A method and system for measuring chest impedance based on multi-source information denoising

Abstract

This invention relates to a method and system for measuring chest impedance based on multi-source information denoising. The measurement method includes the following steps: simultaneously acquiring chest impedance data, cardiac data, and acceleration data during the respiratory process of the subject; performing baseline drift removal processing on the chest impedance data to obtain first data; processing the first data and cardiac data using correlation operations to remove cardiac noise signals from the first data to obtain second data; searching for motion-related signal segments in the second data based on the acceleration data, generating a fitting curve using numerical fitting, and replacing the corresponding signal segments with the fitting curve to obtain the final chest impedance signal after removing motion noise. Compared with existing technologies, this invention is applicable even when the noise frequency is close to the respiratory frequency, and has advantages such as accurate identification of acceleration interference and precise noise reduction.

Description

Technical Field

[0001] This invention belongs to the field of measurement technology and relates to a method and system for measuring chest impedance that can realize lung ventilation detection, and in particular to a method and system for measuring chest impedance based on multi-source information denoising. Background Technology

[0002] Humans breathe through their lungs, exchanging gases with the external environment to maintain the oxygen levels needed for metabolism and expel carbon dioxide. Parameters such as respiratory rate, respiratory depth, and pulmonary blood perfusion can reflect the health of the lungs and the intensity of exercise / work. Therefore, monitoring respiration is of great significance in fields such as healthcare and sports, and the social demand for wearable respiratory monitoring devices is becoming increasingly widespread.

[0003] During human respiration, the air content in the lungs changes, and the electrical conductivity of lung tissue changes accordingly. Higher air content results in lower conductivity. Therefore, the impedance measured from the chest is sensitive to the lung inflation status, and lung ventilation can be monitored by measuring chest impedance. Impedance pneumography (IP) is a non-invasive method for detecting lung ventilation by measuring changes in chest impedance. Compared to clinically used spirometers and pulmonary function instruments that require a monitoring tube for exhalation and inspiration, IP does not require a monitoring tube for exhalation and inspiration, offering significant advantages such as not increasing respiratory resistance and eliminating the risk of cross-infection. It is more suitable for widespread application in lung ventilation monitoring, especially in portable wearable devices.

[0004] However, the transthoracic impedance method is currently only used clinically for respiratory rate monitoring. Its application in lung ventilation monitoring is limited by several key factors, with noise suppression being a critical one, affecting the stability of lung ventilation monitoring. Because transthoracic impedance is sensitive to multiple factors, the thoracic impedance signal contains not only lung ventilation-related information but may also include cardiac-related information; movement can also affect the thoracic impedance value. Therefore, the noise suppression problem must be solved when using the thoracic impedance method to detect lung ventilation.

[0005] Existing denoising algorithms for thoracic impedance signals used in lung ventilation measurement mainly fall into three categories: First, methods such as polynomial fitting are used to remove baseline drift. For example, in the applicant's patent CN112022123B (A Pulmonary Function Measurement System Based on Thoracic Impedance), wavelet filtering and smoothing are applied to the thoracic impedance signal, and polynomial fitting is used to remove baseline drift to achieve interference removal. Second, frequency domain filtering algorithms are used based on spectral characteristics to remove noise signals that differ significantly from the respiratory rate, primarily targeting cardiac noise. Third, thresholding methods are used empirically to remove small-amplitude random fluctuations, primarily targeting motion noise. However, these methods still suffer from poor performance when the noise frequency is close to the respiratory rate, or they fail when the noise amplitude increases. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art by providing a chest impedance measurement method and system based on multi-source information denoising that can effectively remove the influence of interference sources and has high accuracy.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for measuring chest impedance based on multi-source information denoising includes the following steps:

[0009] Simultaneously acquire chest impedance data, cardiac data, and acceleration data of the subject during the respiratory process;

[0010] The chest impedance data is subjected to baseline drift removal processing to obtain the first data;

[0011] The first data and cardiac data are processed using relevant operations to remove cardiac noise signals from the first data, thereby obtaining the second data;

[0012] Based on the acceleration data, search for motion-related signal segments in the second data, generate a fitting curve using numerical fitting, and replace the corresponding signal segments with the fitting curve to obtain the final chest impedance signal after removing motion noise.

[0013] Furthermore, the process of acquiring the cardiac noise signal specifically includes:

[0014] The first data and the heart rate data are cross-correlated to obtain the peak value A1.

[0015] Autocorrelation analysis was performed on the heart rate data to obtain the peak value A2.

