flow waveform 906. The pressure waveform 904 and the flow waveform 906 can be filtered through a moving average filter that can average the data of the respective waveforms 904, 906 over a plurality of frames, such as within a range of 5 frames to about 20 frames or about 10 frames. The tidal volume waveform 902 can be filtered using a high-pass filter, such as a high-pass Butterworth filter cutoff frequency within a range of about 0.01 Hz to about 0.03 Hz, such as about 0.02 Hz.

[0064] After filtering the lung volume waveform 902, the pressure waveform 904, and the flow waveform 906, the controller can analyze the characteristics of each cycle within waveforms 902, 904, 906 to determine whether a combined cycle has occurred. For example, the controller can determine the trapped volume 808 during each cycle. If the trapped volume 808 is greater than a threshold volume, the cycle can be identified for further analysis. The threshold volume may be in the range of about 0.5 mL / kg to about 2 mL / kg, for example, 1 mL / kg. The controller may also determine whether the expiratory time between two cycles, such as a flagged cycle, is less than a threshold time. The threshold time may be in the range of about 0.5 s to about 2 s, for example, about 1 s. The controller may also compare the expiratory time between two cycles with the previous expiratory time. For example, the controller can calculate a value G using the following equation.

[0065] [Number]

[0066] wherein [Number] is the trimmed average of several past cycles, such as the past 10 cycles, the past 15 cycles, or the past 20 cycles. If the calculated value G is greater than a threshold, the controller determines that the two associated cycles are a combined cycle. The threshold may be in the range of about 0.3 to about 0.8, for example, about 0.5. One cycle or two cycles that meet the above threshold are defined as asynchronous cycles.

[0067] Once an asynchronous cycle is identified, the controller can classify the type of asynchrony. For example, the distinction between whether the cycle is a double trigger or a reverse trigger depends on the nature of the first cycle: if the first cycle is triggered by the patient's muscle movement, it is double-triggered. If the first cycle is triggered by a ventilator, it is labeled as a reverse trigger.

[0068] The controller can determine an estimated value of the patient's muscle movement by the following formula.

[0069]

Number

[0070] In the formula,

Number

[0071] A rough estimated value of muscle movement is used to evaluate the grade G. First, a window (W) is defined using the following formula. W = {P est (t i ) | t i ∈[t ins - 0.2s, t ins + 0.2s]} G is estimated using the following relationship. f(W) = t ins - 20, then G = 0, where t0 is the trigger point. f(W) ≠ t ins - 20, then G = max(W) - min(W1), where W1 = {P est (t i ) | t i ∈[t ins - 0.2, f(W)]}

[0072] After G is estimated through the above relationship, G can be used to determine which type of asynchronous event is associated with the cycle in question. For example, if G is greater than a threshold value, the controller may determine that the event was triggered by the patient and is a double trigger. On the other hand, if G is less than the threshold value, the controller may determine that the event was triggered by the ventilator and is an inverse trigger. For example, the threshold value may be set to 1.38. Thus, if G is 1.38 or greater, the second cycle is determined to be double-trigger asynchrony. On the other hand, if G is less than 1.38, the cycle is determined to be an inverse trigger.

[0073] Breath stacking is identified when the trapped volume 808 (FIG. 8) and the stacked volume 810 (FIG. 8) are greater than a threshold value. The threshold value can be set to a value approximating a conventional change in breath volume, such as in the range of about 0.8 mL / kg to about 1.5 mL / kg, or about 1 mL / kg. Thus, if both the trapped volume 808 and the stacked volume 810 are greater than the threshold value, the controller determines that the asynchronous event includes breath stacking.

[0074] One asynchronous event can include more than one asynchrony. For example, a double-trigger event can also include a breath-stacking event caused by double triggering.

[0075] In some cases, the asynchronous event affects only the patient's lung region. For example, as shown in FIG. 10A, the asynchronous event may overinflate one region of the patient's lung but not the other regions. Thus, monitoring the global flow volume to the patient's lungs may not capture the event, while monitoring the regional volume may provide additional data for determining whether such a condition exists.

