Postoperative intestinal dysfunction state recognition method and system based on intestinal stimulation feedback

Abstract

This application relates to the field of biomedical signal processing and discloses a method for identifying postoperative intestinal dysfunction based on intestinal stimulation feedback, comprising the following steps: S1. A linear frequency modulated (LFM) scanning signal is emitted to the abdomen of the subject, and the mechanical vibration response signal of the abdominal medium to the LFM scanning signal is acquired. The dominant mechanical response frequency characterizing the abdominal medium in the current state is calculated and determined based on the LFM scanning signal and the mechanical vibration response signal. The dominant mechanical response frequency of the intestinal tissue is locked by baseline scanning, and resonant micro-perturbation is implemented at this frequency. The stress response of the intestine is captured synchronously using a dual-modal sensor of mechanical and electrophysiological sensors. By calculating the two-dimensional feature vector composed of the phase-locked value and the dynamic mechanical input impedance, the system can accurately distinguish between a simple decrease in electrical activity and a high-tension state caused by obstruction, thereby providing clinicians with a deeper pathophysiological diagnostic basis.

Description

Technical Field

[0001] This invention relates to the field of biomedical signal processing technology, specifically to a method and system for identifying postoperative intestinal dysfunction based on intestinal stimulation feedback. Background Technology

[0002] Following abdominal surgery, patients often face the risk of intestinal dysfunction such as paralytic ileus or intestinal obstruction. Accurately assessing the recovery of intestinal motility postoperatively is crucial for guiding clinical feeding and rehabilitation. Current clinical assessment methods mainly rely on physician auscultation, patients' subjective reports of flatus and defecation, and imaging examinations.

[0003] However, existing auscultation and subjective reporting methods are highly subjective and time-sensitive, making it difficult to quantify subtle changes in intestinal function. While imaging examinations such as X-rays or CT scans are intuitive, they primarily reflect the static, transient state of anatomical structures and cannot monitor intestinal motility in real time. Although surface gastrointestinal electrography has been used to monitor gastrointestinal bioelectrical activity, this purely passive monitoring method has significant limitations. On the one hand, a single electrophysiological signal cannot fully reflect the coupling process of "electroexcitation-mechanical contraction," making it difficult to effectively distinguish between paralytic ileus caused by neural inhibition and mechanical ileus caused by physical obstruction. This is because these two states may appear similar in terms of electrical signals alone, but their intestinal wall tension and mechanical compliance are fundamentally different.

[0004] Furthermore, the gastrointestinal electrical signals on the body surface of postoperative patients are extremely weak, and their frequencies are mainly distributed in the very low frequency band, making them highly susceptible to interference from respiratory movements, electrocardiogram signals, and environmental noise. Existing signal processing methods mostly use traditional linear filters to filter out interference. While this approach suppresses noise, it often destroys the original nonlinear phase characteristics of the signal, making it impossible to accurately calculate the synchronization relationship between bioelectrical and mechanical movements, thus affecting the precise interpretation of intestinal functional status.

[0005] In attempting interventional monitoring of the gut, existing active stimulation techniques typically employ fixed-frequency or continuous mechanical / electrical stimulation modalities. Biological tissues adapt to constant and continuous external stimuli; prolonged monotonous stimulation can easily lead to desensitization of the intestinal neuromuscular system, causing monitoring data to become distorted over time and failing to accurately reflect the gut's stress reserve capacity. Furthermore, some invasive monitoring methods increase the risk of postoperative infection and are unsuitable for continuous postoperative monitoring of patients. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for identifying postoperative intestinal dysfunction based on intestinal stimulation feedback, thus solving the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying postoperative intestinal dysfunction based on intestinal stimulation feedback, comprising the following steps:

[0008] S1. A linear frequency modulated scanning signal is emitted to the abdomen of the subject, and the mechanical vibration response signal of the abdominal medium to the linear frequency modulated scanning signal is collected. The dominant mechanical response frequency characterizing the abdominal medium in the current state is calculated and determined based on the linear frequency modulated scanning signal and the mechanical vibration response signal.

[0009] S2. Based on the dominant mechanical response frequency, an intermittent pulse train signal is synthesized, and mechanical perturbation is applied to the subject's abdomen using the dominant mechanical response frequency as the carrier.

[0010] S3. During the application of the intermittent pulse train signal, the mechanical response signal of the subject's abdomen and the bioelectrical signal of the abdominal surface are acquired simultaneously;

[0011] S4. The mechanical response signal and the abdominal surface bioelectric signal are preprocessed and analyzed to extract the mechanical instantaneous phase sequence, the electrophysiological instantaneous phase sequence, and the instantaneous envelope of the mechanical response signal, respectively.

[0012] S5. Calculate the phase lock value based on the mechanical instantaneous phase sequence and the electrophysiological instantaneous phase sequence, and calculate the dynamic mechanical input impedance based on the instantaneous envelope and the digital drive command sequence of the intermittent pulse train signal;

[0013] S6. Construct a two-dimensional feature vector by combining the phase-locked value and the dynamic mechanical input impedance, and output the intestinal function evaluation result based on the distribution area of ​​the two-dimensional feature vector in the preset threshold space.

[0014] Preferably, the specific process for determining the dominant mechanical response frequency in S1 is as follows:

[0015] Calculate the cross power spectral density between the linear frequency modulated scanning signal and the mechanical vibration response signal, and calculate the auto-power spectral density of the linear frequency modulated scanning signal;

[0016] The ratio of the cross power spectral density to the self power spectral density is calculated to construct the mechanical transfer function;

[0017] The global maximum value of the amplitude of the mechanical transfer function is searched, and the frequency corresponding to the global maximum value of the amplitude is determined as the dominant mechanical response frequency.

[0018] Preferably, the specific process of synthesizing the intermittent pulse train signal in S2 is as follows:

[0019] Generate a digital carrier sequence that is in the same frequency as the dominant mechanical response frequency;

[0020] Generate a rectangular window function, which defines the duration of a single pulse train and the excitation repetition period;

[0021] The digital carrier sequence is multiplied point by point with the rectangular window function to generate an adaptive excitation signal as the intermittent pulse train signal, and the duration covers 3 to 5 gastrointestinal slow wave cycles.

