Model-based lung fluid status detection techniques

Abstract

One embodiment is a method of performing a chest tomography on a human subject, comprising taking a plurality of 4-wire impedance measurements on a region of interest to obtain measured impedance data; comparing the measured impedance data to simulated impedance data obtained from a plurality of models of the region of interest; for each of the models, determining a fit of the model based on a comparison between the simulated impedance data obtained from the model and the measured impedance data; and integrating individual resistivity estimates obtained from the models based on the fit of the models, such that individual resistivity estimates from better fitting models are more heavily weighted in a final resistivity estimate than individual resistivity estimates from worse fitting models.

Description

[0001] Related applications

[0002] This disclosure claims priority to U.S. Provisional Patent Application No. 63 / 124206, entitled “Pattern-Based Detection of Lung Fluid State”, filed December 11, 2020, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] This disclosure generally relates to the field of lung fluid tomography, and more specifically, to a model-based technique for detecting lung fluid state using multiple impedance measurements combined with prior knowledge of the region of interest. Attached Figure Description

[0004] To provide a more complete understanding of this disclosure and its features and advantages, reference is made to the following description in conjunction with the accompanying drawings, wherein like reference numerals denote like parts, wherein:

[0005] Figure 1 An example environment for an illustrative system for model-based lung fluid state detection according to some embodiments of the present disclosure is shown;

[0006] Figure 2 This illustrates some embodiments according to the present disclosure. Figure 1 A block diagram of exemplary functional components of the system;

[0007] Figure 3 The operation of a 4-wire impedance measurement system according to one embodiment is shown;

[0008] Figure 4 An example electrical impedance tomography technique for detecting lung fluid state is shown, in which multiple 4-wire impedance measurements are performed using multiple (e.g., 8 or more) electrodes dispersed around the region of interest to obtain a resistivity estimation map without using any prior knowledge of the region of interest;

[0009] Figure 5 It shows a cross-sectional model of the human upper body, displaying various types of tissues (lungs, heart, bones, and soft tissues) and air;

[0010] Figure 6 This is a flowchart illustrating the operation of a model-based fluid state detection technique using a single model of a region of interest;

[0011] Figure 7A and 7B An example embodiment of the model-based lung fluid state detection system described herein is shown, which uses multiple impedance measurements combined with prior knowledge of the region of interest.

[0012] Figure 8A and8B Another example embodiment described herein is illustrated for implementing a model-based lung fluid state detection system using a combination of multiple impedance measurements and prior knowledge of the region of interest; and

[0013] Figure 9 A schematic block diagram of a system for measuring chest impedance in human subjects is shown. Detailed Implementation

[0014] Lung resistivity is a physiological parameter that describes the electrical properties of the lungs. Lung composition changes due to variations in lung tissue, fluid, and air volume. Various diseases that can cause changes in lung composition can be monitored by measuring lung resistivity. Lung fluid state is a change in lung composition that can be monitored by measuring lung resistivity.

[0015] In some embodiments, the chest impedance (Z) measurement system is an inexpensive, non-invasive system for assessing lung resistivity and thus lung fluid status, and is provided in a wearable form for home use and monitoring. In some embodiments, chest impedance measurements can be performed using four electrodes.

[0016] Figure 1 Example environment 100 is depicted, illustrating an illustrative embodiment of a system 102 for performing model-based lung fluid state detection in human subjects, according to some embodiments of this disclosure. Monitoring can be performed in a continuous or periodic manner. Figure 1 As shown, according to an exemplary embodiment, system 102 includes a 4-wire chest impedance measurement module 112 and a plurality of surface electrodes / sensors 114a-114d (e.g., four (4) surface electrodes or sensors, or any other suitable number of surface electrodes / sensors). For example, one or more surface electrodes may be implemented as solid gel surface electrodes, or any other suitable surface electrodes. System 102 may be configured as a generally triangular device, or any other suitable shape, operable to contact one or more of the torso, upper chest, and neck regions, or any other suitable part or region of the body, of a human subject 104 via at least the plurality of surface electrodes / sensors 114a-114d.

[0017] In various implementations, system 102 may be configured to allow it to be implemented as multiple patch-like devices or any other suitable structure or device within a wearable vest-like structure. In a possible environment, such as environment 100, system 102 may operate for bidirectional communication with smartphone 106 via wireless communication path 116, and smartphone 106 may in turn operate for bidirectional communication with communication network 108 (e.g., the Internet) via wireless communication path 118. Alternatively, a direct link to cloud 110 may be provided without hopping through base stations or cellular phones. Smartphone 106 may also operate via communication network 108 to bidirectionally communicate with cloud 110 via wireless communication path 120, which may include resources for cloud computing, data processing, data analytics, data trending, data reduction, data fusion, data storage, and other functions. System 102 may also operate for direct bidirectional communication with cloud 110 via wireless communication path 122.

