Model-based techniques for detecting lung fluid state

Abstract

One embodiment is a method of performing chest tomography on a human subject, the method including: performing a plurality of four-wire 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; determining, for each of the models, a goodness-of-fit of the model based on a comparison of the simulated impedance data obtained from the model with the measured impedance data; and aggregating the individual resistivity estimates obtained from the models based on the goodness-of-fit of the model such that individual resistivity estimates from better fitting models are weighted more heavily in a final resistivity estimate than individual resistivity estimates from worse fitting models.

Description

[Technical Field]

[0001] Related applications This disclosure claims priority to U.S. Provisional Patent Application No. 63 / 124,206, entitled "MODEL-BASED LUNG FLUID STATUS DETECTION," filed on 11 December 2020, which is incorporated in its entirety by reference.

[0002] This disclosure relates generally to the field of pulmonary fluid tomography, and more specifically to a technique for model-based pulmonary fluid state detection using multiple impedance measurements in combination with prior knowledge of the area of ​​interest. [Overview of the project] [Means for solving the problem]

[0003] Embodiments of this disclosure provide a method for detecting the pulmonary fluid state of a human subject. This method is To obtain measured impedance data, multiple impedance measurements are performed on the region of interest, This involves comparing measured impedance data with simulated impedance data obtained from multiple models in the domain of interest, For each model, the degree of fit of the model is determined based on a comparison between the simulated impedance data obtained from the model and the measured impedance data. This includes integrating individual resistivity estimates obtained from the model based on the model's goodness of fit, such that individual resistivity estimates from a better-fitting model are weighted more heavily in the final resistivity estimate than individual resistivity estimates from a worse-fitting model.

[0004] Another embodiment of this disclosure provides a method for detecting the pulmonary fluid state of a human subject. This method is To obtain measured impedance data, multiple impedance measurements are performed on the region of interest, Summarizing a plurality of 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 measured impedance data to create a final resistivity estimate for the region of interest.

Brief Description of the Drawings

[0005] To more fully understand the present disclosure and its features and advantages, reference is made to the following description in conjunction with the accompanying drawings, in which like reference numerals represent like parts.

[0006] [Figure 1] An exemplary environment is illustrated in which there is an exemplary system for model-based lung fluid state detection according to some embodiments of the present disclosure. [Figure 2] FIG. 1 is a block diagram showing exemplary functional components of the system of FIG. 1 according to some embodiments of the present disclosure. [Figure 3] FIG. 2 illustrates the operation of a four-wire impedance measurement system according to one embodiment. [Figure 4] Exemplary electrical impedance tomography techniques for lung fluid state detection are illustrated in which a plurality of (e.g., eight or more) electrodes distributed around the region of interest are used to perform a plurality of four-wire impedance measurements to obtain a resistivity estimation map without using prior knowledge of the region of interest. [Figure 5] A cross-sectional model of the upper human body is illustrated showing various types of tissue (lung, heart, bone, and soft tissue) and air. [Figure 6] FIG. 3 is a flowchart showing the operation of a model-based liquid state detection technique using a single model of the region of interest. [Figure 7A] Exemplary embodiments described herein for implementing a model-based lung fluid state detection system using a plurality of impedance measurements in combination with prior knowledge of the region of interest are illustrated. [Figure 7B]Exemplary embodiments described herein for implementing a model-based lung fluid state detection system using multiple impedance measurements in combination with prior knowledge of the domain of interest are illustrated. [Figure 8A] Another exemplary embodiment described herein illustrates a model-based lung fluid state detection system that uses multiple impedance measurements in combination with prior knowledge of the domain of interest. [Figure 8B] Another exemplary embodiment described herein illustrates a model-based lung fluid state detection system that uses multiple impedance measurements in combination with prior knowledge of the domain of interest. [Figure 9] A schematic block diagram of a system for performing chest impedance measurements on human subjects is shown. [Modes for carrying out the invention]

[0007] Lung resistivity is a physiological parameter that describes the electrical properties of the lungs. Lung composition changes due to changes 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 one such change in lung composition that can be monitored by measuring lung resistivity.

[0008] In certain embodiments, a chest impedance (Z) measurement system is an inexpensive, non-invasive system for evaluating lung resistivity, and therefore pulmonary fluid status, and is available in a wearable form factor to enable home use and monitoring. In some embodiments, chest impedance measurement can be achieved using four electrodes.