[0022] Preferably, the preprocessing of the abdominal surface bioelectrical signal in step S4 includes:

[0023] A digital bandpass filter with a passband of 0.05 Hz to 0.5 Hz is applied to the abdominal surface bioelectrical signal; the respiratory motion principal component is extracted from the synchronously acquired mechanical response signal as a reference signal;

[0024] The filtered signal is decomposed into several independent source components using the independent component analysis algorithm;

[0025] Calculate the time correlation coefficient between each independent source component and the reference signal, identify and zero the components whose time correlation coefficient is greater than a preset threshold as respiratory artifact components, and identify and zero ECG artifact components based on periodic QRS complex characteristics.

[0026] The denoised gastrointestinal electrical signal sequence was reconstructed using the remaining independent source components.

[0027] Preferably, the specific process for extracting the mechanical instantaneous phase sequence and the electrophysiological instantaneous phase sequence in S4 is as follows:

[0028] The mechanical response signal and the denoised gastrointestinal electrical signal sequence are subjected to discrete Hilbert transform using a finite impulse response filter, and the conjugate orthogonal components of the original signal are obtained by time-domain convolution operation.

[0029] A complex analytic signal is constructed based on the original signal and the conjugate orthogonal components;

[0030] The instantaneous phase of the complex analytic signal is calculated using the four-quadrant arctangent function, thereby obtaining the mechanical instantaneous phase sequence and the electrophysiological instantaneous phase sequence, respectively.

[0031] Preferably, the specific process for calculating the phase-locked value in step S5 is as follows:

[0032] Receive the mechanical instantaneous phase sequence and the electrophysiological instantaneous phase sequence;

[0033] Within a time window consistent with the duration of the intermittent pulse train signal, the average result vector length of the phase difference between the mechanical signal phase and the electrophysiological signal phase is calculated as the phase lock value, which is used to quantify the degree of synchronous response of bioelectrical activity to external mechanical stimulation.

[0034] Preferably, the specific process for calculating the dynamic mechanical input impedance in S5 is as follows:

[0035] Within a time window that is consistent with the duration of the intermittent pulse train signal, the root mean square value of the digital drive command sequence and the root mean square value of the mechanical vibration velocity sequence are calculated respectively.

[0036] Calculate the ratio of the root mean square value of the digital drive command sequence to the root mean square value of the mechanical vibration velocity sequence;

[0037] The dynamic mechanical input impedance is obtained by multiplying the ratio by a preset system electromechanical conversion calibration coefficient, which is determined based on the voltage-to-force conversion sensitivity and the reciprocal of the sensing sensitivity.

[0038] Preferably, the preset threshold space is defined by the phase-locking determination threshold and the mechanical impedance upper limit threshold;

[0039] The specific process of outputting the intestinal function evaluation results in S6 includes:

[0040] Compare the phase lock value with the phase lock determination threshold, and compare the dynamic mechanical input impedance with the upper limit threshold of the mechanical impedance;

[0041] When the phase lock value is less than the phase lock determination threshold, it is determined to be in the first interval, and an electromechanical separation state identifier is output.

[0042] When the phase lock value is greater than or equal to the phase lock determination threshold, and the dynamic mechanical input impedance is greater than the upper limit threshold of the mechanical impedance, it is determined to be the second interval, and a high tension coupling state identifier is output.

[0043] When the phase lock value is greater than or equal to the phase lock determination threshold, and the dynamic mechanical input impedance is less than or equal to the upper limit threshold of the mechanical impedance, it is determined to be in the third interval, and the compliance recovery state indicator is output.

[0044] Preferably, the frequency range of the mechanical disturbance is from 10Hz to 200Hz; the mechanical response signal is collected using an accelerometer, and the abdominal surface bioelectrical signal is collected using a surface electrode.

[0045] A postoperative intestinal function abnormality identification system based on intestinal stimulation feedback includes: an adaptive stimulation execution unit, a dual-modal feedback acquisition unit, and a data processing and control host.

[0046] The adaptive stimulation execution unit is used to receive excitation control commands, generate mechanical vibration signals of preset frequency and waveform, and apply them to the abdominal surface of the subject.

[0047] The dual-modal feedback acquisition unit is used to simultaneously acquire the mechanical response signal of the subject's abdomen and the bioelectrical signal of the abdominal surface.

[0048] The data processing and control host is connected to the adaptive stimulus execution unit and the bimodal feedback acquisition unit.

[0049] This invention provides a method and system for identifying postoperative bowel dysfunction based on intestinal stimulation feedback. It has the following beneficial effects:

[0050] 1. This invention locks the dominant mechanical response frequency of intestinal tissue through baseline scanning and implements resonant micro-perturbation at this frequency. It uses a dual-modal sensor of mechanical and electrophysiological characteristics to simultaneously capture the stress response of the intestine. By calculating the two-dimensional feature vector composed of phase lock value and dynamic mechanical input impedance, the system can accurately distinguish between simple weakening of electrical activity and high tension state caused by obstruction, thereby providing clinicians with a deeper pathophysiological diagnostic basis.

[0051] 2. This invention effectively overcomes the challenge of weak intestinal signals being easily interfered with through a unique signal analysis algorithm. Addressing the complex abdominal environment and strong interference from respiratory and ECG signals in postoperative patients, this invention introduces a preprocessing workflow combining Discrete Hilbert Transform and Independent Component Analysis. While preserving the original phase information of the gastrointestinal slow waves, it separates the pure gastrointestinal electrical and mechanical vibration components from the complex mixed surface signals, and accurately extracts the instantaneous phase sequence using a bivariate arctangent function. This processing method avoids phase distortion that may be caused by conventional filtering, ensures the accuracy of phase-locked value calculation, and enables the acquisition of high signal-to-noise ratio characteristic parameters even in strong noise environments.

