A cardiopulmonary activity signal separation method, system, device and storage medium

Abstract

The present invention provides a cardiopulmonary activity signal separation method, system, device and storage medium, which belongs to the field of bioelectrical impedance tomography, and the method includes the following steps: obtaining multiple cardiopulmonary activity signal impedance measurement channel data of a subject, and dividing the cardiopulmonary activity signal impedance measurement channel into a cardiac ejection activity strong correlation channel and a cardiac ejection activity weak correlation channel according to whether the cardiac activity signal strength contained in the breath-holding segment impedance signal in each measurement channel data is greater than a set threshold; decomposing the impedance data of the natural breathing segment in each measurement channel data to obtain multiple IMF components, extracting all IMF components related to pulmonary ventilation from the multiple IMF components, and obtaining a pulmonary ventilation reconstruction signal; subtracting the original measurement data from the corresponding pulmonary ventilation reconstruction signal to obtain a cardiac ejection activity related signal. The present invention can more accurately separate the cardiac ejection activity related impedance signal and the pulmonary ventilation related impedance signal.

Description

Technical Field

[0001] The present invention belongs to the field of bioelectrical impedance imaging, and in particular relates to a cardiopulmonary activity signal separation method, system, device and storage medium. Background Art

[0002] Electrical impedance tomography (EIT) is a new type of medical imaging technology that uses tissue conductivity distribution or changes as imaging targets. It applies a safe excitation signal to the human body, measures the boundary voltage generated by the excitation, and uses the corresponding imaging algorithm to obtain the internal impedance distribution of the human body. EIT has been used for functional imaging related to physiological changes such as breathing. Since the amplitude of the electrical impedance signal related to cardiac ejection activity is less than 5% of the amplitude of the electrical impedance signal of pulmonary ventilation, and there is a certain spectral aliasing between the electrical impedance signals related to pulmonary ventilation and cardiac ejection activity, it is very difficult to separate the electrical impedance signals related to pulmonary ventilation and cardiac ejection activity.

[0003] When processing EIT data, traditional signal separation methods tend to first convert the EIT signal mixed with pulmonary ventilation and cardiac ejection activity into an image, and then try to separate the cardiac and respiratory related signal components through various methods based on the reconstructed image. However, the problem with these methods is that the expression of cardiac signals in EIT images may be interfered by many factors, such as changes in chest volume caused by pulmonary ventilation, electrode layout, tissue structure, and limitations of the image reconstruction algorithm itself, making it difficult to accurately parse cardiac signals from the reconstructed EIT images. Summary of the invention

[0004] In order to overcome the above-mentioned shortcomings of the prior art, the present invention provides a cardiopulmonary activity signal separation method, comprising the following steps:

[0005] Acquire multiple cardiopulmonary activity signal impedance measurement channel data of the subject, and determine whether the strength of the cardiac activity signal contained in the breath-holding segment impedance signal in each measurement channel data is greater than a set threshold value. If so, the cardiopulmonary activity signal impedance measurement channel is a strongly correlated channel for cardiac ejection activity; if not, the cardiopulmonary activity signal impedance measurement channel is a weakly correlated channel for cardiac ejection activity;

[0006] The EEMD algorithm is used to decompose the electrical impedance data of the natural breathing segment in the strong correlation channel and the weak correlation channel of the cardiac ejection activity to obtain multiple intrinsic mode function IMF components;

[0007] All IMF components related to pulmonary ventilation are extracted from multiple IMF components through spectrum analysis to obtain pulmonary ventilation reconstruction signals;

[0008] The original measurement data in each measurement channel data is subtracted from the lung ventilation reconstruction signal of the corresponding channel to obtain a cardiac ejection activity related signal.

[0009] Preferably, before extracting all IMF components related to pulmonary ventilation from the multiple IMF components through spectrum analysis, the method further includes performing Fourier transform on the multiple IMF components to convert the IMF components into frequency domain signals.

