Electrical multi-mode signal acquisition and digital processing system for lung respiration monitoring
By combining sensor arrays and digital phase-sensitive detection technology, the problem of insufficient information complementarity in single sensor technology is solved, enabling real-time and accurate detection of the human breathing process and improving the system's detection accuracy and anti-interference capability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-18
- Publication Date
- 2026-04-03
AI Technical Summary
Existing respiratory monitoring systems use single-sensor technology, resulting in limited measurement data dimensions. This makes it difficult to comprehensively and accurately reflect complex respiratory physiological processes, and the lack of information complementarity limits the accuracy and reliability of detection.
By employing a highly integrated detection circuit based on a sensor array and a dedicated data processing module, combined with capacitive and resistive sensing, sinusoidal excitation signals of different frequencies are generated through an FPGA. The amplitude and phase information of the signals are extracted using digital phase-sensitive detection technology, thereby realizing multimodal signal acquisition and digital processing.
It achieves real-time and accurate detection of the human breathing process, improves the system's anti-interference ability and detection accuracy, and can comprehensively reflect multi-dimensional information such as chest cavity deformation and changes in air content, making it suitable for a variety of practical application environments.
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Figure CN121774485A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical electronic detection technology, specifically to an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring. Background Technology
[0002] Respiratory monitoring is a method of continuously and in real-time observing and measuring the human respiratory process using technological means. It aims to assess the functional status of the respiratory system and promptly detect and diagnose respiratory-related diseases or abnormalities. As one of the fundamental processes of human life, the frequency, depth, and rhythm of respiration directly reflect a person's health status. Respiratory monitoring has wide applications and functions in various fields, including clinical medicine, sports science, sleep research, and family health management. It not only helps doctors accurately diagnose respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), but also provides important physiological indicators in postoperative recovery, intensive care, and exercise training, guiding the formulation and adjustment of treatment plans, thereby ensuring patient safety and health.
[0003] Existing respiratory monitoring systems primarily employ single-sensor technology, such as capacitive or reactive respiratory monitoring. Capacitive respiratory monitoring systems typically apply an AC voltage signal of specific frequency and amplitude to sensing electrodes, creating an electric field distribution on the surface of the human chest cavity that changes with respiration. Changes in capacitance are then detected to reflect the dynamic deformation of the chest cavity. Reactive respiratory monitoring systems, on the other hand, apply a stable current signal to electrodes on the chest cavity surface, monitoring voltage changes between the electrodes to reflect dynamic changes in the dielectric properties and conductivity within the chest cavity, thereby monitoring the respiratory process. While these systems can monitor human respiration to some extent, they still have the following limitations: First, due to the use of single-sensor technology, the measurement data dimensions are limited, making it difficult to comprehensively and accurately reflect the complex respiratory physiological process. Second, the information complementarity is insufficient; the multidimensional characteristics of different physical quantities in respiratory monitoring are not effectively utilized, resulting in limited detection accuracy and reliability. The main reason for these shortcomings is that existing systems have failed to effectively integrate multiple sensor technologies and lack the ability to collaboratively process multi-source data. Specifically, single capacitance or impedance detection methods can only capture one aspect of the respiratory process and cannot comprehensively reflect multidimensional information such as chest cavity deformation and changes in air content. Summary of the Invention
[0004] The purpose of this invention is to provide an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring, so as to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, the present invention provides the following technical solution: an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring, the core of which includes a highly integrated detection circuit based on a sensor array and a dedicated data processing module.
[0006] The detection circuit is responsible for generating excitation signals, acquiring sensor data, and performing preliminary signal processing. The detection circuit consists of a field-programmable gate array (FPGA), a direct digital frequency synthesizer (DDS), a bandpass filter, a voltage amplifier, a C / V conversion circuit, an I / V conversion circuit, and a current source circuit. The data processing module uses digital phase-sensitive detection (DPSD) to extract the amplitude and phase information of the signal, including a capacitance sensing module and a impedance sensing module. The system synchronously acquires signals of chest wall deformation and tissue electrical property changes induced by respiration through the sensor array, and the detection circuit and data processing module complete the high-precision excitation, acquisition and digital analysis of the signals.
