Newborn gastric motility intelligent monitoring and intervention system based on deep learning

Abstract

The invention relates to the technical field of medical monitoring equipment, in particular to a newborn gastric motility intelligent monitoring and intervention system based on deep learning, which comprises a data acquisition module for acquiring electrogastrogram signals by adopting a flexible electrode array and acquiring body surface abdomen sound signals by adopting an acoustic sensor; the data processing module constructs an analysis framework comprising a topological feature extraction unit, a collaborative learning unit and a time sequence prediction unit based on a topological data analysis method, converts gastric motility signals into point cloud data in a high-dimensional feature space, calculates a persistent homology group, generates topological descriptors and constructs gastric motility state manifolds; gastric motility state recognition and prediction are realized through a deep neural network; according to the method, the internal structure characteristics of the gastric motility signal are effectively represented through a topological method, the detection rate of the gastric motility abnormality is remarkably increased, the gastric motility abnormality is predicted in advance, and a scientific basis is provided for clinical intervention.

Description

Technical Field

[0001] This invention relates to the field of medical monitoring equipment technology, specifically to a deep learning-based intelligent monitoring and intervention system for neonatal gastric motility, and particularly to a system that uses topological data analysis methods to process neonatal electrogastrogram signals and abdominal sound signals to achieve assessment and prediction of gastric motility status. Background Technology

[0002] Gastric motility disorders are a common health problem in newborns, especially premature infants. Traditional methods for monitoring gastric motility mainly rely on electrogastrography (EGG), but this method suffers from problems such as inconvenient signal acquisition, complex data analysis, and poor real-time performance. Furthermore, traditional analysis methods are often based on simple time-frequency domain feature extraction and threshold judgment, making it difficult to effectively identify complex patterns of abnormal gastric motility, particularly for newborns whose gastrointestinal systems are not yet fully developed, resulting in highly variable and individual-specific EGG signals.

[0003] Existing neonatal gastric motility monitoring systems have several problems: First, the signal acquisition devices are mostly rigid electrodes, which are not suitable for the sensitive skin of newborns; second, the analysis methods are mainly based on linear models and simple statistical characteristics, which cannot effectively capture the nonlinear dynamic characteristics of the gastric motility system; third, they lack personalized adaptability and are difficult to cope with the physiological differences between newborns; and finally, most of them are passive response systems and lack the ability to predict changes in gastric motility.

[0004] With the development of deep learning technology, it has shown great potential in the field of biomedical signal processing. However, deep learning methods also face challenges such as insufficient feature extraction, poor model interpretability, and limited generalization ability when processing high-dimensional nonlinear biological signals. Topological data analysis, as an emerging mathematical tool, can extract topologically invariant features from the intrinsic structural characteristics of data, providing a new perspective for the analysis of complex biological signals. Summary of the Invention

[0005] The purpose of this invention is to provide a newborn gastric motility intelligent monitoring and intervention system based on topological data analysis. By integrating topological data analysis and deep learning technology, it addresses the shortcomings of traditional gastric motility monitoring systems in terms of signal acquisition, feature extraction, individual adaptability, and predictive ability.

[0006] This invention proposes a deep learning-based intelligent monitoring and intervention system for neonatal gastric motility, comprising:

[0007] The data acquisition module is used to acquire electrogastrogram signals from newborns via flexible electrodes and to acquire abdominal sound signals from newborns via acoustic sensors.

[0008] The cloud storage module is communicatively connected to the data acquisition module and is used to store the electrogastrogram signal and the abdominal sound signal, and to compare and analyze the currently acquired electrogastrogram signal and abdominal sound signal with the stored historical data.

[0009] A data processing module, communicatively connected to the cloud storage module, is used to process the electrogastrogram signal and the abdominal sound signal based on a topological data analysis method. The data processing module includes:

[0010] The topological feature extraction unit is used to convert the electrogastrogram signal and the abdominal sound signal into point cloud data in a high-dimensional feature space, construct a multi-scale simple complex sequence, calculate persistent homology groups, and generate a topological descriptor characterizing the gastric motility state.

[0011] A collaborative learning unit is used to fuse the topological descriptor with traditional time-frequency features to identify the gastric motility state of newborns through a deep neural network;

[0012] The temporal prediction unit is used to construct a gastric motility state manifold, analyze the trajectory on the manifold, and predict the changing trend of gastric motility state.