[0009] Figure 1 shows an exemplary environment 100 illustrating an exemplary embodiment of a system 102 for performing model-based pulmonary fluid state detection in a human subject according to several embodiments of the present disclosure. Monitoring may be performed continuously or periodically. As shown in Figure 1, according to one exemplary embodiment, the system 102 includes a four-wire chest impedance measurement module 112 and a plurality of surface electrodes / sensors 114a-114d (e.g., four surface electrodes / sensors, or any other preferred number of surface electrodes / sensors). For example, one or more of the surface electrodes may be implemented as solid gel surface electrodes or any other preferred surface electrodes. The system 102 may be configured as a substantially triangular device, or a device of any other preferred shape, capable of operating to contact one or more of the torso, upper chest, and neck, or any other preferred part or region of the body of a human subject 104 via at least a plurality of surface electrodes / sensors 114a-114d.

[0010] In various embodiments, system 102 may have a configuration that allows it to be implemented as multiple patch-like devices within a wearable vest-like structure, or as any other suitable structure or device. In one possible environment, such as environment 100, system 102 may be operable to communicate bidirectionally with a smartphone 106 via a wireless communication path 116, and smartphone 106 may then be operable to communicate bidirectionally with a communication network 108 (e.g., the Internet) via the wireless communication path 118. Alternatively, a direct link to the cloud 110 may be provided without requiring a base station or relay point via a mobile phone. Smartphone 106 may further be operable to communicate bidirectionally with the cloud 110 via a wireless communication path 120 and the communication network 108, which may include resources for cloud computing, data processing, data analysis, data trends, data organization, data fusion, data storage, and other functions. System 102 may further be operable to communicate directly and bidirectionally with the cloud 110 via a wireless communication path 122.

[0011] Figure 2 shows an exemplary block diagram of a system 102 for performing model-based pulmonary fluid state detection in a human subject, according to several embodiments of the present disclosure. As shown in Figure 2, the system includes a chest impedance measurement module 112, a processor 202 and associated memory 208, data storage 206 for storing chest impedance, pulmonary resistivity, model, and associated data, and a transmitter / receiver 204. The transmitter / receiver 204 may be configured to perform Bluetooth® communication, Wi-Fi communication, or any other suitable short-range communication for communicating with a smartphone 106 (Figure 1) via a wireless communication path 116. The transmitter / receiver 204 may be further configured to perform cellular communication or any other suitable long-range communication for communicating with a cloud 110 (Figure 1) via a wireless communication path 122. In certain embodiments, the chest impedance measurement module 112 may further include an electrode / sensor connection switching circuit 224 for switchable connection to a plurality of surface electrodes / sensors 114a-114d shown in Figure 1.

[0012] The processor 202 may include multiple processing modules, such as a data analyzer 226 and a data fusion / decision engine 228. The transmitter / receiver 204 may include at least one antenna 210 capable of transmitting and receiving radio signals, such as Bluetooth® signals or Wi-Fi signals, via a wireless communication path 116 to and from a smartphone 106, which may be a Bluetooth® or Wi-Fi enabled smartphone, or any other suitable smartphone. The antenna 210 may further be capable of transmitting and receiving radio signals, such as cellular signals, via a wireless communication path 122 to and from a cloud 110.

[0013] The processor 202 may further include a pulmonary fluid state detection module 234 for performing model-based pulmonary fluid state detection in a human subject according to the embodiments described herein, as will be described in more detail below. It will be recognized that all or part of the processor 202, and modules shown to form part of the processor 202 (e.g., part or all of the pulmonary fluid state detection module 234), may be additionally or alternatively implemented within the cloud 110 (Figure 1), and may actually include multiple processors and / or processing elements for implementing the technology described herein. It will be further recognized that other elements shown in Figure 2 as constituting part of the system 102 may be additionally and / or alternatively present within the cloud 110 (Figure 1). In certain embodiments, the processor 202 may include a controller for controlling the operation of the measurement module / circuit.

[0014] The transmitter / receiver 204 may include at least one antenna 210 that is capable of transmitting and receiving wireless signals, such as Bluetooth® signals or Wi-Fi signals, via the wireless communication path 116 to and from a smartphone 106, which may be a Bluetooth® or Wi-Fi enabled smartphone, or any other suitable smartphone. The antenna 210 is further capable of transmitting and receiving wireless signals, such as cellular signals, via the wireless communication path 122 to and from a cloud 110.