[0016] The cardiac noise signal coupled to the chest impedance signal is calculated based on the peak values ​​A1 and A2.

[0017] Furthermore, the formula for calculating the cardiac noise signal is as follows:

[0018]

[0019] Among them, {Z H [n]} represents the cardiac noise signal, and H[n] represents the cardiac data.

[0020] Furthermore, the search for the signal segment specifically includes:

[0021] Starting from time 0, the amplitude of the acceleration data is thresholded. When the amplitude at a certain time is greater than the set threshold, it is determined that an action has occurred. The start and end times of the amplitude being greater than the set threshold are recorded, and a segment of the second data with the start and end times as endpoints is obtained as the signal segment.

[0022] Iterate through all acceleration data to obtain all signal segments.

[0023] Furthermore, the generation of the fitted curve specifically includes:

[0024] For each signal segment, several data points outside the signal segment are extracted from the two endpoints of the signal segment, one forward and one backward. The data points and the two endpoints are used as the basic data for fitting to generate a fitting curve.

[0025] Furthermore, the chest impedance data are processed to remove baseline drift using a polynomial fitting method.

[0026] Furthermore, the measurement frequency used when simultaneously acquiring the chest impedance data, cardiac data, and acceleration data is greater than or equal to 20Hz.

[0027] Furthermore, the method also includes:

[0028] After obtaining the first data, the first data is subjected to low-pass filtering.

[0029] The present invention also provides a chest impedance measurement system based on multi-source information denoising, comprising:

[0030] At least one set of chest impedance measurement electrode group, which includes multiple measuring electrodes, wherein the line connecting each measuring electrode in each set of chest impedance measurement electrode group passes through the center of the lung region to be measured.

[0031] At least one accelerometer, and each of the accelerometers is located at least one of the measuring electrodes;

[0032] At least one heart rate sensor is located near the chest;

[0033] The measuring device, which is connected to the chest impedance measuring electrode group, the accelerometer and the cardiac sensor respectively, includes one or more processors, a memory and one or more programs stored in the memory, the one or more programs including instructions for executing the chest impedance measurement method based on multi-source information denoising as described above.

[0034] Furthermore, the measuring electrodes, accelerometer, and heart rate sensor are arranged on the measuring strip.

[0035] Furthermore, the measuring electrodes, accelerometer, and heart rate sensor are mounted on the measuring belt via slip rings.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. This invention simultaneously acquires multi-source data including chest impedance data, cardiac data, and acceleration data. Based on the multi-source data, the chest impedance data is denoised. Compared with the method of only measuring chest impedance signals, this can provide a basis for accurate noise reduction and is more conducive to accurate noise reduction. It is also applicable when the noise frequency is close to the respiratory frequency.

[0038] 2. This invention combines cross-correlation and autocorrelation techniques to accurately extract cardiac interference components coupled into the chest impedance signal. Compared to existing methods that only use frequency domain filtering to remove chest impedance noise, this invention can accurately remove cardiac noise signals, and in particular, it solves the problem that the performance of frequency domain filtering algorithms degrades or even becomes ineffective when the noise frequency differs from the signal frequency by less than four times.

[0039] 3. Based on measured acceleration signals, this invention uses a threshold to determine the time period of the action, which solves the problem that existing methods based on the local jump amplitude of chest impedance signals to determine motion disturbances when the motion amplitude is too large, mistakenly taking motion noise as a breathing signal. This invention can more accurately identify segments of chest impedance that are disturbed by acceleration, making it convenient to perform noise reduction processing on the chest impedance signal of these segments.

[0040] 4. After acquiring the segment affected by acceleration interference, this invention performs numerical fitting based on the data points before and after the interference segment, replacing the original interfered chest impedance data with points on the fitted curve, effectively removing the influence of motion noise. Compared with existing methods, it can more accurately fit the chest impedance curve, thereby improving measurement accuracy.

[0041] 5. This invention lays the foundation for applying the thoracic impedance lung ventilation detection method to more scenarios with motion noise, and has important application value and good application prospects in the diagnosis of pulmonary function diseases. Attached Figure Description

[0042] Figure 1 This is a flowchart of the chest impedance measurement process according to an embodiment of the present invention;

[0043] Figure 2 This is a flowchart illustrating the process of removing cardiac noise according to an embodiment of the present invention;

[0044] Figure 3 This is a flowchart illustrating the process of removing motion noise according to an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the electrode and sensor measurement strip provided in an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram showing the electrode and sensor wearing positions when measuring chest impedance according to an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of the measurement system according to an embodiment of the present invention;