[0018] Figure 2 Example block diagrams of a system 102 for performing model-based lung fluid state detection in human subjects, according to some embodiments of the present disclosure, are depicted. Figure 2 As shown, the system includes a chest impedance measurement module 112, a processor 202 and associated memory 208, a data storage 206 for storing chest impedance, lung resistivity, model and related data, and a transmitter / receiver 204. The transmitter / receiver 204 can be configured to perform Bluetooth communication, Wi-Fi communication or any other suitable short-range communication for communication with a smartphone 106 via wireless communication path 116. Figure 1 The transmitter / receiver 204 can also be configured to perform cellular communication or any other suitable long-range communication for communication with the cloud 110 via wireless communication path 122. Figure 1 ) to communicate. In some embodiments, the chest impedance measurement module 112 may also include an electrode / sensor connection switching circuit 224 for communicating with Figure 1 The multiple surface electrodes / sensors 114a-114d shown can be switched and connected.

[0019] Processor 202 may include multiple processing modules, such as data analyzer 226 and data fusion / decision engine 228. Transmitter / receiver 204 may include at least one antenna 210 operable to transmit / receive wireless signals (e.g., Bluetooth or Wi-Fi signals) to / from smartphone 106 via wireless communication path 116, smartphone 106 being a Bluetooth or Wi-Fi enabled smartphone or any other suitable smartphone. Antenna 210 may also be operable to transmit / receive wireless signals, such as cellular signals, to / from cloud 110 via wireless communication path 122.

[0020] Processor 202 may also include lung fluid state detection module 234 for performing model-based lung fluid state detection in human subjects according to the embodiments described herein and in more detail below. It will be appreciated that all or part of processor 202, and modules shown as forming part of processor 202 (e.g., part or all of lung fluid state detection module 234), may additionally and / or alternatively be implemented in cloud 110. Figure 1 It can, in fact, include multiple processors and / or processing elements for implementing the techniques described herein. It will be further appreciated that... Figure 2 Other components that constitute part of system 102 shown may additionally and / or alternatively be located in cloud 110. Figure 1 In some embodiments, processor 202 may include a controller for controlling the operation of the measurement module / circuit.

[0021] The transmitter / receiver 204 may include at least one antenna 210 operable to transmit wireless signals (e.g., Bluetooth or Wi-Fi signals) to / from a smartphone 106 via wireless communication path 116. The smartphone 106 may be a Bluetooth or Wi-Fi enabled smartphone or any other suitable smartphone. The antenna 210 may also be operable to send wireless signals, such as cellular signals, to / from a cloud 110 via wireless communication path 122.

[0022] Please refer to the following illustrative examples and Figure 1 and Figure 2Further understanding is needed regarding the operation of a system 102 for performing model-based lung fluid state detection in human subjects according to some embodiments. In this illustrative example, while a human subject 104 is in a supine or upright position, at a fixed time each day (e.g., twice daily) or for a predetermined number of consecutive days, the human subject or human assistant positions the system 102, which is configured as a generally triangular device (or any other suitable shape), such that it contacts one or more of the subject's torso and upper chest and neck regions (or any other suitable part or region of the body) via a plurality of surface electrodes / sensors 114a-114d.

[0023] After positioning the system 102 in contact with the torso and / or upper chest and / or neck region of a human subject, the 4-wire chest impedance measurement module 112 can be activated to collect, gather, sense, measure or otherwise acquire chest impedance data from the human subject 104 and generate signals indicating such data.

[0024] The 4-wire chest impedance measurement module 112 can perform chest impedance measurements using some or all of a plurality of surface electrodes 114a-114d, which are in contact with the skin of a human subject 104 on his torso, upper chest and / or neck region.

[0025] In some embodiments, chest impedance data from the chest impedance measurement module 112 can be provided to the data analyzer 226 for at least partial data analysis, data trend analysis, and / or data reduction. In one embodiment, chest impedance measurement data, combined with other metadata such as medical history, demographic information, and other test patterns, can also be analyzed, trended, and / or reduced "in the cloud," and preset alerts can be provided in the cloud-based data storage 110 for clinical interventions at various levels regarding respiratory parameters.