[0015] The operation of System 102 for performing model-based pulmonary fluid state detection in a human subject, according to several embodiments, will be further understood by referring to the following exemplary examples and Figures 1 and 2. In this exemplary example, while the human subject 104 is in a supine or standing position, the human subject or human assistant positions System 102, configured as a substantially triangular device (or a device of any other preferred shape), to contact one or more of the subject's torso, upper chest, and neck (or any other preferred part or region of the body) via a plurality of surface electrodes / sensors 114a to 114d, a fixed number of times each day (e.g., twice a day) or for a predetermined number of consecutive days.

[0016] After positioning the system 102 to contact the torso and / or upper chest and / or neck 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 a signal indicating the data.

[0017] The four-wire chest impedance measurement module 112 can perform chest impedance measurements using some or all of the multiple surface electrodes 114a to 114d that come into contact with the skin of the torso, upper chest, and / or neck of a human subject 104.

[0018] In some embodiments, chest impedance data from the chest impedance measurement module 112 may be provided to a data analyzer 226 for at least partial data analysis, data trending, and / or data organization. In one embodiment, chest impedance measurement data, combined with other metadata such as medical history, demographic information, and other laboratory modalities, may also be analyzed, trended, and / or organized "in the cloud" and made available in a cloud-based data storage 110 with pre-configured alerts for use in various levels of clinical intervention regarding respiratory parameters.

[0019] The data analyzer 226 may provide the data fusion / decision engine 228 with at least partially analyzed chest impedance data, which can then effectively fuse or combine the chest impedance data with other sensing data, at least partially, according to one or more algorithms and / or decision criteria, for subsequent use in making one or more inferences about the human subject 104. The processor 202 may then provide the at least partially combined chest impedance data and other sensing data to the transmitter / receiver 204, which can directly transmit the combined chest impedance data and sensing data to the cloud 110 via the wireless communication path 122, or to the smartphone 106 via the wireless communication path 116. The smartphone 106 can then transmit the combined chest impedance data and sensing data to the cloud 110 via the communication network 108 and wireless communication paths 118 and 120, where the data can be further analyzed, trended, organized, and / or fused. As described above, it will be recognized that communication data can be transmitted directly to the cloud 110 without the involvement of a smartphone / mobile phone or base station.

[0020] The resulting curated combined sensing data can then be remotely downloaded by hospital clinicians for risk scoring / stratification, monitoring, and / or tracking.

[0021] Figure 3 illustrates the operation of a four-wire chest impedance measurement system 300 according to one embodiment. As shown in Figure 3, the system includes four electrodes 302A to 302D. During 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 the region of interest 304 (e.g., the lung), and the voltage difference V across 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). A change in impedance indicates a change in the region of interest 304.

[0022] As will be described in more detail, according to the features of the embodiments described herein, the technique may also be performed using measurements at multiple excitation frequencies, which, as opposed to results from a single excitation frequency, have final clinically useful information derived from a combination of results from multiple excitation frequencies.

[0023] Four-wire chest impedance measurement systems, such as System 300, suffer from certain limitations. For example, when the region of interest 304 has high resistivity, as in the case of the lungs, the calculated impedance Z is not sensitive to changes in resistivity and is not specific. In addition, slight changes in pulmonary fluid state are easily obscured by other factors.

[0024] Referring here to Figure 4, this specification illustrates an exemplary electrical impedance tomography technique 400 for pulmonary fluid state detection, in which multiple quaternary impedance measurements are performed using multiple (e.g., eight or more) electrodes 402A-402H distributed around a region of interest 404 to obtain a map of resistivity estimates without using prior knowledge of the region of interest. As shown in Figure 4, the region of interest 404 represents the upper body (or chest) of a human, including the lungs 406 and heart 408 of the individual.

[0025] According to the features of the embodiments described herein, the model-based liquid state detection technique is performed using a limited number of electrodes (e.g., 5 or 6) in combination with prior knowledge of the region of interest or domain, and multiple 4-wire impedance measurements are performed to determine the resistivity (ρ) of the lung region. lung This is performed to extract domain information including ). The techniques described herein are more sensitive and specific to changes in lung fluid than single four-wire impedance measurement techniques, but are less computationally heavy and complex than conventional electrical impedance tomography techniques.

[0026] FIG. 5 illustrates a cross-sectional model 500 of a human upper body (or chest) showing various types of tissues including lungs 502, heart 504, bone 506, and soft tissue 508. The lungs 502 are filled with air. An exemplary arrangement of electrodes 510A - 510E is also shown in FIG. 5. Lung resistivity (or lung fluid state) can be estimated within a single model such as the model 500 shown in FIG. 5.