[0048] Figure 7 The original chest impedance measurement value provided in the embodiments of the present invention;

[0049] Figure 8 A schematic diagram of cardiac interference components coupled to the thoracic impedance provided in an embodiment of the present invention;

[0050] Figure 9 This is a schematic diagram of a clean thoracic impedance signal reflecting lung ventilation after noise reduction, provided in an embodiment of the present invention. Detailed Implementation

[0051] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment provides a method for measuring chest impedance based on multi-source information denoising, including the following steps:

[0054] S1. Simultaneously acquire chest impedance data and interference data during the subject's breathing process. Interference data includes cardiac data and acceleration data, recorded as chest impedance sequence {Z0[n]}, acceleration signal sequence {M[n]}, and cardiac signal sequence {H[n]}. The original chest impedance measurements are as follows: Figure 7 As shown.

[0055] In this embodiment, the measurement frequency used when simultaneously acquiring chest impedance data, cardiac data, and acceleration data is not less than 20Hz.

[0056] S2. Perform baseline drift removal processing on the chest impedance sequence {Z0[n]} to obtain the first data {Z1[n]}.

[0057] In a specific embodiment, a polynomial fitting method is used, but not limited to, to remove baseline drift in the chest impedance signal.

[0058] S3. Low-pass filtering is used to remove high-frequency noise in {Z1[n]} that is significantly higher than human gait and heart rate, and the chest impedance signal {Z2[n]} after initial noise reduction is obtained.

[0059] S4. The chest impedance signal {Z2[n]} and the cardiac signal sequence {H[n]} are processed by correlation operation to remove cardiac noise signal and obtain the second data {Z3[n]}.

[0060] refer to Figure 2 As shown, in this embodiment, step S4 specifically includes:

[0061] S401. Perform cross-correlation calculation between the chest impedance signal {Z2[n]} after initial noise reduction and the cardiac signal {H[n]} to obtain the peak value A1;

[0062] S402. Perform autocorrelation estimation on the measured cardiac signal {H[n]} to obtain the peak value A2;

[0063] S403. Calculate the cardiac noise signal coupled in the chest impedance signal. Components of emotional attraction, such as Figure 8 As shown;

[0064] S404, Remove cardiac noise {Z2[n]} from {Z2[n]} H [n]}, thus obtaining the cardiac impedance signal {Z3[n]} = {Z2[n] - Z H [n]}.

[0065] S5. Based on the acceleration data, search for motion-related signal segments in {Z3[n]}, generate fitting curves using numerical fitting, and replace the corresponding signal segments with the fitting curves to obtain the final chest impedance signal {Z4[n]} after removing motion noise. The noise reduction process is complete. The clean chest impedance value reflecting lung ventilation after noise reduction is as follows: Figure 9 As shown.

[0066] The chest impedance signal {Z4[n]} obtained by the above method can be used for lung ventilation measurement, improving measurement accuracy.

[0067] refer to Figure 3 As shown, in this embodiment, step S5 specifically includes:

[0068] S501, Set the acceleration threshold h;

[0069] S502. Perform threshold judgment on the amplitude of the acceleration data. If the amplitude at a certain moment exceeds a set threshold, then an action is determined to have occurred. Record the start time T[S(i)] and end time T[E(i)] when the amplitude exceeds the set threshold, i.e., search for the segment {M[n]} where {M[n]} > h. k , and obtain a segment of {Z3[n]} with the above start time and end time as the endpoints as the signal segment;

[0070] S503. Extract several data points outside the signal segment from the two endpoints of the signal segment, one forward and one backward, and use the data points and the two endpoints as the basic data for fitting.

[0071] S504, Generate the fitting curve f k (t);

[0072] S505. Replace the corresponding signal segment with the fitted curve to form a new chest impedance fitted value: Z4[t]=f k (t);

[0073] S506. Determine whether all data has been processed. If yes, end the process; otherwise, return to step S502.

[0074] In step S503 above, two data points are extracted forward and two backward, resulting in a total of six chest impedance data points including the endpoints. Numerical fitting is then performed, and the fitted curve is used to replace the removed chest impedance sampling point values ​​to obtain a chest impedance signal with motion noise removed.

[0075] The above-mentioned synchronous sampling of the chest impedance signal and interference sources during the respiratory process of the subject, such as cardiac signals and acceleration, is used to help remove noise from the chest impedance signal. This includes using cross-correlation to remove cardiac noise from the chest impedance signal and using threshold judgment and interpolation to remove motion noise from the chest impedance. Finally, a clean chest impedance signal reflecting the lung ventilation status is obtained, realizing the monitoring of the lung ventilation of the subject with high measurement reliability.