[0013] The decision assessment and intervention module is communicatively connected to the data processing module. It is used to assess the gastric motility level based on the newborn's gastric motility status and predicted trends, combined with the newborn's basic characteristics, and generate a corresponding intervention plan.

[0014] Preferably, the flexible electrode acquisition structure in the data acquisition module includes:

[0015] A flexible electrode array, wherein the flexible electrode array adopts an integrated design of contact electrodes and a stretchable elastic substrate, including six gastric electrical signal acquisition electrodes evenly distributed on the abdomen of the newborn.

[0016] The signal conditioning unit is used to pre-amplify, filter, and perform power frequency notch processing on the signals acquired by the flexible electrode array.

[0017] An array fixation assembly, wherein the array fixation assembly is designed with a skin-friendly, non-conductive material and coated with an antibacterial resin, and is capable of stretching and deforming to adhere to the abdomen of a newborn.

[0018] Preferably, the array fixing assembly includes an elastic stretchable material and a silicone pad, and the electrode and the stretchable elastic substrate are integrally electroplated with conductive material.

[0019] Preferably, the processing steps of the topological feature extraction unit include:

[0020] The electrogastrogram signal and the abdominal sound signal are divided according to a predetermined time window;

[0021] Extract multidimensional feature vectors from each time window to form a point cloud in a high-dimensional feature space;

[0022] Vietoris-Rips complexes are constructed based on a preset distance threshold sequence;

[0023] Calculate the persistent homology group of the complex and record the appearance and disappearance of topological features;

[0024] Generate persistent graphs and topological descriptors to characterize the structural properties of gastric motility.

[0025] Preferably, the collaborative learning unit includes:

[0026] Topology-aware convolutional network structures contain multiple parallel branches that process topological features of different dimensions.

[0027] A multi-scale feature fusion mechanism is used to integrate gastric motility patterns at different time scales;

[0028] Attention mechanisms are used to dynamically adjust the weights of features in different topological dimensions;

[0029] Adaptive training strategies are used to balance the model's generalization ability and individual performance.

[0030] Preferably, the time-series prediction unit includes:

[0031] State manifold building components are used to represent gastric motility states as points on a manifold and to build a geometric representation of state transitions.

[0032] The trajectory analysis component is used to analyze the movement trajectory of gastric motility on the manifold;

[0033] A critical point identification component is used to detect topological singularities on a manifold and predict state abrupt changes;

[0034] The early warning generation component is used to generate hierarchical early warning information based on the rate of topological change.

[0035] Preferably, the decision evaluation and intervention module is used for:

[0036] Receive the gastric motility status assessment and prediction results output by the data processing module;

[0037] The degree of gastric motility abnormality is comprehensively assessed by combining the basic characteristics of the newborn, such as gestational age, weight, race, and feeding method.

[0038] Based on the cause and severity of abnormal gastric motility, an intervention plan is generated;

[0039] Based on real-time monitoring data, monitoring parameters and intervention measures are dynamically adjusted to achieve personalized intervention.

[0040] Preferably, the cloud storage module is used for:

[0041] Store historical data on gastric motility and intervention effects in newborns;

[0042] Based on the characteristics and attributes of newborns, relevant reference indicators are extracted;

[0043] Classify and label gastric motility data to establish a gastric motility feature database;

[0044] Incorporating the analysis results of newborns' gastric motility status into the electronic medical record system can support doctors' diagnostic and treatment decisions.

[0045] Preferably, the data processing module further includes a personalized topology pattern library for:

[0046] Based on the basic characteristics of newborns, an initial template is selected;

[0047] As monitoring data accumulates, individual-specific topological patterns are continuously updated;

[0048] Record the topological feature change patterns before the transition of gastric motility;

[0049] Establish a pattern similarity metric based on topological distance to support efficient retrieval.

[0050] Preferably, the system further includes a feedback module for:

[0051] Receive feedback from guardians or medical personnel regarding the implementation of intervention measures;

[0052] The intervention effect data is sent to the cloud storage module;

[0053] Based on feedback information, update the expert knowledge base and the personalized topology pattern library;

[0054] Improve the system's accuracy in identifying specific neonatal gastric motility characteristics and enhance intervention effectiveness.

[0055] The beneficial effects of this invention include:

[0056] 1. By designing a flexible electrode array and acoustic sensors, a comfortable and non-invasive gastric motility monitoring of newborns was achieved, significantly improving the comfort and signal quality of the monitoring process.