[0010] Preferably, the method of extracting all IMF components related to pulmonary ventilation from multiple IMF components through spectral analysis to obtain a pulmonary ventilation reconstruction signal is specifically: comparing the spectra of multiple IMF components converted into frequency domain signals with the respiratory frequency range of a healthy adult to obtain all IMF components related to pulmonary ventilation, and accumulating and reconstructing them to obtain a pulmonary ventilation reconstruction signal.

[0011] Preferably, after subtracting the original measurement data in each measurement channel data from the obtained pulmonary ventilation reconstruction signal of the corresponding channel to obtain the cardiac ejection activity related signal, it also includes performing dimensionality reduction processing on the cardiac ejection activity related signal, specifically: setting all cardiac ejection activity weakly correlated channel signals in the obtained cardiac ejection activity related signal to zero.

[0012] Preferably, after subtracting the original measurement data in each measurement channel data from the obtained pulmonary ventilation reconstruction signal of the corresponding channel, the method further includes performing noise reduction processing on the obtained cardiac ejection activity related signal, specifically: using bandpass filtering to remove high-frequency noise in the cardiac ejection activity related signal.

[0013] Preferably, after using bandpass filtering to remove high-frequency noise in the cardiac ejection activity signal, the method also includes using an EIT reconstruction algorithm to image the cardiac ejection activity-related signals and the pulmonary ventilation reconstruction signals to obtain an image sequence reflecting the cardiac ejection activity and an image sequence reflecting the pulmonary ventilation activity.

[0014] The present invention also provides a cardiopulmonary activity signal separation system, comprising:

[0015] The signal channel classification module is used to obtain multiple cardiopulmonary activity signal impedance measurement channel data of the subject, and determine whether the cardiac activity signal strength contained in the breath-holding segment impedance signal in each measurement channel data is greater than a set threshold value. If so, the cardiopulmonary activity signal impedance measurement channel is a strongly correlated channel for cardiac ejection activity; if not, the cardiopulmonary activity signal impedance measurement channel is a weakly correlated channel for cardiac ejection activity;

[0016] A signal decomposition module is used to decompose the electrical impedance data of the natural breathing segment in the strong correlation channel of cardiac ejection activity and the weak correlation channel of cardiac ejection activity by using an ensemble empirical mode decomposition (EEMD) algorithm to obtain multiple intrinsic mode function (IMF) components;

[0017] A pulmonary ventilation signal recognition module is used to extract all IMF components related to pulmonary ventilation from multiple IMF components through spectrum analysis to obtain a pulmonary ventilation reconstruction signal;

[0018] The cardiac ejection signal acquisition module is used to subtract the original measurement data in each measurement channel data from the obtained pulmonary ventilation reconstruction signal of the corresponding channel to obtain a cardiac ejection activity related signal.

[0019] The present invention also provides a computer device, comprising a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the cardiopulmonary activity signal separation method.

[0020] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the cardiopulmonary activity signal separation method.

[0021] The cardiopulmonary activity signal separation method, system, device and storage medium provided by the present invention have the following beneficial effects:

[0022] The present invention firstly conducts an in-depth analysis of the data in the breath-holding state, and classifies the electrical impedance measurement channels of the cardiopulmonary activity signals according to the strength differences of the cardiac ejection activity signals in the breath-holding segment, so as to more accurately lock and extract the signal components related to the cardiac ejection activity, thereby avoiding the interference of the pulmonary ventilation activity and other potential noise sources on the cardiac signal detection to a large extent, and improving the accuracy of the decomposition of the cardiac activity signal in the natural breathing state; by adopting the EEMD algorithm, the impedance data of the natural breathing segment in the two types of channels are carefully decomposed, and multiple IMF components are separated therefrom, and by spectral analysis of the multiple IMF components, the electrical impedance signal components related to the pulmonary ventilation are effectively separated and reconstructed, so that the signal components closely related to the cardiac ejection activity and the signal components related to the pulmonary ventilation can be more accurately extracted and identified, thereby providing more objective and accurate data for the evaluation of the heart and lung function activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a cardiopulmonary activity signal separation method according to an embodiment of the present invention;

[0025] Figure 2 Finite element model of the lung with electrode labels and division of the cardiopulmonary region of interest (ROI) used for EIT monitoring; (a) is a schematic diagram of the location of lung electrodes; (b) is a finite element model of the lung and division of the cardiopulmonary region of interest (ROI);