[0007] Furthermore, the excitation signal generation: the FPGA controls the DDS to generate two sinusoidal excitation signals F1 and F2 with different frequencies, wherein the sinusoidal excitation signal with frequency F1 is used for the capacitance sensor, and the sinusoidal excitation signal with frequency F2 is used for the impedance sensor.
[0008] Furthermore, in the capacitance sensing detection circuit: a sinusoidal excitation signal with a frequency of F1 is filtered by a bandpass filter with a center frequency consistent with its frequency and then amplified by a voltage, before being applied to the excitation terminal of the capacitance sensor. During respiration, the surface area of the thoracic cavity changes with breathing, causing a change in the effective area of the capacitive electrode on the human body surface. This results in a change in the capacitance value at the sensor's measurement end. The C / V conversion circuit converts this change into a voltage signal and feeds it back to the digital phase-sensitive detector module (DPSD) in the FPGA.
[0009] Furthermore, in the impedance sensing detection circuit: the sinusoidal excitation signal with a frequency of F2 is filtered and amplified by a voltage, then input to the current source circuit, converted into a stable current signal, and drives the excitation terminal of the impedance sensor. During respiration, the flow of blood inside the body causes a change in impedance. The I / V conversion circuit converts this change into a voltage signal, which is also sent back to the digital phase-sensitive detector (DPSD) module in the FPGA.
[0010] Furthermore, the digital phase-sensitive detection module extracts the amplitude and phase information of the detection signals with frequencies F1 and F2 respectively. By analyzing the amplitude and phase changes of the F1 and F2 signals, the monitoring of the human respiratory state is realized.
[0011] Furthermore, the sensor array adopts an inner and outer double-layer electrode design, with a total of 96 electrodes, including 48 inner layer electrodes and 48 outer layer electrodes, evenly distributed in three layers, with 16 electrodes in each layer. The electrodes synchronously acquire the sensor's own deformation and displacement information, as well as the conductivity and dielectric constant distribution signals of the tested tissue.
[0012] Furthermore, the sensor array includes the following two operating modes: Capacitive sensor mode: When used as a capacitive sensor, the inner and outer electrodes work together to reflect the dynamic deformation of the thoracic cavity by monitoring the change in capacitance between the electrodes. Impedance sensor mode: When used as an impedance sensor, only the inner electrode works, and the physiological parameters of multiphase flow in the thoracic cavity are reflected by monitoring the voltage changes between the electrodes on the body surface.
[0013] Furthermore, the signal processing flow of the data processing module is as follows: The FPGA receives analog signals from the detection circuit and converts them into digital signals; The analog signal is multiplied by in-phase and quadrature signals of the same frequency, and the results are summed to obtain the real and imaginary parts of the signal. The amplitude of the signal is obtained by calculating the square root of the sum of the squares of the real and imaginary parts, and the phase of the signal is obtained by calculating the ratio of the real to the imaginary parts.
[0014] Furthermore, the capacitance sensing module processes: In the capacitance sensing module, the FPGA receives the analog signal. Convert to digital signal Then, respectively with in-phase signals of the same frequency and orthogonal signals Multiply and sum to obtain the real part of the signal. and the virtual part ; Digital signals In-phase signal and orthogonal signals The expressions are as follows: ; ; ; In the formula, This represents the total number of sampling points, n = 1, 2, 3, ..., N; The frequencies are respectively The amplitude of the detected signal, The frequencies are respectively The phase of the detected signal; The frequencies are respectively The amplitude of the interference signal, The frequencies are respectively The phase of the interference signal; The frequencies are respectively The number of sampling points in one cycle of the detection signal. The frequencies are respectively The number of sampling points for the interference signal in one cycle; and and The results of the multiplication are as follows: ; ; Within an integer number of periods, the sum of uniform samples of a sine or cosine function over an integer number of periods is zero; and Furthermore, we can obtain: ; ; Finally, the amplitude and phase information of the F1 signal are obtained through the following calculations: ; .