[0076] If the above methods are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] Example 2

[0078] This embodiment provides a chest impedance measurement system based on multi-source information denoising, comprising: at least one set of chest impedance measurement electrode groups, each set including multiple measurement electrodes, wherein the connecting lines of the measurement electrodes in each set pass through the center of the lung region to be measured; at least one accelerometer, each accelerometer being close to at least one measurement electrode; at least one cardiac sensor located near the chest; and a measuring device connected to the chest impedance measurement electrode groups, the accelerometer, and the cardiac sensor, including one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the chest impedance measurement method based on multi-source information denoising as described in Embodiment 1.

[0079] In this embodiment, there are two sets of chest impedance measurement electrode groups. Each set of chest impedance measurement electrode groups includes four measuring electrodes, which are respectively attached to the front and back of the chest, corresponding to four regions of the lungs, including the left upper lung, left lower lung, right upper lung, and right lower lung, to perform chest impedance measurement in the four regions.

[0080] like Figure 4 As shown, measuring electrodes 4, an accelerometer, and a cardiac sensor are arranged on measuring band 1. The accelerometer and cardiac sensor together form an acceleration module 0, located at the center of the measuring band for easy installation on the subject's chest. Measuring band 1 is made of elastic band and is 5cm wide. Hook and loop fasteners 3 are added to the beginning and end of measuring band 1, allowing the length of the electrode band to be adjusted according to the subject's body shape, so that it can better adhere to the chest cavity surface.

[0081] Furthermore, the measuring electrodes, accelerometer, and heart rate sensor are mounted on the measuring belt 1 via slip ring 2, allowing for free adjustment of the positions of the measuring electrodes and sensor modules.

[0082] In this embodiment, the measuring electrode is a composite electrode with a length and width of 2.4cm ± 0.1cm. The cardiac sensor is a photoelectric cardiac sensor, using, but not limited to, the MAXM86161 sensor, which acquires the cardiac signal of the subject by detecting changes in reflected light from blood flow during each pleural perfusion. The accelerometer is a capacitive accelerometer, using, but not limited to, the LSM6DSO32 six-axis sensor, which acquires the linear acceleration of the subject in the X / Y / Z axes and the rotational acceleration around the three axes by detecting changes in capacitance along the X / Y / Z axes.

[0083] In this embodiment, as Figure 6 As shown, the measuring device includes a bioelectrical impedance measurement circuit 5, a synchronization clock module 7, and a controller 6. The measuring electrodes are connected to the bioelectrical impedance measurement unit. A weak excitation current (0.95mA in this embodiment) is applied to the human body through the excitation electrodes, and the response voltage is measured through the measuring electrodes to obtain the chest impedance signal of the subject. When there are multiple pairs of electrodes, the chest impedance measurement unit quickly switches between multiple electrode pairs to achieve approximately synchronous measurement of chest impedance of multiple channels. The measuring electrodes, accelerometer, and cardiac sensor perform periodic measurements under the control of the synchronization clock module. The measurement frequency is not less than 20Hz. After the controller sends a command, they are started synchronously to achieve synchronous acquisition of the three signals, obtaining the chest impedance sequence {Z0[n]}, the acceleration signal sequence {M[n]}, and the cardiac signal sequence {H[n]}.

[0084] The process of measuring chest impedance using the aforementioned chest impedance measurement system based on multi-source information denoising includes:

[0085] (1) As Figure 5 As shown, a measuring electrode band with two composite electrodes is tied under the armpit of the subject. The Velcro is adjusted to make the electrode band fit tightly against the skin. The position of the measuring electrodes is adjusted by sliding the collar so that the chest impedance measuring electrodes are located under the armpits on both sides. The electrode connection line crosses the chest cavity area, and the accelerometer and heart rate sensor are close to the chest.

[0086] (2) The measurement system starts measuring, simultaneously measuring chest impedance, acceleration and cardiac signal to obtain chest impedance sequence {Z0[n]}, acceleration signal sequence {M[n]} and cardiac signal sequence {H[n]}. The measurement frequency is greater than 20Hz, preferably 128Hz.