[0026] Figure 3 It is the chest impedance data of natural breathing stage and breath holding stage;

[0027] Figure 4 is the cardiac ejection activity signal intensity of each measurement channel during the breath-holding period;

[0028] Figure 5 The original signal waveform and its corresponding representative spectrum diagram; (a) is the original signal of the third measurement channel; (b) is the spectrum of the signal of the third measurement channel; (c) is the original signal of the 12th measurement channel; (d) is the spectrum of the signal of the 12th measurement channel;

[0029] Figure 6 The components decomposed by EEMD under two types of measurement channels and their corresponding representative spectra; (a) is the component decomposed by the third measurement channel; (b) is the spectrum corresponding to each component of the third measurement channel; (c) is the component decomposed by the 12th measurement channel; (d) is the spectrum corresponding to each component of the third measurement channel;

[0030] Figure 7 The pulmonary ventilation-related impedance signal and the cardiac ejection activity-related impedance signal decomposed from the two measurement channels are representative diagrams, as well as the original impedance signals of the respective channels and the corresponding blood oxygen pulse wave (PPG) signals; wherein, (a) is the original impedance signal of the third measurement channel and the separated pulmonary ventilation-related impedance signal; (b) is the original impedance signal of the 12th measurement channel and the separated pulmonary ventilation-related impedance signal; (c) is the separated cardiac ejection activity-related impedance signal and the corresponding PPG of the third measurement channel; (d) is the separated cardiac ejection activity-related impedance signal and the corresponding PPG of the 12th measurement channel;

[0031] Figure 8 These are the two signal means obtained after the EEMD algorithm decomposes all measurement channels; (a) is the mean of the pulmonary ventilation impedance signal; (b) is the mean of the cardiac ejection activity-related impedance signal;

[0032] Fig. 9 The electrical impedance imaging of two signals; (a) is a lung ventilation EIT image sequence of one respiratory cycle; (b) is a cardiac activity EIT image sequence of 4 cycles;

[0033] Fig.10 It is the power spectrum index under three algorithms. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0035] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the technical solutions of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0036] In addition, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise clearly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. In the description of the present invention, unless otherwise specified, "plurality" means two or more, which will not be described in detail here.

[0037] Example

[0038] The present invention provides a method for separating cardiopulmonary activity signals, specifically Figure 1 As shown, the following steps are included:

[0039] Step 1: Obtain multiple cardiopulmonary activity signal impedance measurement channel data of the subject, and determine whether the cardiac activity signal intensity contained in the breath-holding segment impedance signal in each measurement channel data is greater than a set threshold. If so, the cardiopulmonary activity signal impedance measurement channel is a strongly correlated channel for cardiac ejection activity; if not, the cardiopulmonary activity signal impedance measurement channel is a weakly correlated channel for cardiac ejection activity.

[0040] The present invention uses an electrical impedance imaging data acquisition system to collect data from the electrical impedance measurement channel of cardiopulmonary activity signals. Figure 2As shown in (a), 16 electrodes are placed horizontally on the chest of the subject at equal intervals and heights. The subject is connected to the electrical impedance imaging data acquisition system through an electrode cable. The operating frequency of the system is set to 50kHz, the acquisition frame rate is set to 40 frames / s, and a safe current is injected between the 16 pairs of measurement electrodes in sequence, and the potential difference between the 16 adjacent electrodes is measured in sequence, and a total of 256 measurement voltages are obtained, which is called a frame of data. The subject takes a sitting position and collects data in a state of natural breathing for more than 60 seconds and holding the breath at the end of inspiration for about 30 seconds.

[0041] like Figure 3 The EIT chest data was successfully collected for the subjects. Each data set includes the natural breathing and breath-holding impedance signals, where the natural breathing impedance signals are used as the original impedance signals for signal separation. The present invention uses the breath-holding impedance signals as the basis for measuring channel classification, and first performs Fourier transform on them to obtain the intensity of the cardiac ejection activity related signals under all channels, such as Figure 4 The present invention uses 25% as a threshold to distinguish the strength of the channel electrical impedance signal and the cardiac ejection activity, and divides each measurement channel data of the cardiopulmonary activity signal into cardiac ejection activity strong correlation channel data and cardiac ejection activity weak correlation channel data.