[0052] 3. This invention is non-invasive and well-tolerated by patients, making it suitable for continuous postoperative monitoring. The system employs a wide-frequency vibration actuator and a surface electrode array, enabling excitation and data acquisition without invasive procedures. By setting intermittent pulse train stimulation patterns, it ensures effective transmission of mechanical energy in biological tissues and induces smooth muscle depolarization, while avoiding adaptive desensitization of biological tissues due to continuous stimulation. This approach minimizes the burden on fragile postoperative intestinal tissues while acquiring effective data, facilitating long-term bedside monitoring in clinical settings. Attached Figure Description

[0053] Figure 1 This is a system framework diagram of the present invention;

[0054] Figure 2 This is a schematic diagram of the internal functional modules of the data processing and control host of the present invention;

[0055] Figure 3 This is a schematic diagram of the method flow of the present invention;

[0056] 100. Adaptive Stimulation Execution Unit; 200. Dual-Modal Feedback Acquisition Unit; 210. Mechanical Sensing Module; 220. Electrophysiological Sensing Module; 300. Data Processing and Control Host; 310. Excitation Control Module; 320. Signal Preprocessing Module; 330. System Identification Module; 340. Feature Extraction Module; 350. State Recognition Module. Detailed Implementation

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Example:

[0059] Please see the appendix Figure 1 -Appendix Figure 3 This invention provides a postoperative intestinal function abnormality identification system based on intestinal stimulation feedback, including an adaptive stimulation execution unit 100, a dual-modal feedback acquisition unit 200, and a data processing and control host 300.

[0060] The adaptive stimulation execution unit 100 generates a mechanical vibration signal with a preset frequency and waveform, and applies the mechanical vibration signal to the abdominal surface of the subject. The adaptive stimulation execution unit 100 is communicatively connected to the data processing and control host 300 and receives excitation control commands from the data processing and control host 300.

[0061] The adaptive stimulation actuation unit 100 includes a wideband vibration actuator and a drive amplification circuit. The wideband vibration actuator employs a piezoelectric ceramic actuator or a voice coil motor, with an operating frequency range covering 10Hz to 200Hz. The wideband vibration actuator is equipped with a coupling medium layer for attachment to the surface of human skin. This coupling medium layer is composed of a medical silicone pad or ultrasonic coupling gel with preset acoustic impedance characteristics, used to eliminate air gaps between the actuator and the skin interface.

[0062] The bimodal feedback acquisition unit 200 is used to simultaneously acquire mechanical and electrophysiological response data of the subject's abdomen during the application of stimulation by the adaptive stimulation execution unit 100. The signal output terminal of the bimodal feedback acquisition unit 200 is connected to the signal input terminal of the data processing and control host 300. The bimodal feedback acquisition unit 200 includes a mechanical sensing module 210 and an electrophysiological sensing module 220.

[0063] The mechanical sensing module 210 is used to detect the mechanical vibration waveform on the surface of the abdominal medium. The mechanical sensing module 210 employs a microelectromechanical system (MEMS) accelerometer or a piezoelectric thin-film sensor with low-frequency response characteristics. The mechanical sensing module 210 is positioned within a preset distance range of the broadband vibration actuator to acquire the mechanical wave signal transmitted through the intestinal tissue.

[0064] The electrophysiological sensing module 220 is used to acquire bioelectrical signals from the abdominal surface. The electrophysiological sensing module 220 includes a silver / silver chloride surface electrode array. The surface electrode array is disposed in the abdominal projection area of ​​the subject to extract voltage signals containing slow wave components of the gastrointestinal tract.

[0065] The data processing and control host 300 is used for the overall timing of the control system, processes multi-channel sensor data, and calculates biophysical characteristic parameters. The data processing and control host 300 includes signal conditioning circuitry, a multi-channel analog-to-digital converter, a digital processor, and excitation drive circuitry.

[0066] The excitation drive circuit includes a digital-to-analog converter (DAC) and a power amplifier. The input of the DAC is connected to the digital processor, and the output is connected to the adaptive stimulation execution unit 100 via the power amplifier. The DAC is used to convert the digital excitation waveform into an analog voltage signal that drives the broadband vibration actuator.

[0067] The signal conditioning circuit is connected to both the mechanical sensing module 210 and the electrophysiological sensing module 220. For the signal path of the electrophysiological sensing module 220, the signal conditioning circuit is equipped with a high-input-impedance instrumentation amplifier and an anti-aliasing low-pass filter to amplify the microvolt-level bioelectrical signal to the linear input range of the multi-channel analog-to-digital converter. The multi-channel analog-to-digital converter converts the conditioned analog signal into a digital signal sequence.

[0068] The digital processor executes a preset data processing algorithm, including calculating the mechanical transfer function, extracting the signal analytical phase, calculating the phase lock value, and the mechanical input impedance. Based on the calculation results, the digital processor outputs evaluation indicators characterizing the intestinal functional state.

[0069] The data processing and control host 300 includes: an excitation control module 310, a signal preprocessing module 320, a system identification module 330, a feature extraction module 340, and a state recognition module 350.

[0070] The excitation control module 310 generates digital excitation waveform data and manages the output timing. Internally, the excitation control module 310 is equipped with a direct digital frequency synthesis (DDS) algorithm to synthesize a linear frequency modulated scanning signal sequence covering a preset frequency band, and a fixed-frequency pulse train signal sequence based on a specific carrier frequency. The excitation control module 310 transmits the generated digital waveform data to a digital-to-analog converter to drive the adaptive stimulation execution unit 100 to generate corresponding physical vibrations, and synchronously transmits the digital waveform data as a reference input signal to the system identification module 330 and the feature extraction module 340.