[0057] 2. By employing topological data analysis, features with topological invariance are extracted from the intrinsic structure of electrogastrography and abdominal sound signals, enhancing the system's ability to identify complex nonlinear gastric motility patterns and increasing the abnormality detection rate from 75% in traditional methods to 92%.

[0058] 3. Based on the collaborative learning mechanism of topological features and deep neural networks, an adaptive personalized model was established, which can continuously improve performance with the extension of usage time and effectively cope with the physiological differences among newborns.

[0059] 4. By constructing a gastric motility state manifold and topological singularity detection technology, early prediction of gastric motility abnormalities can be achieved, with an average early warning issued 30-45 minutes in advance, providing sufficient preparation time for clinical intervention.

[0060] 5. A prediction-based precision intervention mechanism was established, which reduced unnecessary interventions by 40% and shortened the gastric motility recovery time by 35%, significantly improving the quality of neonatal care. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the overall system architecture of the present invention;

[0062] Figure 2 This is a flowchart of the topological feature extraction unit in the data processing module.

[0063] Figure 3 This is a schematic diagram illustrating the construction and prediction of the gastric motility manifold.

[0064] Figure 4 A flowchart for the decision assessment and intervention module;

[0065] Figure 5 This is a schematic diagram of data flow between modules in the system of the present invention. Detailed Implementation

[0066] Please refer to Figures 1-5 The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0067] like Figure 1 As shown, the present invention provides a deep learning-based intelligent monitoring and intervention system for neonatal gastric motility, including a data acquisition module 1, a cloud storage module 2, a data processing module 3, and a decision evaluation and intervention module 4. The modules are connected through communication to form a complete monitoring and intervention closed loop.

[0068] Data acquisition module 1 is used to acquire electrogastrogram signals from newborns via flexible electrodes and to acquire abdominal sound signals from newborns via acoustic sensors. The flexible electrode acquisition structure in data acquisition module 1 includes a flexible electrode array, a signal conditioning unit, and an array fixing assembly.

[0069] The flexible electrode array employs an integrated design of contact electrodes and a stretchable elastic substrate, comprising six gastric electrical signal acquisition electrodes evenly distributed on the neonatal abdomen. Preferably, these six electrodes are evenly distributed according to the anatomical characteristics of the neonatal abdomen, covering the stomach, duodenum, and upper jejunum to comprehensively capture gastric electrical activity signals. In clinical applications, this electrode array can adapt to various neonatal positional changes, maintaining stable signal acquisition even during neonatal activity. The flexible electrodes are made of Ag / AgCl material, achieving stable contact with the neonatal skin through conductive gel or dry electrode technology. The electrodes have a diameter of 10 mm and a thickness of 0.5 mm, effectively capturing weak gastric electrical signals (typically in the range of 50-500 μV). The signal conditioning unit is used to pre-amplify, filter, and perform power frequency notch filtering on the signals acquired by the flexible electrode array. In one embodiment of the invention, the preamplifier gain is set to 1000 times, and the passband of the bandpass filter is 0.5-9 cpm (cycles per minute) to specifically filter the characteristic frequency range of the gastric electrical signal. Simultaneously, a 50Hz (or 60Hz, depending on the local power grid frequency) notch filter is set to effectively suppress power frequency interference. Furthermore, the signal conditioning unit includes a 16-bit analog-to-digital converter with a sampling rate set to 4Hz, sufficient to capture the slow-wave characteristics of the gastric electrical signal.

[0070] The array fixation component is designed with skin-friendly, non-conductive materials and coated with antibacterial resin, allowing it to stretch and deform to adhere to the newborn's abdomen. Specifically, the array fixation component includes an elastic, stretchable material and a silicone pad. The elastic, stretchable material is made of medical-grade thermoplastic polyurethane (TPU) with a stretchability of up to 150%, adapting to various body shapes and movements of the newborn's abdomen. The silicone pad is made of medical-grade silicone material, 2mm thick, with a Shore A hardness of 10-15, providing a comfortable feel and stable contact. The electrodes and the stretchable elastic base are integrated with a conductive material electroplating design, with the addition of 20-30% silver nanoparticles to enhance conductivity while maintaining the material's flexibility. In practical applications, the array fixation component maintains a stable fit during the newborn's sleep, feeding, and daily care without causing irritation or pressure marks on the newborn's skin.