[0027] FIG. 6 is a flow diagram showing the operation of a model-based liquid state detection technique using a single model. In step 600, N four-wire measurements are performed using some (e.g., 5 or 6 (preferably less than 8)) electrodes to construct a set Z meas = Z1, Z2,... Z N of measured impedance data for a region or domain of interest. In step 602, simulated measurements are obtained using a model of the domain of interest such as the model 500 shown in FIG. 5. Typically, model parameters (x) consisting of continuous variables including ρ lung are simulated to determine impedance measurements Z 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, the lung resistivity estimate is performed through an optimization process that identifies a set of x including ρ lung that minimizes a cost function, for example, as described but not limited to the following.

[0028]

Number

[0029] Solving the inverse problem described above for estimating lung resistance presents several challenges. For example, the measurement conditions are likely to differ significantly from those assumed in the simulation model (e.g., electrode arrangement, lung size, tissue arrangement). As a result, the final results may be overly sensitive to factors other than the state of the lung fluid.

[0030] According to the features of the embodiments described herein, in order to ensure that the final results are highly sensitive and specific to changes in lung fluid rather than to other factors, several inverse problems are solved, and the results are summarized based on the "goodness of fit" between the measurement data and each problem. Referring here to Figure 7A, in one embodiment, the measured impedance data Z at frequency f in the region of interest (e.g., the human chest or upper body) meas 700 is the simulated impedance data Z obtained from each of the M sample models 702(1) to 702(M). sim This is compared with the following. Each of the M sample models 702(1)–702(M) represents a different possible combination of electrode arrangement and specific anatomical features of the region of interest (e.g., relative size and location of lung tissue, cardiac tissue, soft tissue, and bone). The “goodness of fit” can be determined for each of the M models based on a comparison of measured impedance data with simulated impedance data from the model 704(1)–704(M). Individual resistivity estimates (ρ) of the models est,1 -ρ est,N Each estimated model parameter (X) of the model, including ) est,1 -X est,N ) are combined or summarized based on the "goodness of fit" between the measured data and each model, such that estimates from better "fit" models are weighted more heavily than those from worse "fit" models in the final resistivity estimate (706), and the final resistivity estimate (ρ est The final model parameters (X) include ) est )708 is constructed. For example, the final resistivity estimate may be a weighted average of the individual estimates, with weights (W1~W N) is assigned to model i, defined, for example, by, but not limited to, the following:

[0031] 1 / f residual,cost,i

[0032] Here, f residual,cost This is the residual cost function value of the optimization for solving the inverse problem. In the embodiment illustrated in Figure 7A, one measurement is understood as a number of possible arrays and anatomical geometric shapes, each having its own likelihood or weight.

[0033] It should be noted that chest impedance measurements can vary depending on the excitation frequency used to perform the measurement, due to fundamental changes in how current flows in the region of interest. For example, at a first excitation frequency, the measurement may be more sensitive to changes in soft tissue, while at a different excitation frequency, the measurement may be more sensitive to changes in lung tissue.

[0034] According to embodiments of the specification described herein, as shown in Figure 7B, the technique shown in Figure 7A measures impedance data Z meas (f1)~Z meas (f m ), and estimated (or simulated) impedance data Z est (f1)~Z est (f m To generate ), multiple frequencies f1~f m The measured and estimated impedance data can be applied to each of them. The result is Z meas (f1)~Z meas (f m ) each simulated impedance measurement Z est (f1)~Z est (f mThe data is combined by fitting the models to construct 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 estimates across the frequency range than those from the worse-fitting model. The resulting parameters are used to generate final clinical insights into changes in the region of interest.

[0035] Figure 8A illustrates an alternative embodiment in which M models 702(1) to 702(M) are “summarized” (through the mean of the weighted sum of the models) 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 obtain the final resistivity estimate ρ est , and the weights W used in the weighted sum of M sample models to create a single sample model 806. i Final model parameters X, including est Impedance data Z for creating meas Estimated impedance data (Z) that "fits" (804) est It is used to create ).

[0036] According to embodiments of the specification described herein, as shown in Figure 8B, the technique shown in Figure 8A provides the final resistivity estimate ρ estf1 ~ρ estfm and weight W if1 ~W ifm Model parameter X including est (f1)~X est (f m To generate ), multiple excitation frequencies f1~f m This can be performed on the following: The resulting parameters are combined and used to produce final clinical insights into changes in the area of ​​interest.