[0042] Step 2: Use the ensemble empirical mode decomposition (EEMD) algorithm to decompose the electrical impedance data of the natural breathing segment in the strong correlation channel of cardiac ejection activity and the weak correlation channel of cardiac ejection activity to obtain multiple intrinsic mode function (IMF) components.

[0043] The ensemble empirical mode decomposition was proposed by Wu et al. This method is a multiple empirical mode decomposition of superimposed Gaussian white noise. Different white noises of the same amplitude are added each time to change the extreme point characteristics of the signal. Then, the corresponding IMFs obtained by multiple EMDs are averaged to offset the added white noise, thereby suppressing the generation of modal aliasing to a certain extent. First, EEMD is used to decompose the natural breathing impedance data of the two types of measurement channels, and a series of IMF expressions of different time scales are decomposed as shown in (1):

[0044]

[0045] Where X(t) is a set of EIT measurement data, IMF i (t) is the i-th IMF component, r n (t) is the decomposition residual term. The decomposed IMF components are arranged in descending order of frequency.

[0046] Step 3: Extract all IMF components related to pulmonary ventilation from multiple IMF components through spectrum analysis to obtain pulmonary ventilation reconstruction signals, subtract the original measurement data in each measurement channel data from the obtained pulmonary ventilation reconstruction signals of the corresponding channels to obtain signals related to cardiac ejection activity.

[0047] The normal range of respiratory rate for healthy adults is 12 to 20 times / min, corresponding to a frequency band of 0.20 to 0.33 Hz. All measurement channels were decomposed by EEMD, and then each component was Fourier transformed. According to the spectrum of each component and the range of respiratory rate for healthy adults, all components related to pulmonary ventilation were found, and they were accumulated and reconstructed to obtain the pulmonary ventilation electrical impedance signal.

[0048] Subsequently, the original impedance signal from the strongly correlated cardiac activity channel is subtracted from the pulmonary ventilation impedance signal reconstructed from the corresponding channel to obtain the cardiac ejection activity impedance signal of the corresponding channel. Since the obtained signal usually contains noise, these cardiac ejection activity-related signals reconstructed from the strongly correlated cardiac activity channels are also subjected to a 0.5-3.0 Hz bandpass filter to filter out baseline and high-frequency noise. For the weakly correlated cardiac ejection activity channel, since the noise signal is mainly obtained after removing the pulmonary ventilation impedance signal, it is directly set to zero, thereby achieving data dimensionality reduction and noise reduction.

[0049] The obtained impedance signals related to lung ventilation and cardiac ejection activity in the natural breathing period were subjected to impedance tomography. The lung finite element model used was as follows: Figure 2 as shown in (b).

[0050] The present invention divides all measurement channels into two categories according to the strength of the contained cardiac activity signals: the channels with the top 25% of intensity are channels strongly correlated with cardiac ejection activity, and the rest are weakly correlated channels. Typical channels in these two categories are selected for analysis. Figure 5 is the original signal waveform and its corresponding representative spectrum; where Figure 5 (a) is the original signal of the third measurement channel; Figure 5 (b) is the spectrum of the signal of the third measurement channel; Figure 5 (c) is the original signal of the 12th measurement channel; Figure 5 (d) is the spectrum of the signal of the 12th measurement channel. It can be seen that one of the data is the impedance data and corresponding spectrum of the 3rd measurement channel (strong correlation channel of cardiac ejection activity) and the 12th measurement channel (weak correlation channel of cardiac ejection activity). The 3rd measurement channel contains more cardiac ejection activity information, and its corresponding spectrum is also higher. On the contrary, the 12th measurement channel has almost no cardiac ejection activity information, and its corresponding spectrum is also lower.