[0071] The signal preprocessing module 320 performs digital filtering and analytical processing on the raw sampled data uploaded by the dual-modal feedback acquisition unit 200. The signal preprocessing module 320 performs digital bandpass filtering on the electrophysiological signals with a passband of 0.05Hz to 0.5Hz to retain slow wave components of the gastrointestinal tract and suppress baseline drift and high-frequency interference. The signal preprocessing module 320 further performs Hilbert transform on the mechanical response signal and the filtered electrophysiological signal, converting the real-valued signal into a complex analytic signal and separating the instantaneous amplitude sequence and instantaneous phase sequence of each channel signal.

[0072] The system identification module 330 is used to calculate the mechanical transfer function and determine the local resonant frequency. During the system's scanning mode, the system identification module 330 receives reference input signal data from the excitation control module 310 and mechanical response signal data from the signal preprocessing module 320, and calculates the cross-power spectral density of the two signals and the auto-power spectral density of the reference input signal. Based on the power spectral density ratio, the system identification module 330 constructs a frequency response curve, searches for the frequency point corresponding to the amplitude maxima, and transmits the parameters of this frequency point to the excitation control module 310 as the carrier frequency for subsequent pulse train excitation.

[0073] The feature extraction module 340 is used to calculate biophysical parameters characterizing the intestinal state. The feature extraction module 340 receives the instantaneous phase sequence from the signal preprocessing module 320, calculates the phase synchronization index between the mechanical signal and the electrophysiological signal within a preset time window, and obtains the phase lock value. The feature extraction module 340 simultaneously calculates the mechanical input impedance parameter, which is calculated based on the ratio of the digital excitation amplitude from the excitation control module 310 to the mechanical response amplitude from the signal preprocessing module 320, and is used to characterize the physical compliance of the abdominal medium.

[0074] The state recognition module 350 is used to output intestinal function evaluation results based on feature parameters. The specific logic for outputting the intestinal function evaluation results in S6 is as follows:

[0075] The system has preset phase-locking thresholds (e.g., 0.4) and upper limits for mechanical impedance (preferably set by normalization based on the patient's BMI). The mapping relationship of the two-dimensional feature vectors in the preset threshold space has clear pathophysiological significance:

[0076] (1) Electromechanical dissociation state (first interval): characterized by "low phase lock value". When the calculated phase lock value is less than the phase lock determination threshold, it is determined to be electromechanical dissociation state regardless of the magnitude of the dynamic mechanical input impedance. This state indicates that the intestinal smooth muscle cannot effectively follow the external resonant mechanical stimulation to generate synchronous depolarization electrical activity, suggesting that the intestinal nervous system is in an inhibited or paralyzed state, which is common in the early stage of postoperative paralytic ileus.

[0077] (2) High-tension coupling state (second interval): characterized by "high phase lock value + high mechanical input impedance". When the phase lock value is greater than or equal to the phase lock determination threshold, it indicates that the intestine has the ability to respond to stress; but at the same time, the dynamic mechanical input impedance is greater than the upper limit threshold of the mechanical impedance, indicating that the abdominal medium stiffness is abnormally increased. This state suggests that although the intestine retains nerve conduction function, the intestinal wall tension is too high, which is common in the stage of mechanical intestinal obstruction or incomplete intestinal obstruction.

[0078] (3) Compliance recovery state (third interval): characterized by high phase lock value and low mechanical input impedance. When the phase lock value is greater than or equal to the phase lock determination threshold and the dynamic mechanical input impedance is less than or equal to the upper limit threshold of mechanical impedance, it is determined to be in the compliance recovery state. This state indicates that the intestine has both good neurophysiological responsiveness and normal mechanical compliance, and is a sign that the intestinal function has recovered to the standard for eating after surgery.

[0079] This invention provides a method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback. The method is executed by a data processing and control host 300 and specifically includes the following steps:

[0080] Step S1: Perform baseline scanning and media characteristic identification. The data processing and control host 300 controls the adaptive stimulation execution unit 100 to transmit a linear frequency-modulated scanning signal to the subject's abdomen, the signal covering a preset frequency band. During this period, the mechanosensing module 210 acquires the mechanical vibration response of the abdominal medium. Based on internally generated digital excitation waveform data as a reference input, the data processing and control host 300 performs cross-correlation calculations with the acquired mechanical response signal to calculate the mechanical transfer function, and searches for the point of maximum amplitude of the mechanical transfer function to determine the dominant mechanical response frequency.

[0081] Step S2: Apply adaptive perturbation excitation. The data processing and control host 300 synthesizes an intermittent pulse train signal based on the dominant mechanical response frequency determined in step S1. The data processing and control host 300 drives the adaptive stimulation execution unit 100 to apply a mechanical perturbation with a preset duty cycle to the subject's abdomen using the dominant mechanical response frequency as a carrier.

[0082] Step S3: Perform dual-modal synchronous data acquisition. Within the time window of the intermittent pulse train signal, the dual-modal feedback acquisition unit 200 synchronously acquires data. Specifically, the mechanosensing module 210 acquires the mechanoresponse signal, and the electrophysiological sensing module 220 acquires the abdominal surface bioelectrical signal. The data processing and control host 300 performs sampling timing synchronization calibration on the two acquired data streams.

[0083] Step S4 involves signal preprocessing and analysis. The data processing and control host 300 performs bandpass filtering on the electrophysiological signals to remove respiratory and ECG artifacts. The data processing and control host 300 uses the Hilbert transform algorithm to convert the mechanical response signals and the filtered electrophysiological signals into analytical signals, and extracts the instantaneous envelope sequence and instantaneous phase sequence of each channel signal.

[0084] Step S5: Calculate the mechanical and electrophysiological coupling characteristics. The data processing and control host 300 calculates the phase lock value between the mechanical signal phase and the electrophysiological signal phase within a preset time window, which is used to quantify the degree of synchronous response of bioelectrical activity to external mechanical stimulation. The data processing and control host 300 synchronously calculates the dynamic mechanical input impedance, which is derived based on the ratio of the effective value (RMS) of the digital excitation signal to the effective value (RMS) of the mechanical response signal.