[0037] Figure 9 shows the measured impedance data Z of the patient's region of interest (e.g., lungs), as described above. measA block diagram of an exemplary system 900 for obtaining the results is shown. As shown in Figure 9, an elastic chest band 902 to which electrodes are connected is attached to the chest of a human subject 904. In the illustrated embodiment, three of the electrodes may be arranged near the center of the subject 904's chest and three may be arranged on the left side of the subject. A microcontroller unit (MCU) 906 under the control of a computer (PC) 908 controls the measurement sequence. Specifically, the MCU 906 modifies the switch configuration 910 to perform different four-wire impedance measurements using a four-wire impedance measurement module 912 with six electrodes (in the illustrated embodiment) arranged on the chest of the subject 904.

[0038] It will be recognized that some or all of the processes, modules, and / or devices illustrated and described with reference to Figure 9 may be implemented using some or all of the processes, modules, and / or devices shown and described with reference to Figures 1 and / or 2, or vice versa.

[0039] Example 1 provides a method for detecting the pulmonary fluid state of a human subject, 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; determining the goodness of fit of each model based on a comparison of the simulated impedance data obtained from the model with the measured impedance data; and integrating the individual resistivity estimates obtained from the models based on the goodness of fit, such that the individual resistivity estimates from the better-fitting models are weighted more heavily in the final resistivity estimate than the individual resistivity estimates from the worse-fitting models.

[0040] Example 2 provides the method of Example 1, wherein performing multiple impedance measurements includes performing multiple four-wire impedance measurements.

[0041] Example 3 provides the method of Example 2, wherein up to eight electrodes are used to perform multiple four-wire impedance measurements.

[0042] Example 4 provides a method according to any one of Examples 1 to 3, wherein each model represents a different possible combination of electrode arrangement and specific anatomical features of a human subject.

[0043] Example 5 provides the method according to Example 4, wherein a specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

[0044] Example 6 provides a method according to any one of Examples 1 to 3, wherein the final resistivity estimate is a weighted average of the individual resistivity estimates.

[0045] In Example 7, the weights assigned to Model N are 1 / f residual,cost,N Defined by, where f residual,cost The method described in Example 6 provides the residual cost function value of the optimization for solving the inverse problem.

[0046] Example 8 provides a method according to any one of Examples 1 to 3, further comprising constructing a single sample model using a weighted sum of multiple models based on the fitness of each model.

[0047] Example 9 provides a method according to any one of Examples 1 to 3, wherein multiple impedance measurements are performed at a single excitation frequency.

[0048] Example 10 provides a method according to any one of Examples 1 to 3, wherein multiple impedance measurements are performed at multiple excitation frequencies.

[0049] Example 11 provides the method of Example 10, further comprising comparing, determining, and integrating for each excitation frequency.

[0050] Example 12 provides a system for detecting the pulmonary fluid state of a human subject, wherein the system comprises a plurality of electrodes on the chest of the human subject and a chest impedance detection module connected to the electrodes, wherein the chest impedance detection module performs a plurality of impedance measurements on a region of interest to acquire measured impedance data, compares the measured impedance data with simulated impedance data obtained from a plurality of models of the region of interest, determines the goodness of fit of each model based on a comparison of the simulated impedance data obtained from the model with the measured impedance data, and integrates the individual resistivity estimates obtained from the models based on the goodness of fit of the models such that individual resistivity estimates from better-fitting models are weighted more heavily in the final resistivity estimate than individual resistivity estimates from worse-fitting models.

[0051] Example 13 provides the system described in Example 12, wherein performing multiple impedance measurements includes performing multiple four-wire impedance measurements.

[0052] Example 14 provides the system according to Example 12 or 13, wherein the electrodes are connected to elastic chest straps for attachment around the chest of a human subject to ensure the correct positioning of the electrodes relative to the region of interest.

[0053] Example 15 provides the system described in Example 12 or 13, in which each model represents a different possible combination of electrode arrangement and specific anatomical features of a human subject.

[0054] Example 16 provides the system described in Example 15, wherein a specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

[0055] Example 17 provides the system described in Example 12 or 13, wherein the final resistivity estimate is a weighted average of the individual resistivity estimates.

[0056] Example 18 shows that the weights assigned to Model N are 1 / f residual,cost,N Defined by, where f residual,cost This provides the system described in Example 17, which is the residual cost function value of the optimization for solving the inverse problem.