[0071] The acoustic sensor employs a piezoelectric design with a sensitivity of -40 dB and a frequency response range of 20-2000 Hz, effectively capturing surface sound signals generated by gastrointestinal motility. The sensor is fixed to the neonatal abdomen slightly to the right of the midline using medical-grade tape, a position typically most sensitive to gastrointestinal sounds. The sound signal is acquired at 44.1 kHz and then downsampled to 1 kHz to reduce data volume while preserving key frequency information. In clinical practice, acoustic signals provide direct evidence of gastrointestinal motility, particularly during post-feeding digestion, and are of significant value in assessing gastric motility.

[0072] Cloud storage module 2 communicates with data acquisition module 1 to store electrogastrogram (EGG) signals and abdominal acoustic signals. It compares and analyzes the currently acquired EGG and abdominal acoustic signals with previously stored historical data. Cloud storage module 2 uses a hierarchical storage architecture: raw signal data is stored in a cache for 24 hours, then transferred to standard storage for 3 months, and finally compressed and archived. The comparative analysis uses a sliding window method, comparing the current 15 minutes of data with historical data from the same period (e.g., the same time period the previous day) to generate a trend report. Furthermore, cloud storage module 2 implements data encryption and access control to ensure patient data security, while allowing medical staff to view monitoring data and analysis results at any time via authorized terminals.

[0073] The data processing module 3 is communicatively connected to the cloud storage module 2 and is used to process electrogastrogram signals and abdominal sound signals based on topological data analysis methods. The data processing module 3 is the core innovative part of this invention, including a topological feature extraction unit 31, a collaborative learning unit 32, a time series prediction unit 33, and a personalized topological pattern library 34.

[0074] The processing steps of the topology feature extraction unit 31 are as follows: Figure 2 As shown, the process includes segmenting the electrogastrogram signal and the abdominal sound signal according to a predetermined time window; extracting multidimensional feature vectors from each time window to form a point cloud in a high-dimensional feature space; constructing a Vietoris-Rips complex based on a preset distance threshold sequence; calculating the persistent homology group of the complex to record the appearance and disappearance of topological features; and generating a persistent graph and topological descriptor to characterize the structural properties of gastric motility.

[0075] Specifically, the topology feature extraction unit 31 first segments the continuous signal into a 60-second sliding window, with a window overlap rate set to 50%. For each time window, a 10-dimensional feature vector is extracted, including the peak frequency of the gastric electrical signal power spectrum, the peak-to-trough difference of the gastric electrical signal power spectrum, the energy ratio of the gastric electrical signal, the high-frequency energy ratio of the gastric electrical signal, the high-frequency / low-frequency / medium-frequency / DC components of the gastric electrical signal, and the mean amplitude / frequency of sound on the abdominal surface. These feature vectors form point cloud data in 10-dimensional space. In practical applications, this step can convert the original continuous-time signal into a discrete set of feature points, laying the foundation for subsequent topology analysis. For example, when monitoring a preterm infant with a gestational age of 34 weeks, the system can extract a set of feature vectors every minute to record the changing patterns of the newborn's gastric motility.

[0076] Next, the topological feature extraction unit 31 calculates the distance between point pairs in the point cloud and constructs a distance matrix. This invention uses weighted Chebyshev distance, as shown in the following formula:

[0077] ,

[0078] in, For point and points The weighted Chebyshev distance between them and They are two 10-dimensional feature vectors. and It is the first of the vectors One portion, It is the first The weight coefficients of each feature express and The absolute difference, This indicates the operation of taking the maximum value. In this invention, weights are assigned according to clinical importance. For example, the weight of the peak frequency of the gastric electrical signal power spectrum is 0.25, the weight of the peak-to-trough difference of the gastric electrical signal power spectrum is 0.20, the weight of the gastric electrical signal energy ratio is 0.15, and the weights of other features decrease sequentially. In actual monitoring, this weighting method can highlight the more clinically important gastric motility features and improve the detection rate of abnormal patterns.

[0079] Then, the topological feature extraction unit 31 sets a distance threshold sequence. ( (A geometric sequence, with a first value of 0.01 and subsequent values ​​of 2.0), for each threshold... Construct the corresponding Vietoris-Rips complex. Specifically, when the distance between two points is less than or equal to... When the points are connected, an edge is added between them; when all three points are connected pairwise by edges, a 2-simplex (triangle) is formed; this process continues to form higher-dimensional simplexes. Here, Indicates the first A distance threshold, This indicates the length of the threshold sequence. In practical applications, this multi-scale analysis method can capture topological features at different scales, such as in monitoring gastric motility disorders, where the small-scale threshold ( arrive It may capture local frequency fluctuations, while large-scale thresholds ( arrive This may reflect changes in the overall gastric motility pattern.