[0051] The IMF components decomposed by the EEMD algorithm and their corresponding representative spectra are shown in the figure below. Figure 6 shown. Figure 6 The components of EEMD decomposition under two types of measurement channels and their corresponding representative spectra; Figure 6 (a) is the component decomposed by the third measurement channel; Figure 6 (b) is the spectrum corresponding to each component of the third measurement channel; Figure 6 (c) is the components decomposed by the 12th measurement channel; Figure 6 (d) is the spectrum corresponding to each component of the third measurement channel. According to the frequency band of the respiratory frequency, the signal components related to lung ventilation can be found from the spectrum diagram, such as Figure 6 (a) IMF5, IMF6 and Figure 6 For the frequency band where the heart rate is located, the components related to the cardiac ejection activity can be found from the spectrum diagram, such as Figure 6 For the weakly correlated channels of cardiac ejection activity, it is difficult to find the components related to cardiac ejection activity in the spectrum diagram, such as Figure 6 The IMF3 in (c) is almost entirely noise. Figure 6 (a) and Figure 6 In (c), IMF1 and IMF2 represent high-frequency noise, while IMF7 represents the residual term.

[0052] Figure 7 The EEMD algorithm separates two representative channels of pulmonary ventilation-related impedance signals and cardiac ejection activity-related impedance signals, as well as the original impedance signals of each channel and the corresponding blood oxygen pulse wave (PPG) signals. Figure 7 (a) is the original electrical impedance signal of the third measurement channel and the separated pulmonary ventilation related electrical impedance signal; Figure 7 (b) is the original electrical impedance signal of the 12th measurement channel and the separated pulmonary ventilation related electrical impedance signal; Figure 7 (c) is the electrical impedance signal related to cardiac ejection activity separated by the third measurement channel and the corresponding PPG; Figure 7 (d) is the electrical impedance signal related to cardiac ejection activity separated by the 12th measurement channel and the corresponding PPG. Figure 7 (a) and Figure 7 As can be seen in (b), the pulmonary ventilation impedance signal obtained after the two types of channels are separated is highly consistent with the original impedance signal, and the Pearson correlation coefficients are as high as 0.8819 and 0.9997 respectively. Figure 7 (c) It can be observed that the period of the cardiac ejection activity-related signal separated from the third channel signal is exactly the same as the PPG signal, both of which are 73 periods, but Figure 7In (d), almost no heart-related electrical impedance signal can be observed.

[0053] According to the above algorithm, the global electrical impedance signals related to pulmonary ventilation and cardiac ejection activity of all measurement channels are decomposed as follows: Figure 8 As shown. Among them, Figure 8 (a) is the mean value of pulmonary ventilation impedance signal; Figure 8 (b) is the mean value of the electrical impedance signal related to cardiac ejection activity; Figure 8 The electrical impedance signals related to pulmonary ventilation and the electrical impedance signals related to cardiac ejection activity are imaged periodically to obtain the EIT image sequences of pulmonary ventilation and cardiac ejection activity. Fig. 9 shown. Fig. 9 (a) is a lung ventilation EIT image sequence of one respiratory cycle; Fig. 9 (b) is a 4-cycle EIT image sequence of cardiac activity. In the obtained EIT image, the red area represents impedance decrease (blood inflow area), and the blue area represents impedance increase (gas inflow area). By observing the pulmonary ventilation EIT image sequence, it can be seen that: during inhalation, the impedance increases and the blue color becomes darker, and during exhalation, the impedance decreases and the blue color becomes lighter. By observing the EIT image sequence of cardiac ejection activity, it can be seen that during diastole, the impedance of the heart area decreases and the red color becomes darker, and during systole, the impedance rises and the red color becomes lighter.

[0054] Table 1 shows the statistical data of the retention and suppression of relevant activity information obtained by analyzing the image sequences obtained after the EIT data from 9 healthy volunteers were separated and imaged using different signal separation algorithms (the method established by the present invention, the conventional EMD signal separation algorithm, and the complementary set empirical mode decomposition (CEEMD) algorithm). The data results are listed in the form of mean ± standard deviation. Fig.10 As shown. Among them, Fig.10 The a in the abscissa is the lung ventilation related signal of the lung ROI in the lung ventilation image sequence. Fig.10 The b in the abscissa is the cardiac activity-related signal of the heart ROI in the lung ventilation image sequence. Fig.10 The c in the abscissa is the cardiac activity-related signal of the heart ROI in the cardiac ejection activity image sequence. Fig.10 The d in the abscissa is the pulmonary ventilation-related signal of the lung ROI in the cardiac ejection activity image sequence; (* indicates P<0.05, ** indicates P<0.01, *** indicates P<0.001, **** indicates P<0.0001).