[0057] Example 19 provides the system described in Example 12 or 13, wherein the chest impedance detection module is further configured to construct a single sample model using a weighted sum of multiple models based on the fit of each model.

[0058] Example 20 provides the system described in Example 12 or 13, wherein the plurality of electrodes includes fewer than eight electrodes.

[0059] Example 21 provides the system according to Example 12 or 13, further comprising three electrodes on the front of the chest and three of the electrodes on the left side of the chest.

[0060] Example 22 provides the system described in Example 12 or 13, in which multiple impedance measurements are performed at a single excitation frequency.

[0061] Example 23 provides the system described in Example 12 or 13, in which multiple impedance measurements are performed at multiple excitation frequencies.

[0062] Example 24 provides the system described in Example 23, wherein the chest impedance detection module is further configured to perform comparison, determination, and integration for each excitation frequency.

[0063] Example 25 provides a method for detecting the pulmonary fluid state of a human subject, 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 measured impedance data to create a final resistivity estimate of the region of interest.

[0064] Example 26 provides the method of Example 25, further comprising applying weights to each of the models in order to create a weighted model, before summarizing.

[0065] Example 27 provides the method of Example 26, which in summary further includes calculating the average of the sum of the weighted models.

[0066] Example 28 provides the method of Example 26, further comprising fitting simulated impedance data to measured impedance data to create weights that can be applied to the model.

[0067] Example 29 provides a method according to any one of Examples 25-28, wherein performing multiple impedance measurements includes performing multiple four-wire impedance measurements.

[0068] Example 30 provides the method of Example 29, wherein fewer than eight electrodes are used to perform multiple four-wire impedance measurements.

[0069] Example 31 provides a method according to any one of Examples 25-28, wherein each model represents a different possible combination of electrode arrangement and specific anatomical features of a human subject.

[0070] Example 32 provides the method according to Example 31, wherein a specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

[0071] Example 33 provides a method according to any one of Examples 25-28, wherein multiple impedance measurements are performed at a single excitation frequency.

[0072] Example 34 provides a method according to any one of Examples 25-28, wherein multiple impedance measurements are performed at multiple excitation frequencies.

[0073] Example 35 provides the method of Example 34, further comprising summarizing, generating, and adapting for each excitation frequency.

[0074] Example 36 provides a system for detecting the pulmonary fluid state of a human subject, comprising: a plurality of electrodes on the chest of a human subject; and a chest impedance detection module connected to the electrodes, the system for performing a plurality of impedance measurements on a region of interest to acquire measured impedance data; summarizing a plurality of 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 create a final resistivity estimate of the region of interest.

[0075] Example 37 provides the system described in Example 36, wherein performing multiple impedance measurements includes performing multiple four-wire impedance measurements.

[0076] Example 38 provides the system according to Example 36 or 37, wherein the electrodes are connected to elastic chest straps for attachment around the chest of a human subject to ensure the correct positioning of the electrodes relative to the region of interest.

[0077] Example 39 provides the system described in Example 36 or 37, in which each model represents a different possible combination of electrode arrangement and specific anatomical features of a human subject.

[0078] Example 40 provides the system described in Example 39, wherein a specific anatomical feature includes at least one of the relative size and location of lung tissue, heart tissue, soft tissue, and bone.

[0079] Example 41 provides the system described in Example 36 or 37, wherein the chest impedance detection module is further configured to construct a single sample model using a weighted sum of multiple models based on the fit of each model.

[0080] Example 42 provides the system described in Example 36 or 37, wherein the plurality of electrodes includes fewer than eight electrodes.

[0081] Example 43 provides the system according to Example 36 or 37, further comprising three electrodes on the front of the chest and three of the electrodes on the left side of the chest.

[0082] Example 44 provides the system described in Example 36 or 37, in which multiple impedance measurements are performed at a single excitation frequency.

[0083] Example 45 provides the system described in Example 36 or 37, in which multiple impedance measurements are performed at multiple excitation frequencies.

[0084] Example 46 provides the system described in Example 45, further comprising a chest impedance detection module that performs summarizing, generating, and adapting for each excitation frequency.

[0085] It should be noted that all specifications, dimensions, and relationships outlined herein (e.g., the number of elements, actions, steps, etc.) are presented for illustrative and teaching purposes only. Such information may vary significantly without departing from the spirit of this disclosure or the scope of the appended claims. Specifications apply to only one non-limiting example and should therefore be interpreted as such. In the foregoing description, exemplary embodiments are described with reference to the arrangement of specific components. Various modifications and changes can be made to such embodiments without departing from the scope of the appended claims. The description and drawings should therefore be understood as illustrative, not limiting.