[0080] Based on the constructed complex sequence, the topological feature extraction unit 31 calculates persistent homology groups, recording the appearance (life) and disappearance (death) of topological features (connected components, loops, and holes) in different dimensions. The persistence graph represents these topological features as points on a two-dimensional plane, with the horizontal axis representing the threshold at which the feature appears and the vertical axis representing the threshold at which the feature disappears. The lifetime (persistence) of a feature is obtained by subtracting the horizontal axis from the vertical axis, representing the stability of that topological feature. In actual monitoring, stable topological features (long lifetime) usually correspond to the main patterns of gastric motility, while short-lived features may reflect noise or transient changes. For example, when monitoring the normal gastric electrical activity of a full-term newborn, the system may observe a stable oscillation pattern of about 3 cpm, corresponding to a long-lived feature in the persistence graph.

[0081] Finally, the topology feature extraction unit 31 extracts topology descriptors from the persistent graph, including:

[0082] 1. Durability measures: Statistical measures for calculating characteristic lifetimes, such as mean, variance, maximum, etc.

[0083] 2. Betti number sequence: records the changes in connectivity of each dimension at each threshold;

[0084] 3. Topological entropy: An information entropy measure that quantifies the complexity of persistent graphs.

[0085] These topological descriptors form a vectorized representation for subsequent identification of gastric motility status. In clinical applications, these descriptors can effectively distinguish different types of gastric motility status. For example, newborns with normal gastric motility typically exhibit lower topological entropy and stable Betty number sequences, while newborns with disordered gastric motility exhibit higher topological entropy and unstable Betty number variations.

[0086] Collaborative Learning Unit 32, etc. Figure 4 As shown, it includes a topology-aware convolutional network structure, a multi-scale feature fusion mechanism, an attention mechanism, and an adaptive training strategy.

[0087] The topology-aware convolutional network structure comprises three parallel branches, processing 0-dimensional, 1-dimensional, and 2-dimensional topological features respectively. Each branch contains four convolutional layers, with kernel sizes increasing with the number of layers (3×3, 5×5, 7×7, 9×9) to capture topological features at different scales. The number of output channels per layer is set to 32, 64, 128, and 256, extracting higher-level feature representations layer by layer. In practical applications, this parallel branch structure can simultaneously analyze different topological properties of gastric motility signals, improving the model's expressive power. For example, 0-dimensional topological features (connected components) may reflect the basic periodicity of gastric electrical signals, 1-dimensional features (loops) may reflect frequency modulation patterns, while 2-dimensional features (holes) may capture more complex signal interaction patterns.

[0088] The multi-scale feature fusion mechanism employs a feature pyramid structure to simultaneously process gastric motility patterns across four timescales: 5 seconds, 15 seconds, 30 seconds, and 60 seconds. Features from different scales are fused using an adaptive weighting method, enabling the network to simultaneously monitor both instantaneous changes and long-term trends. In clinical monitoring, this multi-scale analysis can comprehensively capture the dynamic characteristics of gastric motility. For example, during neonatal feeding, a short timescale (5 seconds) may reflect the initial gastric response, a medium timescale (15-30 seconds) may reflect the adjustment process of accepting food, while a long timescale (60 seconds) may reflect overall digestive capacity.

[0089] The attention mechanism dynamically adjusts the weights of features in different topological dimensions, as shown in the following formula:

[0090] ,

[0091] in, It is the first Attention weights for 3D topological features It is a learnable parameter vector. It is the current hidden state vector. Represents the natural exponential function. This represents the transpose of a vector. This represents the summation operation. Attention weights. The value of is between 0 and 1, and the sum of the weights of the three dimensions is 1. In this way, the network can automatically adjust the attention given to different dimensions of topological features according to different situations. In practical applications, for newborns with normal gastric motility, the system may pay more attention to 0-dimensional features; while for newborns with gastric motility disorders, the system may increase the attention given to 1-dimensional and 2-dimensional features to capture more complex abnormal patterns.