[0026] Data analyzer 226 can provide at least partially analyzed chest impedance data to data fusion / decision engine 228, which can efficiently fuse or combine the chest impedance data with other sensed data at least partially according to one or more algorithms and / or decision criteria for subsequent use in making one or more inferences about human subject 104. Processor 202 can then provide the at least partially combined chest impedance and other sensed data to transmitter / receiver 204, which can transmit the combined chest impedance and sensed data directly to cloud 110 via wireless communication path 122 or to smartphone 106 via wireless communication path 116. Next, smartphone 106 can transmit the combined chest impedance and sensed data to cloud 110 via wireless communication paths 118, 120 through communication network 108, where it can be further analyzed, trended, reduced, and / or fused. It will be appreciated that, as described above, communication data can be transmitted directly to cloud 110 without involving smartphones / cellular phones or base stations.

[0027] Hospital clinicians can then remotely download the resulting carefully curated combination of sensor data for risk scoring / stratification, monitoring, and / or tracking purposes.

[0028] Figure 3 The operation of a 4-wire chest impedance measurement system 300 according to one embodiment is shown. Figure 3 As shown, the system includes four electrodes 302A-302D. In operation, an alternating current with a fixed excitation frequency I is injected from one of the four electrodes (e.g., electrode 302A) to another of the four electrodes (e.g., electrode 302B) through a region of interest 304 (e.g., a lung), and the voltage difference V between the remaining two electrodes (e.g., electrodes 302C and 302D) is measured. The impedance (Z) can be determined from these values ​​(i.e., Z = V / i). Changes in impedance indicate changes in the region of interest 304.

[0029] As will be described in more detail, the technique can also be performed using measurements at multiple excitation frequencies, whereas, in contrast to results from only a single excitation frequency, the ultimately clinically useful information is derived from a combination of results from multiple excitation frequencies.

[0030] Four-wire chest impedance measurement systems, such as System 300, are subject to certain limitations. For example, when the region of interest 304 has high resistivity, such as in the presence of lungs, the calculated impedance Z is insensitive to resistivity changes and is specific. Furthermore, minute changes in lung fluid state can easily be masked by other factors.

[0031] Now for reference Figure 4The image illustrates an electrical impedance tomography technique 400 for detecting pulmonary fluid state, in which multiple four-wire impedance measurements are performed using multiple (e.g., eight or more) electrodes 402A-402H dispersed around a region of interest 404 to obtain a resistivity estimation map without using any prior knowledge of the region of interest. Figure 4 As shown, region of interest 404 represents the human upper body (or chest), including the individual's lungs 406 and heart 408.

[0032] Based on the features of the embodiments described herein, a model-based fluid state detection technique is performed using a limited number of electrodes (e.g., 5-6) combined with prior knowledge of the region of interest or domain, performing multiple 4-wire impedance measurements to extract resistivity (ρ) including the lung region. lung The technique described in this paper is more sensitive and specific to changes in lung fluid than a single 4-line impedance measurement technique, while being less computationally intensive and less complex than traditional electrical impedance tomography.

[0033] Figure 5 A cross-sectional model 500 of the upper human body (or chest) is shown, displaying various types of tissues, including lungs 502, heart 504, bones 506, and soft tissues 508. Lungs 502 are filled with air. An example arrangement of electrodes 510A-510E is also shown. Figure 5 As shown. Lung resistivity (or lung fluid state) can be estimated within a single model, for example... Figure 5 The model shown is 500.

[0034] Figure 6 This is a flowchart illustrating the operation of a model-based fluid state detection technique using a single model. In step 600, multiple (e.g., 5-6 (preferably less than 8)) electrodes are used to perform N 4-wire measurements to generate a set of measurement impedance data Z for the region or area of ​​interest. meas =Z1,Z2,…. In step 602, a model of the domain of interest (e.g., Figure 5 The model 500 shown is used for simulation measurements. The model parameters (x), typically composed of ρ... lung Composed of continuous variables, including those included, the analog impedance measurement value Z is determined. sim (x)=[Z sim1 Z sim2 ,…Z simN In step 604, a selected portion or all of the measured impedance data is compared with a selected portion or all of the simulated impedance data. In step 606, lung resistivity estimation is performed through an optimization process, wherein a set of x, including ρ, that minimizes the cost function is identified. lung For example, such as, but not limited to, the following:

[0035]

[0036] Solving an inverse problem for estimating lung resistivity, as described above, presents various challenges. For example, the measurement conditions are likely to differ substantially from those assumed in the simulation model (e.g., electrode placement, lung size, tissue placement). Consequently, the final results may be overly sensitive to factors beyond the state of the lung fluid.