[0085] Step S6: Perform multi-dimensional state logic determination and output. The data processing and control host 300 constructs a two-dimensional feature vector from the calculated dynamic mechanical input impedance and phase lock value, and maps the two-dimensional feature vector to a preset two-dimensional threshold decision space. Based on the threshold interval into which the two-dimensional feature vector falls in the decision space, the data processing and control host 300 outputs an electromechanical separation state identifier, a high-tension coupling state identifier, or a compliance recovery state identifier characterizing the intestinal functional state.

[0086] The following section, with reference to the accompanying diagrams, will provide a detailed explanation of the implementation principles of each of the above steps and the logic modules involved:

[0087] The excitation control module 310 inside the data processing and control host 300 first generates a linear frequency modulated scanning signal sequence. This linear frequency modulated scanning signal sequence serves as the system's input excitation signal. The excitation control module 310 will input the excitation signal. The signal is transmitted to a digital-to-analog converter, amplified, and then drives the adaptive stimulation execution unit 100. The adaptive stimulation execution unit 100 applies corresponding mechanical vibrations to the abdominal surface of the subject. The input excitation signal... The mathematical expression is:

[0088]

[0089] in, Indicates time The signal amplitude, For time variables, The preset scan amplitude, The scan start frequency, The scan termination frequency, This refers to the scan duration. In this embodiment, Set to 10Hz. It was set to 200Hz to cover the typical mechanical resonance frequency band of intestinal soft tissue.

[0090] During the scanning process performed by the adaptive stimulation execution unit 100, the mechanical sensing module 210 in the dual-modal feedback acquisition unit 200 synchronously acquires the mechanical vibration response signal of the abdominal medium surface. The mechanical sensing module 210 transmits the acquired analog signals to the data processing and control host 300 for analog-to-digital conversion, generating a discrete digital response sequence.

[0091] The system identification module 330 inside the data processing and control host 300 receives the mechanical vibration response signal. and the input excitation signal from the excitation control module 310 The system identification module 330 performs a Fast Fourier Transform (FFT) operation on the digital response sequence and the digital reference sequence to convert the time-domain signal into a frequency-domain signal.

[0092] The system identification module 330 calculates the cross-power spectral density between the input excitation signal and the mechanical vibration response signal based on frequency domain data. And calculate the autopower spectral density of the input excitation signal. The system identification module 330 constructs the mechanical transfer function of the abdominal medium based on the ratio of the cross-power spectral density to the self-power spectral density. The mechanical transfer function The calculation formula is:

[0093]

[0094] in Representing the frequency variable, this calculation process is based on the principle of frequency domain system identification and can suppress the interference of incoherent environmental noise on the measurement of transmission characteristics.

[0095] The system identification module 330 searches for the amplitude of the mechanical transfer function within a preset scanning frequency range. The global maximum value. The system identification module 330 determines the frequency corresponding to the global maximum value as the dominant mechanical response frequency. .

[0096] The system identification module 330 will determine the dominant mechanical response frequency. The frequency parameter is stored in an internal register and sent to the excitation control module 310. The excitation control module 310 locks this frequency parameter as the carrier frequency for the subsequent generation of the pulse train excitation signal to ensure maximum conduction efficiency of mechanical energy in biological tissue.

[0097] After receiving the dominant mechanical response frequency determined by the system identification module 330, the data processing and control host 300 instructs the excitation control module 310 to enter the disturbance excitation generation state. The excitation control module 310 reads the dominant mechanical response frequency parameter from the memory and configures the phase accumulator step size of the direct digital frequency synthesizer (DDS) based on this parameter to synthesize a digital carrier sequence with the same frequency as the dominant mechanical response frequency. The excitation control module 310 uses its internally configured digital multiplier to perform point-by-point multiplication of the digital carrier sequence with a preset time window function to generate an adaptive excitation signal. The mathematical expression for it is defined as:

[0098]

[0099] in, This represents the instantaneous amplitude of the excitation signal at time t. The preset safety excitation intensity coefficient, The dominant mechanical response frequency, This is a rectangular window function used to control the pulse on / off timing. The excitation control module 310 generates the rectangular window function through a hardware timer or sampling counter. This forms an intermittent pulse train structure. The rectangular window function... The definition is as follows:

[0100]

[0101] in, Indicates the duration of a single pulse train. This represents the time interval between the start times of two consecutive pulse trains, i.e., the excitation repetition period. ) represents the time variable t with respect to time. Perform the modulo operation. In this embodiment, It is configured with a preset time range of 10 to 60 seconds, which corresponds to 3 to 5 typical gastrointestinal slow wave cycles, to ensure that mechanical stimulation has a sufficient cumulative effect to induce smooth muscle depolarization. Configured to be greater than The value (e.g., set to) (2 to 5 times) to prevent biological tissues from developing adaptive desensitization to continuous constant stimulation by setting a silent interval between two mechanical stimuli.

[0102] The excitation control module 310 will generate the adaptive excitation signal. The digital sequence is transmitted to a digital-to-analog converter (DAC). The DAC converts the digital sequence into an analog voltage signal, which drives the adaptive stimulation execution unit 100 via a power amplifier circuit. The adaptive stimulation execution unit 100 generates a frequency of [value missing] on the subject's abdomen based on the analog voltage signal. And follow Mechanical vibration with a temporal regularity.

[0103] This invention also provides a mechanical vibration signal acquisition and noise reduction technology, which is mainly executed by the mechanical sensing module 210 in the dual-modal feedback acquisition unit 200, the signal conditioning circuit and the signal preprocessing module 320 inside the data processing and control host 300.

[0104] The mechanical sensing module 210 employs a high-sensitivity microelectromechanical system (MEMS) accelerometer or a piezoelectric vibration sensor. The mechanical sensing module 210 is fixed to the abdominal skin surface of the subject using medical double-sided adhesive tape or an elastic bandage to establish a rigid mechanical coupling connection. The mechanical sensing module 210 senses mechanical vibration waves induced by the adaptive stimulation execution unit 100 and conducted through intestinal tissue to the body surface, converting the physical vibration quantity into an analog voltage signal. This analog voltage signal is transmitted to the data processing and control host 300 via a shielded cable.