[0087] (3) Remove the baseline drift from the measured chest impedance signal {Z0[n]} to obtain the chest impedance signal {Z1[n]};

[0088] (4) Further, a low-pass filter is used to remove high-frequency noise that is significantly higher than human walking frequency and heart rate, preferably frequency components above 10Hz, to obtain the chest impedance signal {Z2[n]} after the first noise reduction;

[0089] (5) Perform cross-correlation calculation on the chest impedance signal {Z2[n]} after initial noise reduction and the cardiac signal {H[n]} to obtain the peak value A1. Perform autocorrelation calculation on the measured cardiac signal {H[n]} to obtain the peak value A2. Calculate the cardiac noise coupled in the chest impedance signal. Remove cardiac noise {Z2[n]} from {Z2[n]} H [n]}, thus obtaining the cardiac impedance signal {Z3[n]} = {Z2[n] - Z H [n]};

[0090] (6) Starting from time 0, the amplitude of the acceleration signal sequence {M[n]} is judged by a threshold. When the amplitude is greater than the threshold h, preferably, the threshold is set to h = 0.1 m / s. 2 The action is assumed to have occurred. The start time T[S(k)] and end time T[E(k)] when the amplitude is greater than the threshold are recorded. The six points in the chest impedance signal {Z3[n]} from time T[S(k)]-2 to time T[E(k)]+2 are fitted with a 6th order 5th degree polynomial. The fitted curve is used to replace Z3[n] between time T[S(k)] and time T[E(k)]. This process is repeated until all points with acceleration amplitude greater than the threshold have been processed. Finally, the chest impedance data series {Z4[n]} reflecting lung ventilation is obtained and used for lung ventilation measurement. The noise reduction process is then completed.

[0091] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for measuring chest impedance based on multi-source information denoising, characterized in that, Includes the following steps: Simultaneously acquire chest impedance data, cardiac data, and acceleration data of the subject during the respiratory process; The chest impedance data is subjected to baseline drift removal processing to obtain the first data; The first data and cardiac data are processed using relevant operations to remove cardiac noise signals from the first data, thereby obtaining the second data; Based on the acceleration data, search for motion-related signal segments in the second data, generate a fitting curve using numerical fitting, and replace the corresponding signal segments with the fitting curve to obtain the final chest impedance signal with motion noise removed. The process of acquiring the cardiac noise signal specifically includes: The first data and the heart rate data are cross-correlated to obtain the peak value A1. Autocorrelation analysis was performed on the heart rate data to obtain the peak value A2. Based on the peak values ​​A1 and A2, calculate the cardiac noise signal coupled to the chest impedance signal: Among them, {Z H [n]} represents the cardiac noise signal, and H[n] represents the cardiac data.

2. The chest impedance measurement method based on multi-source information denoising according to claim 1, characterized in that, The search for the signal segment specifically includes: Starting from time 0, the amplitude of the acceleration data is thresholded. When the amplitude at a certain time is greater than the set threshold, it is determined that an action has occurred. The start and end times of the amplitude being greater than the set threshold are recorded, and a segment of the second data with the start and end times as endpoints is obtained as the signal segment. Iterate through all acceleration data to obtain all signal segments.

3. The chest impedance measurement method based on multi-source information denoising according to claim 1, characterized in that, The generation of the fitted curve specifically includes: For each signal segment, several data points outside the signal segment are extracted from the two endpoints of the signal segment, one forward and one backward. The data points and the two endpoints are used as the basic data for fitting to generate a fitting curve.

4. The chest impedance measurement method based on multi-source information denoising according to claim 1, characterized in that, The baseline drift of the chest impedance data was removed using a polynomial fitting method.

5. The chest impedance measurement method based on multi-source information denoising according to claim 1, characterized in that, The measurement frequency used when simultaneously acquiring the chest impedance data, cardiac data, and acceleration data is greater than or equal to 20Hz.

6. The chest impedance measurement method based on multi-source information denoising according to claim 1, characterized in that, The method also includes: After obtaining the first data, the first data is subjected to low-pass filtering.

7. A chest impedance measurement system based on multi-source information denoising, characterized in that, include: At least one set of chest impedance measurement electrode group, which includes multiple measuring electrodes, wherein the line connecting each measuring electrode in each set of chest impedance measurement electrode group passes through the center of the lung region to be measured. At least one accelerometer, and each of the accelerometers is located at least one of the measuring electrodes; At least one heart rate sensor is located near the chest; The measuring device, which is connected to the chest impedance measuring electrode group, the accelerometer and the cardiac sensor respectively, includes one or more processors, a memory and one or more programs stored in the memory, the one or more programs including instructions for executing the chest impedance measurement method based on multi-source information denoising as described in any one of claims 1-6.

8. The chest impedance measurement system based on multi-source information denoising according to claim 7, characterized in that, The measuring electrodes, accelerometer, and heart rate sensor are arranged on the measuring strip.

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