[0037] Based on the features of the embodiments described herein, and to ensure that the final results are sensitive and specific to changes in lung fluid rather than other factors, multiple inverse problems are addressed, and the results are summarized based on the "fit" between the measurement data and each problem. Reference now. Figure 7A In one implementation, the measured impedance data Z of the region of interest (e.g., human chest or upper body) at frequency f is... meas 700 and the simulated impedance data Z obtained from each of the M sample models 702(1)–702(M) sim Comparisons are made. Each of the M sample models 702(1)-702(M) represents a different possible combination of electrode placement and specific anatomical features (e.g., relative size and location of lung tissue, heart tissue, soft tissue, and bone) representing the region of interest. Based on the comparison of measured impedance data with simulated impedance data from the models 704(1)-704(M), a “fit” can be determined for each of the M models. Based on the “fit” between the measured data and the corresponding model, a fitting is determined for each model (X). est,1 –X est,N The estimated model parameters (including the model's individual resistivity estimates (ρ)) est,1 –ρ est,N )) to integrate or summarize 706, in order to develop the final model parameters (X) est ), including the final resistivity estimate (ρ est ), 708, which makes the estimates from the better "fitting" model more heavily weighted in the final estimate than those from the worse "fitting" model. For example, the final resistivity estimate could be a weighted average of the individual estimates, where the weights (W1–W N ) is assigned to model i, for example, but not limited to:

[0038] 1 / f residual,cost,i

[0039] Where f residual,cost It is the residual cost function value used to solve the inverse problem. Figure 7A In the embodiments shown, a measurement is understood as a number of possible placement and anatomical geometries with their own possibilities or weights.

[0040] It should be noted that chest impedance measurements may vary depending on the excitation frequency used to perform the measurement, due to potential variations in current flow within the region of interest. For example, at the first excitation frequency, the measurement may be more sensitive to changes in soft tissue, while at different excitation frequencies, the measurement may be more sensitive to changes in lung tissue.

[0041] According to various aspects of the embodiments described herein, such as Figure 7B As shown, Figure 7A The technique shown can be applied to multiple frequencies f1-f m The impedance data at each frequency is measured and estimated to generate the measured impedance data Z. meas (f1)–Z meas (f m and estimated (or simulated) impedance data Z est (f1)–Z est (f m ). By Z meas (f1)–Z meas (f m Fitting to their respective analog impedance measurements Z est (f1)–Z est (f m The results are combined to develop the final model parameters (Xest), including the final resistivity estimate (ρest), such that estimates from the better-fitting model are weighted more heavily in the final estimate than those from the worse-fitting model across the entire frequency range. These final parameters are used to generate final clinical insights about variations in the region of interest.

[0042] Figure 8A An alternative implementation is shown, wherein M models 702(1)-702(M) are “summarized” (by the average of the model weighted sums) 800 into a single-sample model 802, which is considered to best represent the actual measurement conditions 700. The resulting single-sample model 802 is used to generate estimated impedance data (Z). est The impedance data "fits" 804 to the impedance data Z. meas To generate the final model parameters X est This includes the final resistivity estimate ρ est And the weights W used for the weighted summation of the M sample models to produce the single sample model 806. i .

[0043] According to various aspects of the embodiments described herein, such as Figure 8B As shown, Figure 8A The technique shown can be applied to multiple excitation frequencies f1–f m Execute to generate model parameters X est (f1)-Xest (f m ), including the final resistivity estimate ρ estf1 -ρ estfm and weight W if1 -W ifm The obtained parameters are combined and used to generate final clinical insights about changes in the region of interest.

[0044] Figure 9 The measured impedance data Z used to obtain the patient's region of interest (e.g., lung) as described above are shown. meas The example system 900 is shown in the block diagram. Figure 9 As shown, an elastic chest strap 902 with electrodes attached is connected to the chest of the subject 904. In the illustrated embodiment, three electrodes may be placed near the center of the subject's chest 904, and three may be placed on the left side of the subject. A microcontroller unit (MCU) 906 controls the measurement sequence under the control of a computer (PC) 908. Specifically, the MCU 906 changes the switch configuration 910 to perform different 4-wire impedance tests using a 4-wire impedance measurement module 912, which uses six electrodes (in the illustrated embodiment) placed on the chest of the subject 904.

[0045] It will be recognized that, reference Figure 9 Some or all of the processes, modules and / or devices shown and described may be used with reference to Figure 1 and / or Figure 2 This is achieved through some or all of the processes, modules, and / or devices shown and described, or vice versa.

[0046] Example 1 provides a method for detecting the pulmonary fluid state of a human subject, the method comprising: performing multiple impedance measurements on a region of interest to obtain measured impedance data; comparing the measured impedance data with simulated impedance data obtained from multiple models of the region of interest; for each of the models, determining a fit of the model based on the comparison between the simulated impedance data obtained from the model and the measured impedance data; and integrating individual resistivity estimates obtained from the models based on the fit of the models such that individual resistivity estimates from better-fitting models are more heavily weighted in the final resistivity estimate than individual resistivity estimates from worse-fitting models.