[0076] As shown in FIG. 10A, pixel data from the EIT image 1002 can provide data regarding volumes in the anterior region 1004, posterior region 1006, right region 1008, and left region 1010 of the patient's lung. The pixel data can facilitate defining a region of the patient's lung as the overinflated region 1012 and another region of the patient's lung as the underinflated region 1014. The pixel data can also facilitate determining the locations of the overinflated region 1012 and the underinflated region 1014. The pixel data can be used in one or more of the processes described above to detect asynchrony. For example, the elongation of a region can be associated with a reverse trigger asynchronous event. Thus, the controller can use the pixel data for both detecting asynchronous events and classifying asynchronous events.

[0077] Embodiments of the present disclosure provide tools that can provide additional information to a physician regarding a mechanically ventilated patient. The information may be used to reduce and / or prevent asynchrony between the mechanical ventilator and the patient, which can reduce the risk of ventilator-related injury. By evaluating the information in the manner described herein, embodiments of the present disclosure can detect asynchronies that might otherwise be missed by a careful physician. Further, embodiments of the present disclosure can substitute for an experienced physician watching a monitor by detecting asynchronies and warning the attending physician when an asynchrony is detected, which can reduce the cost to the hospital as well as the workload on the attending physician and staff.

[0078] Non-limiting, exemplary embodiments of the present disclosure can include the following. Embodiment 1: A system configured to detect asynchronous breathing events, comprising a receiver configured to receive ventilation data from at least one of a mechanical ventilator, an airway flow sensor, or an airway pressure sensor, and to receive impedance data from an electrical impedance tomography device, a processor, a memory device configured to store the ventilation data and the impedance data, and a non-transitory computer-readable medium storing instructions, which when executed by the processor cause the processor to extract a plethysmogram from the impedance data, extract at least one of a flow waveform and a pressure waveform from the ventilation data, compare the plethysmogram with at least one of the flow waveform and the pressure waveform, determine whether an asynchronous event has occurred based on comparing the plethysmogram with at least one of the flow waveform and the pressure waveform, classify the asynchronous event, and provide the classification of the asynchronous event.

[0079] Embodiment 2: The system of Embodiment 1, wherein the instructions cause the processor to determine a trapped volume during a cycle, determine whether an expiratory time between two cycles is less than a threshold time, and compare the expiratory time between two cycles with a previous expiratory time.

[0080] Embodiment 3: The system of Embodiment 2, wherein the instructions cause the processor to determine whether an asynchronous event has occurred when the trapped volume is greater than a threshold volume.

[0081] Embodiment 4: The system of Embodiment 3, wherein the threshold volume is in the range of about 0.5 mL / kg to about 2 mL / kg.

[0082] Embodiment 5: The system of Embodiment 2 or 3, wherein the instructions cause the processor to determine that an asynchronous event has occurred when the stack volume during a cycle is greater than a threshold volume, the expiratory time between two cycles is less than a threshold time, and the expiratory time between two cycles is shorter than a previous expiratory time.

[0083] Embodiment 6: A system according to any one of Embodiments 2 to 5, wherein the threshold time is in the range of about 0.5 seconds to about 2 seconds.

[0084] Embodiment 7: A system according to any one of Embodiments 2 to 6, wherein the previous expiratory time includes the expiratory times from at least four previous cycles.

[0085] Embodiment 8: A system according to any one of Embodiments 1 to 7, wherein the instruction causes the processor to communicate the recommended adjustment to the user.

[0086] Embodiment 9: A system according to any one of Embodiments 1 to 8, wherein the instruction causes the processor to send a signal providing the adjustment to the mechanical ventilator.

[0087] Embodiment 10: A system according to any one of Embodiments 1 to 9, wherein the instruction causes the processor to determine whether the respiratory cycle is valid by determining that the artifact did not affect the respiratory cycle.