[0086] It should be noted that in many of the examples provided herein, interactions may be described in relation to two, three, four, or more electrical components. However, this is done for clarification and illustrative purposes only. It should be understood that systems can be established in any preferred manner. In line with similar design alternatives, any of the components, modules, and elements shown in the drawings may be combined in various possible configurations, all of which are clearly within the broad scope of this specification. In some cases, one or more functions of a given flowset can be more easily described by referring to only a limited number of electrical elements. It should be understood that the electrical circuits and their teachings in the drawings are readily expandable to accommodate more components, as well as more complex / sophisticated arrangements and configurations. Therefore, the examples provided do not limit the scope or hinder the broad teaching of electrical circuits when potentially applied to countless other architectures.

[0087] Furthermore, it should be noted that any references in this specification to various features (e.g., elements, structures, modules, components, steps, operations, characteristics, etc.) included in "one embodiment," "exemplary embodiment," "embodiment," "another embodiment," "several embodiments," "various embodiments," "other embodiments," or "alternative embodiments" are intended to mean that any such features are included in one or more embodiments of this disclosure, but may or may not be combined in the same embodiment.

[0088] Furthermore, it should be noted that the functions related to the circuit architecture shown in the figures represent only a portion of the possible circuit architecture functions that may be performed by or within the system shown. Some of these operations may be deleted or removed as necessary, or may be substantially modified or altered without departing from the scope of this disclosure. In addition, the timing of these operations may be substantially altered. The aforementioned operation flows are presented for illustrative and discussion purposes only. Substantial flexibility is provided by the embodiments described herein in that any preferred arrangement, sequence of events, configuration, and timing mechanism may be provided without departing from the teachings of this disclosure.

[0089] Many other changes, substitutions, modifications, alterations and modifications may be apparent to those skilled in the art, and this disclosure is intended to encompass all such changes, substitutions, modifications, alterations and modifications as being included within the scope of the appended claims.

[0090] It should be noted that all of the features of the devices and systems described above may also be implemented in relation to the methods or processes described herein, and that the details of the embodiments may be used in any one or more embodiments.

[0091] In these (above) examples, “means for ~” may include, but is not limited to, the use of any suitable components discussed herein, along with any suitable software, circuitry, hubs, computer code, logic, algorithms, hardware, controllers, interfaces, links, buses, communication paths, etc.

[0092] It should be noted that in the examples provided above, and many other examples provided herein, interactions may be described with respect to two, three, or four network elements. However, this is done for clarification and illustrative purposes only. In some cases, one or more functions of a given flowset can be more easily described 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 more components, as well as more complex / sophisticated arrangements and configurations. Therefore, the examples provided are not intended to limit the scope or hinder the extensive teaching of the illustrated topologies when potentially applied to countless other architectures.

[0093] Furthermore, it is important to note that the steps in the aforementioned flowchart illustrate only a portion of the possible signaling scenarios and patterns that may be performed by or within the communication system shown in the diagram. Some of these steps may be deleted or removed as necessary, or they may be substantially modified or altered without departing from the scope of this disclosure. In addition, some of these operations are described as being performed simultaneously with or in parallel with one or more additional operations. However, the timing of these operations may be substantially altered. The aforementioned operation flow is presented for illustrative purposes and for consideration. Substantial flexibility is provided by the communication system shown in the diagram in that any preferred arrangement, sequence of events, configuration, and timing mechanism may be provided without departing from the teachings of this disclosure.

[0094] While this disclosure is described in detail with reference to specific configurations and configurations, these exemplary configurations and configurations may be substantially modified without departing from the scope of this disclosure. For example, while this disclosure is described with reference to specific communication exchanges, embodiments described herein may be applicable to other architectures.

[0095] Many other changes, substitutions, variations, alterations and modifications may be apparent to those skilled in the art, and this disclosure is intended to encompass all such changes, substitutions, variations, alterations and modifications as being included within the scope of the attached claims. For the benefit of the United States Patent and Trademark Office (USPTO) and, additionally, any reader of any patent issued in connection with this application in interpreting the attached claims herein, the applicant wishes to be deemed to have (a) not intended to exercise any of the attached claims in any way that is not reflected in the attached claims unless the words “means for” or “steps for” are specifically used in a particular claim, and (b) not intended to limit this disclosure in any way that is not otherwise reflected in the attached claims by any statement herein.