[0047] Example 2 provides the method of Example 1, wherein performing multiple impedance measurements includes performing multiple 4-wire impedance measurements.

[0048] Example 3 provides the method of Example 2, wherein up to eight electrodes are used to perform the plurality of 4-wire impedance measurements.

[0049] Example 4 provides a method from any of Examples 1-3, where each of the models represents a different possible combination of electrode placement and specific anatomical features of the human subject.

[0050] Example 5 provides the method of Example 4, wherein the specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

[0051] Example 6 provides a method from any of Examples 1-3, wherein the final resistivity estimate is a weighted average of the individual resistivity estimates.

[0052] Example 7 provides the method of Example 6, where the weights assigned to model N are determined by 1 / f residual,cost,N Define f residual,cost It is the residual cost function value used to solve the inverse problem.

[0053] Example 8 provides a method from any of Examples 1-3, further comprising developing a single-sample model using a weighted sum of the plurality of models based on a corresponding fit of the model.

[0054] Example 9 provides a method for any of Examples 1-3, wherein the multiple impedance measurements are performed at a single excitation frequency.

[0055] Example 10 provides a method of any of Examples 1-3, wherein the plurality of impedance measurements are performed at a plurality of excitation frequencies.

[0056] Example 11 provides the method of Example 10, further including performing comparison, determination, and integration for each excitation frequency.

[0057] Example 12 provides a system for detecting pulmonary fluid status in a human subject, the system comprising: a plurality of electrodes located on the chest of the human subject; a chest impedance detection module connected to the electrodes, the chest impedance detection module being configured to perform a plurality of impedance measurements on a region of interest to obtain measured impedance data; comparing the measured impedance data with simulated impedance data obtained from a plurality of models of the region of interest; for each model, determining a model fit based on a comparison between the simulated impedance data obtained from the model and the measured impedance data; and integrating individual resistivity estimates obtained from the models based on the model fit, such that the individual resistivity estimates from better-fitting models are more heavily weighted in the final resistivity estimate than individual resistivity estimates from worse-fitting models.

[0058] Example 13 provides the system of Example 12, in which performing multiple impedance measurements includes performing multiple 4-wire impedance measurements.

[0059] Example 14 provides a system of any of Examples 12-13, wherein the electrodes are connected to an elastic chest strap for attachment around the chest of the human subject to ensure the correct positioning of the electrodes relative to the region of interest.

[0060] Example 15 provides a system of any one of Examples 12-13, wherein each of the models represents a different possible combination of electrode placement and specific anatomical features of the human subject.

[0061] Example 16 provides the system of Example 15, wherein the specific anatomical features include at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

[0062] Example 17 provides a system of any of Examples 12-13, wherein the final resistivity estimate is a weighted average of the individual resistivity estimates.

[0063] Example 18 provides the system of Example 17, where the weights assigned to model N are determined by 1 / f residual,cost,N Define f residual,cost It is the residual cost function value used to solve the inverse problem.

[0064] Example 19 provides a system of any one of Examples 12-13, wherein the chest impedance detection module is further configured to develop a single sample model based on a corresponding fit of the model, using a weighted sum of the multiple models.

[0065] Example 20 provides a system of any one of Examples 12-13, wherein the plurality of electrodes comprises fewer than eight electrodes.

[0066] Example 21 provides a system of any one of Examples 12-13, wherein the plurality of electrodes further includes three electrodes located in the anterior part of the chest and three electrodes located in the left side of the thoracic cavity.

[0067] Example 22 provides a system of any of Examples 12-13, wherein the multiple impedance measurements are performed at a single excitation frequency.

[0068] Example 23 provides a system of any of Examples 12-13, wherein the plurality of impedance measurements are performed at a plurality of excitation frequencies.

[0069] Example 24 provides the system of Example 23, wherein the chest impedance detection module is further configured to perform the comparison, the determination, and the integration for each of the excitation frequencies.

[0070] Example 25 provides a method for detecting the pulmonary fluid state of a human subject, the method comprising: performing multiple impedance measurements on a region of interest to obtain measured impedance data; summarizing multiple models of the region of interest into a single sample model representing the region of interest; generating simulated impedance data using the single sample model; and fitting the simulated impedance data to the measured impedance data to produce a final resistivity estimate of the region of interest.

[0071] Example 26 provides the method of Example 25, which applies weights to each of the models to produce a weighted model before the aggregation.

[0072] Example 27 provides the method of Example 26, wherein the summation further includes calculating the average of the sums of the weighted models.

[0073] Example 28 provides the method of Example 26, further including fitting the simulated impedance data to the measured impedance data to generate weights applied to the model.