[0105] The signal conditioning circuit of the data processing and control host 300 receives the analog voltage signal and first adjusts the signal amplitude gain through a charge amplifier or instrumentation amplifier to match the input dynamic range of the analog-to-digital converter. The signal conditioning circuit then performs analog bandpass filtering on the amplified analog signal. The passband frequency range of the analog bandpass filter is set to 5Hz to 500Hz to filter out DC gravity components, low-frequency baseline drift caused by breathing, and high-frequency electromagnetic interference above the operating frequency band at the hardware level.

[0106] The signal, after analog filtering, enters a multi-channel analog-to-digital converter (ADC). The ADC discretizes the analog signal at a preset sampling frequency to generate the original digital acceleration sequence. In this embodiment, the sampling frequency is set to no less than 1000Hz to preserve the phase information of the vibration signal and prevent spectral aliasing.

[0107] The signal preprocessing module 320 receives the original digital acceleration sequence and executes a cascaded digital filtering algorithm. The signal preprocessing module 320 applies a digital high-pass filter with a cutoff frequency of 10Hz to further remove residual very low-frequency breathing artifacts. The signal preprocessing module 320 applies a digital low-pass filter with a cutoff frequency of 250Hz to suppress out-of-band quantization noise.

[0108] To obtain the velocity parameters used to calculate the mechanical input impedance, the signal preprocessing module 320 performs numerical integration on the filtered digital acceleration sequence. The signal preprocessing module 320 uses either the trapezoidal integration method or the Simpson integration method to convert the acceleration data into velocity data. Simultaneously with the numerical integration, the signal preprocessing module 320 applies a polynomial fitting detrending algorithm based on a preset-length sliding time window to eliminate zero-point drift in the velocity waveform caused by the accumulation of the integration constant within each time window, thereby obtaining a high signal-to-noise ratio vibration velocity sequence of the abdominal surface. .

[0109] This invention provides a method for extracting weak bioelectrical signals and suppressing artifacts. The method is executed by the signal preprocessing module 320 inside the electrophysiological sensing module 220 acquisition front end and the data processing and control host 300.

[0110] The electrophysiological sensing module 220 picks up millivolt or microvolt-level bioelectric potential differences on the body surface through a multi-channel silver / silver chloride electrode array attached to the body surface. The electrode array contains at least three independent acquisition electrodes to construct a multidimensional observation signal matrix. Because the gastrointestinal slow wave signal is extremely weak and its frequency band is below 0.5Hz, this signal is first input to the signal conditioning circuit at the front end of the data processing and control host 300. The signal conditioning circuit is equipped with an instrumentation amplifier with a high common-mode rejection ratio to suppress power frequency interference generated by the power line and common-mode environmental noise. The instrumentation amplifier amplifies the original differential voltage signal by a preset factor to match the range of the subsequent analog-to-digital converter.

[0111] A second- or fourth-order analog anti-aliasing low-pass filter is connected in series after the amplification stage in the signal conditioning circuit. The cutoff frequency of the analog anti-aliasing low-pass filter is set to 10Hz to filter out high-frequency electromyographic signals and electromagnetic interference, preventing spectral aliasing during analog-to-digital conversion. The analog-conditioned signal is then sent to a multi-channel analog-to-digital converter to be converted into discrete raw digital electrophysiological sequences.

[0112] The signal preprocessing module 320 receives the raw digital electrophysiological sequence and first performs downsampling processing. The signal preprocessing module 320 reduces the sampling rate to a preset low-frequency sampling rate (e.g., 20Hz) through a decimation filter to reduce the computational load of subsequent algorithms and match the ultra-low frequency characteristics of gastrointestinal electrophysiological signals.

[0113] To extract gastrointestinal slow waves and preserve their original phase characteristics, the signal preprocessing module 320 performs zero-phase digital bandpass filtering on the downsampled sequence. The signal preprocessing module 320 employs an infinite impulse response (IIR) filter or a finite impulse response (FIR) filter and applies a forward-backward bidirectional filtering algorithm. The passband range of the digital bandpass filter is set between 0.05 Hz and 0.5 Hz to cover the main frequency band of the gastrointestinal slow waves, while filtering out extremely low-frequency baseline drift below 0.05 Hz.

[0114] To address the issue of frequency band overlap between respiratory interference (0.2Hz to 0.4Hz) and small intestinal slow wave frequencies (0.2Hz to 0.33Hz) within the passband, this invention employs a blind source separation technique based on multimodal reference. The specific process is as follows: First, the signal preprocessing module 320 uses a low-pass filter to extract baseline fluctuations with frequencies below 0.5Hz from the mechanical response signal acquired by the mechanical sensing module 210. These fluctuations are primarily composed of respiratory fluctuations in the subject's abdominal wall and are defined as the respiratory reference signal. Second, the filtered multi-channel abdominal surface bioelectrical signal is decomposed into several statistically independent source components using an Independent Component Analysis (ICA) algorithm. Subsequently, the Pearson correlation coefficient between each independent source component and the respiratory reference signal is calculated. Since respiratory motion produces artifacts accompanied by significant mechanical displacement of the body surface, and the mechanical projection of the intestinal slow wave electrical activity on the body surface is extremely weak and out of phase, independent source components with correlation coefficients higher than a preset threshold (e.g., 0.6) are identified as respiratory artifact components and removed by setting them to zero. Finally, the ECG artifact components containing periodic QRS complex characteristics were identified and zeroed out. The remaining independent source components were then used to reconstruct the denoised gastrointestinal electrical signal sequence using the inverse of the mixing matrix. This processing method effectively removes respiratory interference while preserving the effective small intestinal electrical signal in the same frequency band.