[0055] Table 1 Power spectrum index evaluation of three algorithms

[0056]

[0057] Note: Bold indicates the best indicator.

[0058] According to Table 1 and Fig.10 The statistical results show that in the pulmonary ventilation image sequence, the power spectral density (PSD) of the pulmonary ventilation related signal in the pulmonary ROI area reached (52.71±1.39) dB. Compared with the EMD and CEEMD algorithms, the algorithm established by the present invention showed higher efficiency in retaining the pulmonary ventilation signal. At the same time, in the heart ROI area, the PSD of the cardiac ejection activity related signal was only (10.02±2.65) dB, which was significantly lower than the first two algorithms (P<0.0001, P<0.001), indicating that the algorithm established by the present invention has a significant advantage in suppressing the interference of cardiac ejection activity on the pulmonary ventilation signal.

[0059] In the cardiac ejection activity image sequence, the cardiac ejection activity-related PSD of the heart ROI was as high as (43.05±3.26) dB, showing better cardiac ejection activity signal retention performance compared with the EMD and CEEMD algorithms, and the difference was statistically significant (P<0.0001, P<0.01). For the lung ROI area, the pulmonary ventilation-related PSD was not significantly different from that of the first two algorithms, indicating that the algorithm established by the present invention is no less effective than the conventional algorithm in suppressing pulmonary ventilation activity during cardiac activity observation.

[0060] In summary, the algorithm established in the present invention demonstrates excellent performance in separating and extracting pulmonary ventilation and cardiac ejection activity signals. It can not only effectively retain the target signal, but also effectively suppress the interference of irrelevant signals, providing more accurate data support for subsequent cardiopulmonary function evaluation.

[0061] The present invention also provides a cardiopulmonary activity signal separation system, comprising a signal channel classification module, a signal decomposition module, a pulmonary ventilation signal recognition module and a cardiac ejection signal acquisition module. The signal channel classification module is used to obtain multiple cardiopulmonary activity signal impedance measurement channel data of the subject, and divide the cardiopulmonary activity signal impedance measurement channel into a cardiac ejection activity strong correlation channel and a cardiac ejection activity weak correlation channel according to whether the cardiac activity signal strength contained in the breath-holding segment impedance signal in each measurement channel data is greater than a set threshold; the signal decomposition module is used to decompose the impedance data of the natural breathing segment in each measurement channel data using the ensemble empirical mode decomposition EEMD algorithm to obtain multiple intrinsic mode function IMF components, and then extract all IMF components related to pulmonary ventilation from the multiple IMF components through spectrum analysis to obtain a pulmonary ventilation reconstruction signal; the pulmonary ventilation signal recognition module is used to extract all IMF components related to pulmonary ventilation from the multiple IMF components through spectrum analysis to obtain a pulmonary ventilation reconstruction signal; the cardiac ejection signal acquisition module is used to subtract the original measurement data in each measurement channel data from the obtained pulmonary ventilation reconstruction signal of the corresponding channel to obtain a cardiac ejection activity related signal.

[0062] The present invention also provides a computer device, comprising a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute a cardiopulmonary activity signal separation method.

[0063] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is suitable for being loaded by a processor to execute a cardiopulmonary activity signal separation method.

[0064] The embodiments described above are only preferred specific implementation modes of the present invention, and the protection scope of the present invention is not limited thereto. Any simple changes or equivalent replacements of the technical solutions that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belong to the protection scope of the present invention.