[0074] Example 29 provides a method for any of Examples 25-28, wherein performing multiple impedance measurements includes performing multiple 4-wire impedance measurements.

[0075] Example 30 provides the method of Example 29, wherein fewer than eight electrodes are used to perform the plurality of 4-wire impedance measurements.

[0076] Example 31 provides a method of any one of Examples 25-28, wherein each of the models represents a different possible combination of electrode placement and specific anatomical features of the human subject.

[0077] Example 32 provides the method of Example 31, wherein the specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

[0078] Example 33 provides a method for any of Examples 25-28, wherein the multiple impedance measurements are performed at a single excitation frequency.

[0079] Example 34 provides a method for any of Examples 25-28, wherein the plurality of impedance measurements are performed at a plurality of excitation frequencies.

[0080] Example 35 provides the method of Example 34, further including performing the summarization, generation, and fitting for each of the excitation frequencies.

[0081] Example 36 provides a system for detecting pulmonary fluid status in a human subject, the system comprising a plurality of electrodes located on the chest of the human subject; a chest impedance detection module connected to the electrodes, the chest impedance detection module being configured to perform multiple impedance measurements on a region of interest to obtain measured impedance data; summarizing multiple models of the region of interest into a single sample model representing the region of interest; generating simulated impedance data using the single sample model; and fitting the simulated impedance data to the measured impedance data to produce a final resistivity estimate of the region of interest.

[0082] Example 37 provides the system of Example 36, in which performing multiple impedance measurements includes performing multiple 4-wire impedance measurements.

[0083] Example 38 provides a system of any of Examples 36-37, wherein the electrodes are connected to an elastic chest band for attachment around the chest of the human subject to ensure the correct positioning of the electrodes relative to the region of interest.

[0084] Example 39 provides a system for any of Examples 36-37, where each of the models represents a different possible combination of electrode placement and specific anatomical features of the human subject.

[0085] Example 40 provides the system of Example 39, wherein the specific anatomical features include at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

[0086] Example 41 provides a system of any of Examples 36-37, wherein the chest impedance detection module is further configured to develop a single sample model based on a corresponding fit of the model, using a weighted sum of the multiple models.

[0087] Example 42 provides a system of any one of Examples 36-37, wherein the plurality of electrodes comprises fewer than eight electrodes.

[0088] Example 43 provides a system of any of Examples 36-37, wherein the plurality of electrodes further includes three electrodes located on the front of the chest and three electrodes located on the left side of the thoracic cavity.

[0089] Example 44 provides a system of any of Examples 36-37, wherein the multiple impedance measurements are performed at a single excitation frequency.

[0090] Example 45 provides a system of any of Examples 36-37, wherein the plurality of impedance measurements are performed at a plurality of excitation frequencies.

[0091] Example 46 provides the system of Example 45, which also includes a chest impedance detection module that performs summarization, generation, and fitting for each excitation frequency.

[0092] It should be noted that all specifications, dimensions, and relationships (e.g., number of elements, operations, steps, etc.) outlined herein are for illustrative and educational purposes only. Such information may be significantly altered without departing from the spirit of this disclosure or the scope of the appended claims. These specifications apply only to a non-limiting example and are therefore to be interpreted as such. Exemplary embodiments have been described with reference to specific arrangements of components in the foregoing description. Various modifications and changes may be made to such embodiments without departing from the scope of the appended claims. Therefore, the specification and drawings should be considered illustrative rather than restrictive.

[0093] Note that in the numerous examples provided herein, interactions may be described based on two, three, four, or more electrical components. However, this is done merely for clarity and illustration. It should be understood that the system can be combined in any suitable manner. Any components, modules, and elements shown in the figures can be combined into a wide variety of possible configurations, all clearly within the broad scope of this specification, based on similar design alternatives. In some cases, it may be easier to describe one or more functions of a given set of flows by referring only to a limited number of electrical components. It should be understood that the figures and the circuits they teach are readily expandable and can accommodate a large number of parts as well as more complex / complex arrangements and configurations. Therefore, the examples provided should not limit the scope of the circuits or inhibit the broad teaching of the circuits, as the circuits can be applied to countless other architectures.

[0094] It should also be noted that in this specification, references to various features (e.g., elements, structures, modules, components, steps, operations, characteristics, etc.) included in “one embodiment,” “exemplary embodiment,” “embodiment,” and “another embodiment,” “some embodiments,” etc., are intended to mean that any such feature is included in one or more embodiments of this disclosure, but may or may not be combined in the same embodiment.