[0088] Embodiment 11: The system of Embodiment 10, wherein the artifact includes one of sensor disconnection, change in PEEP, purge, and leak.

[0089] Embodiment 12: A method for detecting asynchronous respiratory events, comprising extracting an impedance data waveform from an electrical impedance tomography device, extracting one or more of a flow waveform and a pressure waveform, aligning the impedance data waveform with one or more of the flow waveform and the pressure waveform with respect to time, comparing the impedance data waveform with one or more of the flow waveform and the pressure waveform, determining whether an asynchronous respiratory event has occurred based on comparing the impedance data waveform with one or more of the flow waveform and the pressure waveform, classifying the asynchronous respiratory event, and providing information for identifying the asynchronous respiratory event.

[0090] Embodiment 13: The method of Embodiment 12, wherein classifying an asynchronous breathing event includes determining whether the asynchronous breathing event is a double trigger event or a reverse trigger event.

[0091] Embodiment 14: The method of Embodiment 12 or 13, wherein classifying an asynchronous breathing event includes determining whether the asynchronous breathing event is breath stacking.

[0092] Embodiment 15: The method according to any one of Embodiments 12 to 14, further including estimating a moment of a patient's muscle movement.

[0093] Embodiment 16: The method of Embodiment 15, wherein classifying an asynchronous breathing event includes determining whether a moment of a patient's muscle movement triggered a first cycle of the asynchronous breathing event.

[0094] A method for detecting an asynchronous breathing event, comprising: extracting lung impedance data from an electrical impedance tomography device including a plethysmogram from at least one region of interest; extracting one or more of a flow waveform and a pressure waveform; comparing the impedance data with one or more of the flow waveform and the pressure waveform; identifying a combined cycle based on at least the impedance data, the flow waveform, and the pressure waveform; classifying the combined cycle; and providing a recommended adjustment for a mechanical ventilator.

[0095] Embodiment 18: The method of Embodiment 17, wherein classifying the combined cycle includes determining at least one of a trapped volume or a stack volume based on the impedance data.

[0096] Embodiment 19: The method of Embodiment 17 or 18, wherein identifying the combined cycle includes determining whether a region of a patient's lung is overinflated.

[0097] Embodiment 20: The method of Embodiment 19, wherein determining whether a region of a patient's lung is overinflated includes comparing the inflation of one cycle with the inflation of at least four previous cycles.

[0098] The embodiments of the present disclosure described above and shown in the accompanying drawings do not limit the scope of the present invention. This is because these embodiments are merely examples of embodiments of the present invention defined by the appended claims and their legal equivalents. Any equivalent embodiments are intended to be within the scope of the present disclosure. In fact, various modifications of the present disclosure, such as alternative useful combinations of the described elements, may become apparent to those skilled in the art from the description. Such modifications and embodiments are also intended to fall within the scope of the appended claims and their legal equivalents.

Description of Reference Numerals

[0099] 100 Electrical Impedance Tomography (EIT) system 103 Electrode belt 104 Patient's cranio-caudal axis 105 Patient 110 Electrode 300 EIT system 310 Electrode belt 320 Data processing system 322 Processor 324 Electronic display 326 Input device 328 Memory device 400 System 402 Ventilator system 404 EIT system 406 Controller 408 Mechanical ventilator 410 Patient 412 Breathing circuit 414 Inspiratory limb 416 Patient limb 418 Patient connection 420 Inspiratory port 422 Expiratory rim 424 Expiratory port 426 Gas monitoring sensor 428 Processor 430 Memory 432 Input / output component

Claims

1. A system configured to detect asynchronous breathing events, comprising: a receiver configured to receive ventilation data from at least one of a mechanical ventilator, an airway flow sensor, or an airway pressure sensor, and to receive impedance data from an electrical impedance tomography device; a processor; a memory device configured to store the ventilation data and the impedance data; a non-transitory computer-readable medium storing instructions, wherein the instructions, when executed by the processor, cause the processor to: extract a plethysmograph from the impedance data; extract at least one of a flow waveform and a pressure waveform from the ventilation data; compare the plethysmograph with the at least one of the flow waveform and the pressure waveform; determine whether an asynchronous event has occurred based on comparing the plethysmograph with the at least one of the flow waveform and the pressure waveform; classify the asynchronous event; and provide the classification of the asynchronous event.