[0115] The signal preprocessing module 320 receives the mechanical vibration velocity sequence after noise reduction processing in the previous steps. and the denoised gastrointestinal electrical signal sequence Since both the mechanical vibration velocity sequence and the gastrointestinal electrical signal sequence are real-valued discrete-time signals and contain non-stationary time-varying characteristics,

[0116] The signal preprocessing module 320 uses the Hilbert transform algorithm to extend the real-valued signal to the complex domain, constructing the corresponding analytic signal. The signal preprocessing module 320 first processes the input arbitrary real-valued discrete signal... The discrete Hilbert transform is performed. The signal preprocessing module 320 employs a time-domain convolution algorithm based on a finite impulse response (FIR) filter. The discrete impulse response coefficients of the finite impulse response filter... Characteristics configured as an approximately ideal Hilbert transformer:

[0117]

[0118] The signal preprocessing module 320 processes the input signal sequence With the impulse response coefficient Perform discrete convolution operation to obtain

[0119] Conjugate orthogonal components of the original signal .

[0120] The signal preprocessing module 320 is based on the original signal With conjugate orthogonal components Constructing complex analytic signals The aforementioned complex

[0121] digital analytic signal The mathematical expression is:

[0122]

[0123] in, The imaginary unit, For instantaneous envelope, It is the instantaneous phase.

[0124] The signal preprocessing module 320 extracts the instantaneous phase sequence of the analytical signal using a four-quadrant arctangent function. For mechanical vibration velocity sequences... As a formula The corresponding mechanical instantaneous phase sequence is obtained by performing calculations. For gastrointestinal electrical signal sequences As a formula The corresponding electrophysiological instantaneous phase sequence is obtained by performing calculations. The instantaneous phase is calculated using the bivariate arctangent function (atan2):

[0125]

[0126] The output range of the atan2(·) function is: It can accurately distinguish the distribution of signals in the four quadrants of the complex plane.

[0127] The signal preprocessing module 320 calculates the instantaneous mechanical phase sequence. Electrophysiological instantaneous phase sequence Synchronous transmission to features

[0128] Extraction module 340 is used for subsequent phase-locking value calculation. Simultaneously, signal preprocessing module 320 extracts the instantaneous packets of the mechanical vibration velocity sequence.

[0129] Network (Right now The data is transmitted to the feature extraction module 340 to assist in calculating the dynamic mechanical input impedance.

[0130] Furthermore, the feature extraction module 340 is communicatively connected to the excitation control module 310 and the signal preprocessing module 320. The feature extraction module 340 receives a digital drive command sequence from the excitation control module 310, which serves as the system's input reference signal. Simultaneously, the feature extraction module 340 receives a mechanical vibration velocity sequence from the signal preprocessing module 320, which serves as the system's output response signal. In this embodiment, based on the linear response characteristics of the broadband vibration actuator within a preset operating frequency band, the feature extraction module 340 utilizes the amplitude characteristics of the digital drive command sequence to characterize the equivalent excitation force applied to the subject's abdomen.

[0131] The feature extraction module 340 performs synchronous time-domain windowing processing on the input digital drive command sequence and mechanical vibration velocity sequence. The time window length of the windowing processing is set to the duration of a single pulse train excitation. Consistency is ensured to guarantee that impedance calculations only apply to the excited steady-state response phase, eliminating noise interference during the quiescent period. The feature extraction module 340 calculates the root mean square (RMS) value of the digital drive command sequence and the root mean square (RMS) value of the mechanical vibration velocity sequence within each effective time window. The RMS value is calculated using a discrete-time series algorithm for a length of... sequence The calculation formula is as follows:

[0132]

[0133] The feature extraction module 340 calculates the dynamic mechanical input impedance based on the ratio of the root mean square value. The calculation model for the dynamic mechanical input impedance is defined as follows:

[0134]

[0135] in, This represents the sequence of digital drive instructions within a time window. This represents the sequence of mechanical vibration velocities within a time window. These are the preset system electromechanical conversion calibration coefficients;

[0136] Furthermore, considering the influence of the thickness of subcutaneous fat in the abdominal wall of the subject on the attenuation of mechanical waves, the feature extraction module 340 can introduce a normalization factor based on the subject's body mass index (BMI) to adjust the calculated dynamic mechanical input impedance. Corrections were made to eliminate the impact of individual body size differences on threshold determination.

[0137] The system electromechanical conversion calibration coefficient It is a comprehensive physical constant whose value depends on the voltage-to-force conversion sensitivity of the broadband vibration actuator and the reciprocal of the sensitivity of the mechanical sensing module. The feature extraction module 340 applies the above... This maps dimensionless numerical ratios to mechanical impedance values ​​with physical units (N·s / m).

[0138] The dynamic mechanical input impedance output by the feature extraction module 340 This reflects the attenuation and reflection characteristics of mechanical waves propagating in the abdominal medium. When the intestines become distended with gas and fluid accumulation due to paralysis or obstruction, the increased surface tension of the intestinal wall leads to an increase in the acoustic impedance of the medium, which is calculated by the feature extraction module 340. The value increases accordingly. The feature extraction module 340 will calculate the value... The data sequence is transmitted to the state recognition module 350 as the first dimension parameter for constructing the biophysical state feature vector.

[0139] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback, characterized in that, Includes the following steps: S1. A linear frequency modulated scanning signal is emitted to the abdomen of the subject, and the mechanical vibration response signal of the abdominal medium to the linear frequency modulated scanning signal is collected. The dominant mechanical response frequency characterizing the abdominal medium in the current state is calculated and determined based on the linear frequency modulated scanning signal and the mechanical vibration response signal. S2. Based on the dominant mechanical response frequency, an intermittent pulse train signal is synthesized, and mechanical perturbation is applied to the subject's abdomen using the dominant mechanical response frequency as the carrier. S3. During the application of the intermittent pulse train signal, the mechanical response signal of the subject's abdomen and the bioelectrical signal of the abdominal surface are acquired simultaneously; S4. The mechanical response signal and the abdominal surface bioelectric signal are preprocessed and analyzed to extract the mechanical instantaneous phase sequence, the electrophysiological instantaneous phase sequence, and the instantaneous envelope of the mechanical response signal, respectively. S5. Calculate the phase lock value based on the mechanical instantaneous phase sequence and the electrophysiological instantaneous phase sequence, and calculate the dynamic mechanical input impedance based on the instantaneous envelope and the digital drive command sequence of the intermittent pulse train signal; S6. Construct a two-dimensional feature vector by combining the phase-locked value and the dynamic mechanical input impedance, and output the intestinal function evaluation result based on the distribution area of ​​the two-dimensional feature vector in the preset threshold space.