[0092] The adaptive training strategy consists of three phases: the initial phase is guided by a general topological pattern, focusing on the model's generalization ability; the intermediate phase introduces individual-specific topological features to balance generalization and personalization; and the later phase primarily uses individual historical data to enhance the model's adaptability to specific newborns. The learning rate is dynamically adjusted based on the stability of the topological features, as shown in the following formula:

[0093] ,

[0094] in, It is the first The learning rate of the step, This is the initial learning rate (set to 0.001). This is the attenuation coefficient (set to 0.1). It is a measure of the stability of the current topological features. Represents the natural exponential function. This indicates the number of training steps. Higher stability results in a slower learning rate decay. In clinical applications, this adaptive training strategy can automatically adjust model parameters based on individual differences among newborns. For example, for full-term infants with stable gastric motility patterns, the system may use a lower learning rate to avoid overfitting; while for preterm infants with significantly varying gastric motility patterns, the system may use a higher learning rate to quickly adapt to the new patterns.

[0095] The time series prediction unit 33 includes a state manifold construction component, a trajectory analysis component, a critical point identification component, and an early warning generation component. For example... Figure 3 As shown, the time-series prediction unit 33 constructs a gastric motility state manifold, analyzes the trajectory on the manifold, and predicts the changing trend of gastric motility state.

[0096] The state manifold construction component represents gastric motility states as points on a manifold, with coordinates determined by both topological and temporal features. First, historical gastric motility state sequences are collected to form a high-dimensional point set. Then, a local linear embedding algorithm is applied to reduce the high-dimensional data to a 3D visualization space. Next, adjacency relationships are established, connecting temporally adjacent and feature-similar state points. Finally, local geometric properties on the manifold, such as curvature and geodesic distance, are calculated. In practical applications, the state manifold can visually demonstrate the evolutionary trajectory of gastric motility states. For example, when monitoring a newborn's process from feeding to digestion, the system can observe the trajectory of state points gradually transitioning from the "feeding response zone" to the "normal digestion zone" on the manifold.

[0097] The core steps of the local linear embedding algorithm include:

[0098] 1. Find the k nearest neighbors of each high-dimensional point (k=12);

[0099] 2. Calculate the reconstructed weight matrix W such that each point can be represented by a linear combination of its neighbors:

[0100] ,

[0101] in It is the i-th high-dimensional point. It is the reconstruction weight of point j to point i, and , Describes the Euclidean norm. This indicates a minimization operation. This step determines the reconstruction weights by solving a least-squares problem.

[0102] 3. Computing low-dimensional embeddings This ensures that the same reconstruction relationship is maintained:

[0103] ,

[0104] in, It is the low-dimensional representation of the i-th point. These are the reconstruction weights calculated in the first step, and they remain unchanged. This step obtains the low-dimensional embedding coordinates by solving the eigenvalue problem.

[0105] In clinical applications, the Local Linear Embedding (LLE) algorithm can map complex, high-dimensional gastric motility features into a visualized three-dimensional space, helping healthcare professionals intuitively understand changes in the gastric motility of newborns. For example, when monitoring multiple newborns in the NICU (Neonatal Intensive Care Unit), the system can generate a state manifold diagram that clearly displays the gastric motility status and trends of each newborn.

[0106] The trajectory analysis component analyzes the movement trajectory of gastric motility on the manifold, enabling short-term predictions (5-15 minutes), medium-term predictions (15 minutes-2 hours), and long-term trend analysis (2-24 hours). Short-term predictions are based on the local geometry of the current state on the manifold, considering the velocity and acceleration of the trajectory to estimate the trend of state change. Medium-term predictions analyze the evolution patterns of historically similar trajectories and adjust them in conjunction with individual neonatal characteristics. Long-term trend analysis identifies key regions on the manifold, such as attractors, saddle points, and unstable regions, predicting the long-term stability of gastric motility. In practical applications, this multi-scale prediction can provide timely decision support for clinical interventions. For example, short-term predictions may detect decreased gastric motility in newborns within 15 minutes after feeding, medium-term predictions may indicate that gastric motility will return to normal after 1 hour, and long-term trend analysis may reveal a diurnal variation in gastric motility, with higher activity typically occurring at night.

[0107] The critical point identification component detects topological singularities on the manifold and predicts abrupt state changes. It monitors topological changes on the manifold, identifies precursors to abrupt state changes, calculates the rate of change of trajectory curvature to detect abnormal acceleration or deceleration, and analyzes stability abrupt changes in topological features. The formula for calculating the rate of change of trajectory curvature is as follows:

[0108] ,

[0109] in, It is the rate of change of curvature. It is the curvature of the trajectory on the manifold. It is time. This is the time interval (set to 5 minutes). This represents the derivative of curvature with respect to time. When When the gastric motility exceeds a preset threshold (e.g., 0.2 g / min), the system anticipates a potential abrupt change and triggers an alert. In clinical applications, this curvature-based alert mechanism can detect abnormal changes in gastric motility in advance. For example, when monitoring a premature infant at risk of gastric motility disorder, the system may detect abnormal changes in the trajectory curvature 20-30 minutes before actual gastric motility disturbance, thus issuing an early warning and giving healthcare professionals sufficient time to intervene.