Claims

1. A method for detecting the pulmonary fluid state of a human subject, wherein the pulmonary fluid state is represented by the pulmonary resistivity, and the method is Performing multiple impedance measurements on a region of interest using a measurement module connected to multiple electrodes to acquire measurement impedance data, wherein the multiple electrodes are configured to be in contact with the skin of the chest of the human subject. The processor connected to the measurement module compares the measured impedance data with simulated impedance data obtained from a plurality of models of the region of interest, each of which represents a different possible combination of electrode arrangement and specific anatomical features of the human subject, wherein the specific anatomical features include at least one of the relative size or location of lung tissue, cardiac tissue, soft tissue, and bone. For each of the aforementioned multiple models, the processor determines the degree of fit of the model based on a comparison between the simulated impedance data obtained from the model and the measured impedance data. The processor determines a better-fitting model and a worse-fitting model based on the model's suitability. A method comprising the processor integrating individual resistivity estimates obtained from the plurality of models such that the individual resistivity estimates from the better-fitting model are weighted more heavily in the final resistivity estimate than the individual resistivity estimates from the worse-fitting model.

2. The method according to claim 1, wherein performing multiple impedance measurements includes performing multiple four-wire impedance measurements.

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

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

5. The method according to claim 4, wherein the weights assigned to a particular model among the plurality of models are defined by the inverse value of the residual cost function value of the optimization for solving the inverse problem, and the residual cost function value corresponds to the particular model.

6. The method according to any one of claims 1 to 3, further comprising constructing a single sample model using a weighted sum of the plurality of models based on the fitness of each of the aforementioned models.

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

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

9. The method according to claim 8, further comprising performing the comparison, determination, and integration for each of the excitation frequencies.

10. A system for detecting the pulmonary fluid state of a human subject, wherein the pulmonary fluid state is represented by the pulmonary resistivity, and the system is Multiple electrodes on the chest of the aforementioned human subject, A chest impedance measurement module connected to the plurality of electrodes, wherein the chest impedance measurement module is configured to perform a plurality of impedance measurements on a region of interest in order to acquire measured impedance data, Connected to the aforementioned chest impedance measurement module, The measured impedance data is compared with simulated impedance data obtained from multiple models of the region of interest, each of which represents different possible combinations of electrode arrangement and specific anatomical features of the human subject, wherein the specific anatomical features include at least one of the relative size or location of lung tissue, cardiac tissue, soft tissue, and bone. For each of the aforementioned multiple models, the degree of fit of the model is determined based on a comparison between the simulated impedance data obtained from the model and the measured impedance data. Based on the degree of suitability of the aforementioned model, a better-suited model and a worse-suited model are determined, and A system comprising: a processor configured to integrate individual resistivity estimates obtained from a plurality of models such that individual resistivity estimates from the better-fitting model are weighted more heavily than individual resistivity estimates from the worse-fitting model in the final resistivity estimate.

11. The system according to claim 10, wherein performing multiple impedance measurements includes performing multiple four-wire impedance measurements.

12. The system according to claim 10 or 11, wherein the electrode is connected to an elastic chest strap for attachment around the chest of the human subject to ensure the correct position of the electrode relative to the region of interest.

13. The system according to claim 10 or 11, wherein the final resistivity estimate is a weighted average of the individual resistivity estimates.

14. The system according to claim 13, wherein the weights assigned to a particular model among the plurality of models are defined by the inverse value of the residual cost function value of the optimization for solving the inverse problem, and the residual cost function value corresponds to the particular model.

15. The system according to claim 10 or 11, wherein the chest impedance measurement module is further configured to construct a single sample model using a weighted sum of the plurality of models based on the fit of each of the models.

16. The system according to claim 10 or 11, wherein the plurality of electrodes include fewer than eight electrodes.

17. The system according to claim 10 or 11, wherein the plurality of electrodes further comprises three electrodes on the front of the chest and three of the electrodes on the left side of the chest.

18. The system according to claim 10 or 11, wherein the plurality of impedance measurements are performed at a single excitation frequency.

19. The system according to claim 10 or 11, wherein the plurality of impedance measurements are performed at a plurality of excitation frequencies.

20. The system according to claim 19, wherein the chest impedance measurement module is further configured to perform the comparison, determination, and integration for each of the excitation frequencies.

Images