2. The instructions further cause the processor to: determine a trapped volume during a cycle; determine whether an expiratory time between two cycles is less than a threshold time; and compare the expiratory time between the two cycles with a previous expiratory time.

3. The instructions further cause the processor to determine whether the asynchronous event has occurred when the trapped volume is greater than a threshold volume.

4. The threshold volume is in the range of about 0.5 mL / kg to about 2 mL / kg.

5. The instructions further cause the processor to determine that the asynchronous event has occurred when: a stack volume during a cycle is greater than the threshold volume; the expiratory time between the two cycles is less than the threshold time; and the expiratory time between the two cycles is shorter than a previous expiratory time.

6. The threshold time is in the range of about 0.5 seconds to about 2 seconds.

7. ​ The system according to any one of claims 2 to 5, wherein the previous exhalation time includes exhalation times from at least four previous cycles. **Claim 8** The command causes the processor to communicate to the user the recommended adjustment, the system according to any one of claims 1 to 5. **Claim 9** The command causes the processor to send a signal providing an adjustment to the mechanical ventilator, the system according to any one of claims 1 to 5. **Claim 10** The command causes the processor to determine whether the respiratory cycle is valid by determining that the artifact did not affect the respiratory cycle, the system according to any one of claims 1 to 5. **Claim 11** The system according to claim 10, wherein the artifact includes one of sensor disconnection, change in PEEP, purge, and leak. **Claim 12** A method for detecting an asynchronous breathing event, comprising: retrieving an impedance data waveform from an electrical impedance tomography device; retrieving one or more of a flow waveform and a pressure waveform; aligning the impedance data waveform with the one or more of the flow waveform and the pressure waveform with respect to time; comparing the impedance data waveform with the one or more of the flow waveform and the pressure waveform; determining whether an asynchronous breathing event has occurred based on comparing the impedance data waveform with the one or more of the flow waveform and the pressure waveform; classifying the asynchronous breathing event; and providing information identifying the asynchronous breathing event. **Claim 13** Classifying the asynchronous breathing event includes determining whether the asynchronous breathing event is a double trigger event or a reverse trigger event, the method according to claim 12. **Claim 14** Classifying the asynchronous breathing event includes determining whether the asynchronous breathing event is breath stacking, the method according to claim 12. **Claim 15** The method according to any one of claims 12 to 14, further comprising estimating a moment of muscle movement of a patient. **Claim 16** Classifying the asynchronous breathing event includes The method of claim 15, comprising determining whether the instant of the muscle movement of the patient triggered a first cycle of the asynchronous breathing event.

17. A method for detecting an asynchronous breathing event, comprising: retrieving lung impedance data from an electrical impedance tomography device including a plethysmogram from at least one region of interest; retrieving one or more of a flow waveform and a pressure waveform; comparing the impedance data with the one or more of the flow waveform and the pressure waveform; identifying a combined cycle based on at least the impedance data, the flow waveform, and the pressure waveform; classifying the combined cycle; providing a recommended adjustment for a mechanical ventilator.

18. Classifying the combined cycle comprises: The method of claim 17, comprising determining at least one of a trapped volume or a stack volume based on the impedance data.

19. Identifying a combined cycle comprises: The method of claim 17 or 18, comprising determining whether a region of the patient's lung is overinflated.

20. Determining whether a region of the patient's lung is overinflated comprises: The method of claim 19, comprising comparing the inflation of one cycle with the inflation of at least four previous cycles.

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