[0015] Furthermore, the impedance sensing module processes: In the impedance sensing module, the FPGA receives the analog signal. Convert to digital signal Then respectively with In-phase signals of the same frequency and orthogonal signals Multiply and sum to obtain the real part of the signal. and the virtual part ; Digital signals In-phase signal and orthogonal signals The expressions are as follows: ; ; ; In the formula, This represents the total number of sampling points, n = 1, 2, 3, ..., N; The frequencies are respectively The amplitude of the detected signal, The frequencies are respectively The phase of the detected signal; The frequencies are respectively The amplitude of the interference signal, The frequencies are respectively The phase of the interference signal; The frequencies are respectively The number of sampling points in one cycle of the detection signal. The frequencies are respectively The number of sampling points for the interference signal in one cycle; and and The results of the multiplication are as follows: ; ; Within an integer number of periods, the sum of uniform samples of a sine or cosine function over an integer number of periods is zero, and Further results can be obtained: ; ; Finally, the amplitude and phase information of the F2 signal are obtained through the following calculations: ; .
[0016] Furthermore, in the aforementioned electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring, the capacitance detection circuit of the system outputs a voltage value V that is proportional to the deformation of the thoracic cavity. C According to the pre-calibrated capacitance sensor sensitivity S C Establish the relationship between voltage value and thoracic cavity deformation displacement d k The conversion relationship between them, thus derived from V C The real-time chest wall deformation state was calculated:
[0017] Based on the chest wall deformation, the changes in the internal structure of the pleural cavity during respiration are further deduced, and the sensitive field distribution SR required for electrical impedance imaging is dynamically corrected accordingly. Then, combined with the voltage value VR synchronously measured by the electrical impedance detection circuit, the dynamic conductivity distribution σ of the pleural cavity cross-section is inverted using an image reconstruction algorithm. Finally, the lung ventilation status is characterized based on the changes in this conductivity distribution.
[0018] This invention provides an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring, which has the following advantages: This invention combines digital phase-sensitive detection technology with software-level post-processing of the detection signal, reducing the size of the hardware circuitry and making the entire capacitance-resistance anti-coupling detection system more streamlined. Simultaneously, the reduced hardware circuitry helps minimize interference to the measurement signal, effectively eliminating common-mode interference and enhancing the system's sensitivity to human respiration, enabling real-time and accurate detection of the respiratory process. The entire technical solution is compact, has strong anti-interference capabilities, and is suitable for various practical application environments. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of a wearable sensor for an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 2 This is a schematic diagram of the sensor electrode distribution of an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 3 This is a schematic diagram of the sensor structure of an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 4 This is a side view schematic diagram of the sensor structure of an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention; Figure 5 This is a block diagram of the detection circuit system of an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention; Figure 6 This is a hardware schematic diagram of the impedance module of an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 7 This is a hardware schematic diagram of a capacitor module for an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 8 This is a physical hardware diagram of the impedance module and capacitor module of an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 9 This is a flowchart of a digital phase-sensitive detection system for an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 10 This is a diagram showing the components of a digital phase-sensitive detector module in an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 11 This is a schematic diagram of an AC capacitance detection circuit for an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Figure 12This is a schematic diagram of the current source circuit of an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention.
[0020] Figure 13 This is a circuit diagram of a resistive impedance voltage conversion circuit for an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to the present invention. Detailed Implementation
[0021] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.
[0022] like Figures 1-13 As shown, an electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring includes a highly integrated detection circuit based on a sensor array and a dedicated data processing module.
[0023] The detection circuit consists of a field-programmable gate array (FPGA), a direct digital frequency synthesizer (DDS), a bandpass filter, a voltage amplifier, a C / V conversion circuit, an I / V conversion circuit, and a current source circuit. It is used to generate excitation signals, acquire sensor data, and perform preliminary signal processing. Specifically, the excitation signal generation involves the FPGA controlling the DDS to generate two sinusoidal excitation signals, F1 and F2, at different frequencies. The sinusoidal excitation signal F1 is used for the capacitance sensor, and the sinusoidal excitation signal F2 is used for the impedance sensor.