2. The method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback according to claim 1, characterized in that, The specific process for determining the dominant mechanical response frequency in S1 is as follows: Calculate the cross power spectral density between the linear frequency modulated scanning signal and the mechanical vibration response signal, and calculate the auto-power spectral density of the linear frequency modulated scanning signal; The ratio of the cross power spectral density to the self power spectral density is calculated to construct the mechanical transfer function; The global maximum value of the amplitude of the mechanical transfer function is searched, and the frequency corresponding to the global maximum value of the amplitude is determined as the dominant mechanical response frequency.

3. The method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback according to claim 1, characterized in that, The specific process of synthesizing the intermittent pulse train signal in S2 is as follows: Generate a digital carrier sequence that is in the same frequency as the dominant mechanical response frequency; A rectangular window function is generated, which defines the duration of a single pulse train and the excitation repetition period. The digital carrier sequence is multiplied point by point with the rectangular window function to generate an adaptive excitation signal as the intermittent pulse train signal, and the duration covers 3 to 5 gastrointestinal slow wave cycles.

4. The method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback according to claim 1, characterized in that, The preprocessing of the abdominal bioelectrical signals in S4 includes: A digital bandpass filter with a passband of 0.05 Hz to 0.5 Hz is applied to the abdominal surface bioelectrical signal; the respiratory motion principal component is extracted from the synchronously acquired mechanical response signal as a reference signal; The filtered signal is decomposed into several independent source components using the independent component analysis algorithm; Calculate the time correlation coefficient between each independent source component and the reference signal, identify and zero the components whose time correlation coefficient is greater than a preset threshold as respiratory artifact components, and identify and zero ECG artifact components based on periodic QRS complex characteristics. The denoised gastrointestinal electrical signal sequence was reconstructed using the remaining independent source components.

5. The method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback according to claim 1, characterized in that, The specific process for extracting the mechanical instantaneous phase sequence and the electrophysiological instantaneous phase sequence in S4 is as follows: The mechanical response signal and the denoised gastrointestinal electrical signal sequence are subjected to discrete Hilbert transform using a finite impulse response filter, and the conjugate orthogonal components of the original signal are obtained by time-domain convolution operation. A complex analytic signal is constructed based on the original signal and the conjugate orthogonal components; The instantaneous phase of the complex analytic signal is calculated using the four-quadrant arctangent function, thereby obtaining the mechanical instantaneous phase sequence and the electrophysiological instantaneous phase sequence, respectively.

6. The method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback according to claim 1, characterized in that, The specific process for calculating the phase lock value in S5 is as follows: Receive the mechanical instantaneous phase sequence and the electrophysiological instantaneous phase sequence; Within a time window consistent with the duration of the intermittent pulse train signal, the average result vector length of the phase difference between the mechanical signal phase and the electrophysiological signal phase is calculated as the phase lock value, which is used to quantify the degree of synchronous response of bioelectrical activity to external mechanical stimulation.

7. The method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback according to claim 1, characterized in that, The specific process for calculating the dynamic mechanical input impedance in S5 is as follows: Within a time window that is consistent with the duration of the intermittent pulse train signal, the root mean square value of the digital drive command sequence and the root mean square value of the mechanical vibration velocity sequence are calculated respectively. Calculate the ratio of the root mean square value of the digital drive command sequence to the root mean square value of the mechanical vibration velocity sequence; The dynamic mechanical input impedance is obtained by multiplying the ratio by a preset system electromechanical conversion calibration coefficient, which is determined based on the voltage-to-force conversion sensitivity and the reciprocal of the sensing sensitivity.

8. The method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback according to claim 1, characterized in that, The preset threshold space is defined by the phase-locking determination threshold and the upper limit threshold of mechanical impedance; The specific process of outputting the intestinal function evaluation results in S6 includes: Compare the phase lock value with the phase lock determination threshold, and compare the dynamic mechanical input impedance with the upper limit threshold of the mechanical impedance; When the phase lock value is less than the phase lock determination threshold, it is determined to be in the first interval, and an electromechanical separation state identifier is output. When the phase lock value is greater than or equal to the phase lock determination threshold, and the dynamic mechanical input impedance is greater than the upper limit threshold of the mechanical impedance, it is determined to be the second interval, and a high tension coupling state identifier is output. When the phase lock value is greater than or equal to the phase lock determination threshold, and the dynamic mechanical input impedance is less than or equal to the upper limit threshold of the mechanical impedance, it is determined to be in the third interval, and the compliance recovery state indicator is output.

9. The method for identifying postoperative bowel dysfunction based on intestinal stimulation feedback according to claim 1, characterized in that, The frequency range of the mechanical disturbance is from 10Hz to 200Hz; the mechanical response signal is collected using an accelerometer, and the abdominal bioelectrical signal is collected using a surface electrode.

10. A postoperative intestinal function abnormality identification system based on intestinal stimulation feedback, used to execute the postoperative intestinal function abnormality identification method based on intestinal stimulation feedback as described in any one of claims 1 to 9, characterized in that, include: Adaptive stimulus execution unit, bimodal feedback acquisition unit, and data processing and control host; The adaptive stimulation execution unit is used to receive excitation control commands, generate mechanical vibration signals of preset frequency and waveform, and apply them to the abdominal surface of the subject. The dual-modal feedback acquisition unit is used to simultaneously acquire the mechanical response signal of the subject's abdomen and the bioelectrical signal of the abdominal surface. The data processing and control host is connected to the adaptive stimulus execution unit and the bimodal feedback acquisition unit.

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