[0110] The early warning generation component generates tiered early warning information based on the rate of topological change, including the expected occurrence time, severity, and recommended interventions. Early warnings are divided into four levels (0-3), determined by the speed, magnitude, and direction of the state change. For example, when a trajectory is detected rapidly approaching a known area of ​​gastric motility disorder, and the topological stability decreases rapidly (at a rate exceeding 30% / hour), the system will issue a Level 2 warning, predicting that gastric motility disorder may occur within one hour. In practical applications, this tiered early warning system helps healthcare professionals prioritize tasks according to urgency. For instance, a Level 0 warning may only require routine observation, a Level 1 warning may require increased monitoring frequency, a Level 2 warning may require preparation for intervention, and a Level 3 warning may require immediate intervention.

[0111] The personalized topology pattern library 34 is used to store and update the specific topology patterns of each newborn. Based on the newborn's basic characteristics (such as gestational age and weight), the closest initial template is selected from the general library; as monitoring data accumulates, the individual-specific topology patterns are continuously updated; topology feature changes before the transition of gastric motility are specifically recorded; a topology pattern similarity measure based on Wasserstein distance is established to support efficient retrieval. In clinical applications, the personalized topology pattern library can adapt to individual differences in newborns, improving the accuracy of monitoring and prediction. For example, for a preterm infant with a gestational age of 34 weeks and a weight of 1800g, the system first selects a general template of newborns with a gestational age of 32-36 weeks and a weight of 1500-2000g as the initial model, and then gradually adjusts the model parameters as monitoring data accumulates to form a personalized model for that newborn.

[0112] The Wasserstein distance is a measure of the difference between two probability distributions and is used to compare the similarity of persistent graphs. The formula is as follows:

[0113] ,

[0114] in, yes and Between two persistent graphs Wasserstein distance of order and There are two persistent graphs. It is all possible arrive The set of transmission plans, It is a point and points The distance between them It is the order of the distance (usually taken as...). or ), Indicates the infimum (minimum value). This indicates an integration operation. In this invention, the integration operation is used. The Wasserstein distance (Earth Mover's Distance) is used to measure the similarity between two topological patterns. In practical applications, the Wasserstein distance can effectively measure the differences between different gastric motility states. For example, the system may find that a newborn's current gastric motility pattern is significantly different from its pattern three days ago (Wasserstein distance greater than 0.5), suggesting a possible change in gastric motility function that requires further evaluation.

[0115] The decision assessment and intervention module 4 communicates with the data processing module 3 to assess the gastric motility level based on the newborn's gastric motility status and predicted trends, combined with the newborn's basic characteristics, and generate corresponding intervention plans. For example... Figure 4 As shown, the decision assessment and intervention module 4 receives the gastric motility status assessment and prediction results output by the data processing module 3; it comprehensively assesses the level of gastric motility abnormality by combining the basic characteristics of the newborn such as gestational age, weight, race, and feeding method; it generates an intervention plan based on the cause and severity of the gastric motility abnormality; and it dynamically adjusts the monitoring parameters and intervention measures according to real-time monitoring data to achieve personalized intervention.

[0116] In the assessment of gastric motility, the system categorizes gastric motility into four classes: normal, decreased, disordered, and disordered frequency. Each class is further divided into three levels based on severity: mild, moderate, and severe. The assessment criteria are adjusted according to the gestational age of the newborn. For example, for full-term infants (gestational age ≥37 weeks), the normal gastric electrical frequency range is 2.5-3.5 cpm, while for preterm infants (gestational age <37 weeks), this range is adjusted to 2.0-3.0 cpm. In practical applications, this stratified assessment system provides a more refined description of gastric motility status. For example, for a preterm infant with a gestational age of 32 weeks, if a gastric electrical frequency of 1.8 cpm is monitored, which is below the normal range but close to the lower limit, the system would assess it as "mild decreased gastric motility"; if the frequency drops below 1.5 cpm, it would be assessed as "moderate decreased gastric motility"; and if the frequency is unstable and fluctuates within a range exceeding 1.0 cpm, it may be assessed as "moderate disordered gastric motility".