[0024] 1) Capacitive Sensing Circuit: A sinusoidal excitation signal with a frequency of F1 is filtered by a bandpass filter with a center frequency matching its frequency and then amplified by a voltage amplifier before being applied to the excitation terminal of the capacitive sensor. In this embodiment, the F1 signal is filtered by a bandpass filter with a center frequency of F1 to remove high-frequency noise and low-frequency interference, and then the signal strength is enhanced by a voltage amplifier. The filtered and amplified signal is then applied to the excitation terminal of the capacitive sensor. During respiration, the surface area of the thoracic cavity changes with respiration, causing a change in the effective area of the capacitive electrodes on the human body surface, which in turn causes a change in the capacitance value at the sensor's measurement terminal. The C / V conversion circuit converts this change into a voltage signal and feeds it back to the digital phase-sensitive detector (DPSD) module in the FPGA.
[0025] 2) Impedance Sensing Circuit: A sinusoidal excitation signal with frequency F2, after appropriate filtering and voltage amplification, is input to the current source circuit, converted into a stable current signal, and drives the excitation terminal of the impedance sensor. In this embodiment, the F2 signal, after filtering and voltage amplification, is input to the current source circuit. A voltage-controlled current source structure is used to convert the voltage signal into a stable current signal to drive the excitation terminal of the impedance sensor. During respiration, the flow of blood inside the body causes impedance changes. The I / V conversion circuit converts this change into a voltage signal, which is also sent back to the digital phase-sensitive detector (DPSD) module in the FPGA.
[0026] In this system, the digital phase-sensitive detection module extracts the amplitude and phase information of the detection signals with frequencies F1 and F2 respectively. By analyzing the amplitude and phase changes of the F1 and F2 signals, the system can monitor the human respiratory state.
[0027] Sensor Array: This array synchronously acquires information on the dynamic deformation of the human chest wall during respiration, as well as the conductivity and dielectric constant distribution signals of the measured tissue. To address the dynamic deformation of the human chest wall caused by respiration, the sensor array employs a double-layer electrode design, comprising 96 electrodes in total: 48 inner layer electrodes and 48 outer layer electrodes, evenly distributed across three layers with 16 electrodes per layer. The electrodes synchronously acquire information on the sensor's own deformation and displacement, as well as the conductivity and dielectric constant distribution signals of the measured tissue. In this embodiment, the sensor array includes the following two operating modes: Capacitive sensor mode: When used as a capacitive sensor, the inner and outer electrodes work together to reflect the dynamic deformation of the thoracic cavity by monitoring the change in capacitance between the electrodes.
[0028] Impedance sensor mode: When used as an impedance sensor, only the inner electrode works, and the physiological parameters of multiphase flow in the thoracic cavity are reflected by monitoring the voltage changes between the electrodes on the body surface.
[0029] Data processing module: Employs digital phase-sensitive detection (DPSD) to extract signal amplitude and phase information. DPSD combines digital technology with traditional phase-sensitive detection principles, enabling signal post-processing at the software level, reducing hardware circuitry size, and improving system anti-interference capabilities. In this embodiment, the signal processing of the data processing module includes two main components: capacitance sensing module processing and impedance sensing module processing.
[0030] 1) Capacitive sensing module processing: In the capacitive sensing module, the FPGA processes the received analog signal. Convert to digital signal Then, respectively with in-phase signals of the same frequency and orthogonal signals Multiply and sum to obtain the real part of the signal. and the virtual part .
[0031] Digital signals In-phase signal and orthogonal signals The expressions are as follows: ; ; ; In the formula, This represents the total number of sampling points, n = 1, 2, 3, ..., N; The frequencies are respectively The amplitude of the detected signal, The frequencies are respectively The phase of the detected signal; The frequencies are respectively The amplitude of the interference signal, The frequencies are respectively The phase of the interference signal; The frequencies are respectively The number of sampling points in one cycle of the detection signal. The frequencies are respectively The number of sampling points for one cycle of the interference signal.