Claims

1. A cardiopulmonary activity signal separation method, characterized in that: The steps include: Acquire multiple cardiopulmonary activity signal impedance measurement channel data of the subject, and determine whether the strength of the cardiac activity signal contained in the breath-holding segment impedance signal in each measurement channel data is greater than a set threshold value. If so, the cardiopulmonary activity signal impedance measurement channel is a strongly correlated channel for cardiac ejection activity; if not, the cardiopulmonary activity signal impedance measurement channel is a weakly correlated channel for cardiac ejection activity; The EEMD algorithm is used to decompose the electrical impedance data of the natural breathing segment in the strong correlation channel and the weak correlation channel of the cardiac ejection activity to obtain multiple intrinsic mode function IMF components; All IMF components related to pulmonary ventilation are extracted from multiple IMF components through spectrum analysis to obtain pulmonary ventilation reconstruction signals; The original measurement data in each measurement channel data is subtracted from the lung ventilation reconstruction signal of the corresponding channel to obtain a cardiac ejection activity related signal.

2. The cardiopulmonary activity signal separation method according to claim 1, characterized in that: Before extracting all the IMF components related to pulmonary ventilation from the multiple IMF components through spectrum analysis, the method also includes performing Fourier transform on the multiple IMF components to convert the IMF components into frequency domain signals.

3. The cardiopulmonary activity signal separation method according to claim 2, characterized in that: The method extracts all IMF components related to pulmonary ventilation from multiple IMF components through spectral analysis to obtain a pulmonary ventilation reconstruction signal, specifically: comparing the spectra of multiple IMF components converted into frequency domain signals with the respiratory frequency range of a healthy adult to obtain all IMF components related to pulmonary ventilation, and accumulating and reconstructing them to obtain a pulmonary ventilation reconstruction signal.

4. The cardiopulmonary activity signal separation method according to claim 1, characterized in that: After the original measurement data in each measurement channel data is subtracted from the lung ventilation reconstruction signal of the corresponding channel to obtain the cardiac ejection activity related signal, the method further includes performing dimensionality reduction processing on the cardiac ejection activity related signal, specifically: setting all cardiac ejection activity weakly related channel signals in the obtained cardiac ejection activity related signal to zero.

5. The cardiopulmonary activity signal separation method according to claim 1, characterized in that: After subtracting the original measurement data in each measurement channel data from the obtained pulmonary ventilation reconstruction signal of the corresponding channel, the method further includes performing noise reduction processing on the obtained cardiac ejection activity related signal, specifically: using bandpass filtering to remove high-frequency noise in the cardiac ejection activity related signal.

6. The cardiopulmonary activity signal separation method according to claim 5, characterized in that: After using bandpass filtering to remove high-frequency noise in the cardiac ejection activity signal, the method also includes using an EIT reconstruction algorithm to image the cardiac ejection activity related signal and the pulmonary ventilation reconstruction signal to obtain an image sequence reflecting the cardiac ejection activity and an image sequence reflecting the pulmonary ventilation activity.

7. A cardiopulmonary activity signal separation system, characterized in that: include: The signal channel classification module is used to obtain multiple cardiopulmonary activity signal impedance measurement channel data of the subject, and determine whether the cardiac activity signal strength contained in the breath-holding segment impedance signal in each measurement channel data is greater than a set threshold value. If so, the cardiopulmonary activity signal impedance measurement channel is a strongly correlated channel for cardiac ejection activity; if not, the cardiopulmonary activity signal impedance measurement channel is a weakly correlated channel for cardiac ejection activity; A signal decomposition module is used to decompose the electrical impedance data of the natural breathing segment in the strong correlation channel of cardiac ejection activity and the weak correlation channel of cardiac ejection activity by using an ensemble empirical mode decomposition (EEMD) algorithm to obtain multiple intrinsic mode function (IMF) components; A pulmonary ventilation signal recognition module is used to extract all IMF components related to pulmonary ventilation from multiple IMF components through spectrum analysis to obtain a pulmonary ventilation reconstruction signal; The cardiac ejection signal acquisition module is used to subtract the original measurement data in each measurement channel data from the obtained pulmonary ventilation reconstruction signal of the corresponding channel to obtain a cardiac ejection activity related signal.

8. A computer device, characterized in that: It comprises a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the cardiopulmonary activity signal separation method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the cardiopulmonary activity signal separation method according to any one of claims 1-6.

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