[0095] It should also be noted that the functions related to the circuit architecture only illustrate some possible circuit architecture functions that can be performed by or within the system shown in the figures. Some of these operations may be removed or eliminated where appropriate, or these operations may be significantly modified or changed, without departing from the scope of this disclosure. Furthermore, the timing of these operations may vary considerably. The above business processes are for illustrative and discussion purposes only. The embodiments described herein offer great flexibility, as any suitable arrangement, timeline, configuration, and timing mechanism can be provided without departing from the teachings of this disclosure.

[0096] Those skilled in the art can identify many other changes, substitutions, variations, improvements and modifications, and this disclosure is intended to include all such changes, substitutions, variations, improvements and modifications that fall within the scope of the appended claims.

[0097] Note that all optional features of the devices and systems described above can also be implemented relative to the methods or processes described herein, and the details in the examples can be used anywhere in one or more examples.

[0098] In these cases, the “means” mentioned above may include (but are not limited to) the use of any suitable components discussed herein, as well as any suitable software, circuits, hubs, computer code, logic, algorithms, hardware, controllers, interfaces, links, buses, communication paths, etc.

[0099] Note that the interactions can be described using two, three, or four network elements for the examples provided above, as well as many other examples presented herein. This is done for clarity and illustration only. In some cases, it may be easier to describe one or more functions of a given set of flows by referring to only a limited number of network elements. It should be understood that the topologies illustrated and described with reference to the accompanying drawings (and their teachings) are readily extensible and can accommodate a large number of components, as well as more complex / complex arrangements and configurations. Therefore, the examples provided should not limit the scope of the illustrated topologies or inhibit their extensive teachings, which can be applied to countless other architectures.

[0100] It is equally important to note that the steps in the preceding flowcharts only illustrate some possible signaling scenarios and patterns that can be performed by or within the communication system shown in the diagrams. Some of these steps may be deleted or removed where appropriate, or these steps may be significantly modified or altered, without departing from the scope of this disclosure. Furthermore, many of these operations have been described as being performed simultaneously or in parallel with one or more additional operations. However, the timing of these operations can vary considerably. The above business processes are for illustrative and discussion purposes only. The communication system shown in the diagrams offers considerable flexibility, as any suitable arrangement, timeline, configuration, and timing mechanism can be provided without departing from the teachings of this disclosure.

[0101] Although this disclosure has been described in detail with reference to specific arrangements and configurations, these exemplary configurations and setups may be significantly modified without departing from the scope of this disclosure. For example, although this disclosure has been described with reference to specific communication switches, the embodiments described herein can be applied to other architectures.

[0102] Many other changes, substitutions, variations, alterations, and modifications can be identified by those skilled in the art, and this disclosure is intended to include all such changes, substitutions, variations, alterations, and modifications that fall within the scope of the appended claims. In order to assist the United States Patent and Trademark Office (USPTO) and any reader of any patent issued under this application in interpreting the appended claims, the applicant wishes to draw the attention of the applicant that: (a) the applicant does not intend to invoke section 142, paragraph 6(6) of the United States Code in the appended claims unless the word “means” or “step” is specifically used in a particular claim; and (b) the applicant does not intend to limit this disclosure by any statement in the specification in any way not otherwise reflected in the appended claims.

Claims

1. A method for detecting the pulmonary fluid status of a human subject, the method comprising: Perform multiple impedance measurements on the region of interest to obtain measured impedance data; The measured impedance data is compared with simulated impedance data obtained from multiple models of the region of interest; For each of the plurality of models, the fit of the model is determined based on a comparison between the simulated impedance data obtained from the model and the measured impedance data; and The individual resistivity estimates obtained from the plurality of models are integrated based on the fitting of the model, such that the individual resistivity estimates from the better-fitting model are more heavily weighted in the final resistivity estimate than the individual resistivity estimates from the worse-fitting model.

2. The method of claim 1, wherein performing multiple impedance measurements includes performing multiple 4-wire impedance measurements.

3. The method of claim 2, wherein up to eight electrodes are used to perform the plurality of 4-wire impedance measurements.

4. The method according to any one of claims 1-3, wherein each of the models represents a different possible combination of electrode placement and specific anatomical features of the human subject.

5. The method of claim 4, wherein the specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

6. The method according to any one of claims 1-3, wherein the final resistivity estimate is a weighted average of the individual resistivity estimates.

7. The method of claim 6, wherein the weights assigned to a particular model among the plurality of models are defined by residual cost function values ​​for solving the inverse problem, the residual cost function values ​​corresponding to the particular model.

8. The method according to any one of claims 1-3 further includes developing a single sample model using a weighted sum of the plurality of models based on the corresponding fit of the model.

9. The method according to any one of claims 1-3, wherein the plurality of impedance measurements are performed at a single excitation frequency.