[0032] and and The results of the multiplication are as follows: ; ; Within an integer number of periods, the sum of uniform samples of a sine or cosine function over an integer number of periods is zero; and Furthermore, we can obtain: ; .
[0033] Finally, the amplitude and phase information of the F1 signal are obtained through the following calculations: ; .
[0034] 2) Impedance sensing module processing: In the impedance sensing module, the FPGA processes the received analog signal. Convert to digital signal Then respectively with In-phase signals of the same frequency and orthogonal signals Multiply and sum to obtain the real part of the signal. and the virtual part .
[0035] Digital signals In-phase signal and orthogonal signals The expressions are as follows: ; ; ; In the formula, This represents the total number of sampling points, n = 1, 2, 3, ..., N; The frequencies are respectively The amplitude of the detected signal, The frequencies are respectively The phase of the detected signal; The frequencies are respectively The amplitude of the interference signal, The frequencies are respectively The phase of the interference signal; The frequencies are respectively The number of sampling points in one cycle of the detection signal. The frequencies are respectively The number of sampling points for one cycle of the interference signal.
[0036] and and The results of the multiplication are as follows: ; ; Within an integer number of periods, the sum of uniform samples of a sine or cosine function over an integer number of periods is zero, and Further results can be obtained: ; .
[0037] Finally, the amplitude and phase information of the F2 signal are obtained through the following calculations: ; .
[0038] The system's capacitance detection circuit outputs a voltage value V that is proportional to the thoracic cavity deformation. C According to the pre-calibrated capacitance sensor sensitivity S C Establish the relationship between voltage value and thoracic cavity deformation displacement d kThe conversion relationship between them, thus derived from V C The real-time chest wall deformation state was calculated:
[0039] Based on the chest wall deformation, the changes in the internal structure of the pleural cavity during respiration are further deduced, and the sensitive field distribution SR required for electrical impedance imaging is dynamically corrected accordingly. Then, combined with the voltage value VR synchronously measured by the electrical impedance detection circuit, the dynamic conductivity distribution σ of the pleural cavity cross-section is inverted using an image reconstruction algorithm. Finally, the change in this conductivity distribution is used to characterize the lung ventilation status. .
[0040] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.
Claims
1. An electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring, characterized in that, include: Detection circuit: responsible for generating excitation signals, acquiring sensor data, and performing preliminary signal processing; the detection circuit consists of a field-programmable gate array, a direct digital frequency synthesizer, a bandpass filter, a voltage amplifier, a C / V conversion circuit, an I / V conversion circuit, and a current source circuit; Data processing module: Employs digital phase-sensitive detection to extract the amplitude and phase information of the signal, including a capacitance sensing module and a impedance sensing module; The signal processing flow of the data processing module is as follows: The FPGA receives analog signals from the detection circuit and converts them into digital signals; The analog signal is multiplied by in-phase and quadrature signals of the same frequency, and the results are summed to obtain the real and imaginary parts of the signal. The amplitude of the signal is obtained by calculating the square root of the sum of the squares of the real and imaginary parts, and the phase of the signal is obtained by calculating the ratio of the real to the imaginary parts.
2. The electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to claim 1, characterized in that, The excitation signal generation: The FPGA controls the DDS to generate two sinusoidal excitation signals F1 and F2 with different frequencies, wherein the sinusoidal excitation signal with frequency F1 is used for the capacitance sensor, and the sinusoidal excitation signal with frequency F2 is used for the impedance sensor.
3. The electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to claim 2, characterized in that, The capacitance sensing detection circuit: a sinusoidal excitation signal with a frequency of F1 is filtered by a bandpass filter with a center frequency that is the same as its frequency and then amplified by voltage before being applied to the excitation terminal of the capacitance sensor. During respiration, the surface area of the thoracic cavity changes with breathing, causing a change in the effective area of the capacitive electrode on the human body surface. This results in a change in the capacitance value at the sensor's measuring end. The C / V conversion circuit converts this change into a voltage signal and feeds it back to the digital phase-sensitive detection module in the FPGA.
4. The electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to claim 2, characterized in that, The impedance sensing detection circuit: after the sinusoidal excitation signal with a frequency of F2 is filtered and amplified, it is input to the current source circuit, converted into a stable current signal, and drives the excitation terminal of the impedance sensor. During respiration, the flow of blood inside the body causes a change in impedance. The I / V conversion circuit converts this change into a voltage signal, which is then sent back to the digital phase-sensitive detection module in the FPGA.
5. The electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to claim 4, characterized in that, The digital phase-sensitive detection module extracts the amplitude and phase information of the detection signals with frequencies F1 and F2 respectively. By analyzing the amplitude and phase changes of the F1 and F2 signals, the monitoring of human respiratory status is realized.
6. The electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to claim 1, characterized in that, The capacitance sensing module processes: In the capacitance sensing module, the received analog signal is processed by the FPGA. Convert to digital signal Then, respectively with in-phase signals of the same frequency and orthogonal signals Multiply and sum to obtain the real part of the signal. and the virtual part ; Digital signals In-phase signal and orthogonal signals The expressions are as follows: ； ； ； In the formula, This represents the total number of sampling points, n = 1, 2, 3, ..., N; The frequencies are respectively The amplitude of the detected signal, The frequencies are respectively The phase of the detected signal; The frequencies are respectively The amplitude of the interference signal, The frequencies are respectively The phase of the interference signal; The frequencies are respectively The number of sampling points in one cycle of the detection signal. The frequencies are respectively The number of sampling points for the interference signal in one cycle; and and The results of the multiplication are as follows: ； ； Within an integer number of periods, the sum of uniform samples of a sine or cosine function over an integer number of periods is zero; and Furthermore, we can obtain: ； ； Finally, the amplitude and phase information of the F1 signal are obtained through the following calculations: ； 。 7. The electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to claim 1, characterized in that, The impedance sensing module processes: In the impedance sensing module, the FPGA receives the analog signal. Convert to digital signal Then respectively with In-phase signals of the same frequency and orthogonal signals Multiply and sum to obtain the real part of the signal. and the virtual part ; Digital signals In-phase signal and orthogonal signals The expressions are as follows: ； ； ； In the formula, This represents the total number of sampling points, n = 1, 2, 3, ..., N; The frequencies are respectively The amplitude of the detected signal, The frequencies are respectively The phase of the detected signal; The frequencies are respectively The amplitude of the interference signal, The frequencies are respectively The phase of the interference signal; The frequencies are respectively The number of sampling points in one cycle of the detection signal. The frequencies are respectively The number of sampling points for the interference signal in one cycle; and and The results of the multiplication are as follows: ； ； Within an integer number of periods, the sum of uniform samples of a sine or cosine function over an integer number of periods is zero, and Further results can be obtained: ； ； Finally, the amplitude and phase information of the F2 signal are obtained through the following calculations: ； 。 8. The electrical multimodal signal acquisition and digital processing system for lung respiratory monitoring according to claim 1, characterized in that, The capacitance detection circuit of the system outputs a voltage value V that is proportional to the deformation of the thoracic cavity. C According to the pre-calibrated capacitance sensor sensitivity S C Establish the relationship between voltage value and thoracic cavity deformation displacement d k The conversion relationship between them, thus derived from V C The real-time chest wall deformation state was calculated:
9. Based on the chest wall deformation state, the changes in the internal structure of the pleural cavity during respiration are further deduced, and the sensitive field distribution SR required for electrical impedance imaging is dynamically corrected accordingly. Then, combined with the voltage value VR synchronously measured by the electrical impedance detection circuit, the dynamic conductivity distribution σ of the pleural cavity cross-section is inverted using an image reconstruction algorithm. Finally, the lung ventilation status is characterized based on the change in this conductivity distribution. 。