10. The method according to any one of claims 1-3, wherein the plurality of impedance measurements are performed at a plurality of excitation frequencies.

11. The method of claim 10, further comprising performing the comparison, the determination, and the integration for each excitation frequency.

12. A system for detecting pulmonary fluid status in a human subject, the system comprising: Multiple electrodes are located on the chest of the human subject; A chest impedance detection module, connected to the electrodes, is configured to: Perform multiple impedance measurements on the region of interest to obtain measured impedance data; The measured impedance data is compared with simulated impedance data obtained from multiple models of the region of interest; For each model, the fit of the model is determined based on a comparison between the simulated impedance data obtained from the model and the measured impedance data. and The individual resistivity estimates obtained from the model are integrated based on the fit of the model, such that the individual resistivity estimates from the better-fitting model are more heavily weighted in the final resistivity estimate than the individual resistivity estimates from the worse-fitting model.

13. The system of claim 12, wherein performing multiple impedance measurements includes performing multiple 4-wire impedance measurements.

14. The system of any one of claims 12-13, wherein the electrodes are connected to an elastic chest band for attachment around the chest of the human subject to ensure the correct positioning of the electrodes relative to the region of interest.

15. The system according to any one of claims 12-13, wherein each of the models represents a different possible combination of electrode placement and specific anatomical features of the human subject.

16. The system of claim 15, wherein the specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

17. The system according to any one of claims 12-13, wherein the final resistivity estimate is a weighted average of the individual resistivity estimates.

18. The system of claim 17, wherein the weights assigned to a particular model among the plurality of models are defined by residual cost function values ​​for solving the inverse problem, the residual cost function values ​​corresponding to the particular model.

19. The system according to any one of claims 12-13, wherein the chest impedance detection module is further configured to develop a single sample model based on a corresponding fitting of the model, using a weighted sum of the plurality of models.

20. The system according to any one of claims 12-13, wherein the plurality of electrodes comprises fewer than eight electrodes.

21. The system according to any one of claims 12-13, wherein the plurality of electrodes further comprises three electrodes located on the front of the chest and three electrodes located on the left side of the chest.

22. The system according to any one of claims 12-13, wherein the plurality of impedance measurements are performed at a single excitation frequency.

23. The system according to any one of claims 12-13, wherein the plurality of impedance measurements are performed at a plurality of excitation frequencies.

24. The system of claim 23, wherein the chest impedance detection module is further configured to perform the comparison, the determination, and the integration for each of the excitation frequencies.

25. A method for detecting the pulmonary fluid status of a human subject, the method comprising: Perform multiple impedance measurements on the region of interest to obtain measured impedance data; Based on multiple models, a single sample model representing the region of interest is generated by determining the sum of multiple weighted models, wherein each of the multiple models represents different possible combinations of electrode placement and specific anatomical features of the human subject; The single sample model is used to generate simulated impedance data; The simulated impedance data is fitted to the measured impedance data to generate weights applied to the multiple models and a final resistivity estimate for the region of interest.

26. The method of claim 25, further comprising applying weights to each of the plurality of models to produce the plurality of weighted models before generating a single sample model.

27. The method of claim 25, wherein performing multiple impedance measurements includes performing multiple 4-wire impedance measurements.

28. The method of claim 26, wherein performing multiple impedance measurements includes performing multiple 4-wire impedance measurements.

29. The method of claim 27 or 28, wherein fewer than eight electrodes are used to perform the plurality of 4-wire impedance measurements.

30. The method according to any one of claims 25-28, wherein the specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

31. The method according to any one of claims 25-28, wherein the plurality of impedance measurements are performed at a single excitation frequency.

32. The method according to any one of claims 25-28, wherein the plurality of impedance measurements are performed at a plurality of excitation frequencies.

33. The method of claim 32, further comprising performing the generation of a single-sample model, the generation of simulated impedance data, and the fitting for each of the excitation frequencies.

34. A system for detecting pulmonary fluid status in a human subject, the system comprising: Multiple electrodes are located on the chest of the human subject; A chest impedance detection module connected to the electrodes, the chest impedance detection module being configured to perform the method as described in any one of claims 25-33.

35. The system of claim 34, wherein the electrodes are connected to an elastic chest strap for attachment around the chest of the human subject to ensure the correct positioning of the electrodes relative to the region of interest.

36. The system of claim 34, wherein the chest impedance detection module is further configured to develop a single sample model based on a model-based fitting, using a weighted sum of the plurality of models.

37. The system of claim 34, wherein the plurality of electrodes further comprises three electrodes located on the front of the chest and three electrodes located on the left side of the chest.

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