[0117] Intervention plans are generated based on the type and severity of gastric motility abnormalities, including non-pharmacological interventions (such as postural adjustments and massage) and pharmacological intervention recommendations. For mild gastric motility reduction, the system may recommend right lateral decubitus positioning and gentle abdominal massage; for moderate gastric motility disturbances, it may recommend temporarily suspending feeding and consulting a physician about the need for prokinetic drugs; for severe gastric motility abnormalities, the system will trigger an emergency alert and recommend immediate medical intervention. In clinical practice, this predictive intervention strategy improves the timeliness and specificity of interventions. For example, when the system predicts that a newborn may experience decreased gastric motility in 30 minutes, it may recommend adjusting the feeding schedule or taking preventative measures in advance, rather than intervening after the problem actually occurs.

[0118] The present invention also includes a feedback module 5, which is used to receive feedback information from guardians or medical personnel on the implementation of intervention measures; send intervention effect data to cloud storage module 2; update expert knowledge base and personalized topology pattern library 34 based on feedback information; and improve the system's accuracy in identifying specific neonatal gastric motility characteristics and intervention effect.

[0119] Feedback module 5 collects intervention implementation status and effect evaluation through a simple user interface, including whether the intervention was implemented as recommended, the implementation time, and the observed effects. This feedback information is used in two ways: firstly, to update the personalized topology pattern library 34 and optimize the prediction model for specific newborns; secondly, to update the global knowledge base and improve the overall performance of the system. Feedback evaluation uses a 5-point scale, with 1 point indicating no effect and 5 points indicating significant effect. When the average score of an intervention measure on a specific newborn is below 3 points, the system automatically adjusts the intervention recommendation strategy, prioritizing interventions with higher historical scores. In practical applications, this feedback mechanism enables the system to continuously self-optimize and improve the success rate of interventions. For example, if the system finds that the right lateral decubitus position (average score 4.2 points) is more effective than abdominal massage (average score 2.8 points) for a newborn, it will prioritize the right lateral decubitus position in subsequent intervention recommendations.

[0120] Figure 5 This demonstrates the data flow between modules in the system of this invention. In the signal acquisition → preprocessing → topology analysis link, gastric electrical signals are acquired at a frequency of 4Hz, and sound signals are acquired at 44.1kHz and then downsampled to 1kHz. Preprocessing includes bandpass filtering (gastric electrical signals: 0.5-9cpm, sound signals: 20-2000Hz), baseline correction, and artifact removal. The processing window is 60 seconds, with an overlap rate of 50%. Topology calculation is performed every 15 seconds for a complete analysis and every 5 seconds for an incremental update. In clinical applications, this data flow mode can balance real-time performance and computational efficiency, providing stable support for continuous monitoring. For example, when monitoring multiple newborns in a NICU ward, the system can process multiple signals simultaneously and automatically issue a technical alarm when signal quality deteriorates (such as electrode detachment), prompting medical staff to check the equipment.

[0121] In the topology analysis → neural network → state assessment chain, the topology descriptor is converted into a 320-dimensional normalized vector, and topological features are processed for four consecutive time windows at a time. State assessment is output every 5 seconds, including the current state classification and confidence level. To prevent frequent state fluctuations, the system applies a time smoothing algorithm, confirming a state change only if a new state persists for three consecutive assessment cycles (15 seconds). In actual monitoring, this smoothing process reduces false alarms and improves system reliability. For example, when a newborn's temporary activity causes signal interference, the system does not immediately change the state assessment but waits for the signal to stabilize before making a judgment, avoiding unnecessary alarms.

[0122] In the status assessment → time-series prediction → decision support chain, the system maintains a 24-hour sliding window of gastric motility history. Prediction updates are triggered by status changes or at fixed intervals (15 minutes). Decision recommendations are generated based on the severity, probability of occurrence, and time urgency of the prediction, and are continuously optimized based on intervention feedback. In clinical practice, this decision support mechanism can provide personalized intervention recommendations. For example, for a preterm infant with a history of feeding intolerance, the system may generate a more conservative feeding plan and provide early warnings at the slightest change in gastric motility, while for a full-term infant with stable gastric motility, the system may adopt a more lenient standard.

[0123] In summary, the neonatal gastric motility intelligent monitoring and intervention system based on topological data analysis provided by this invention achieves high-precision assessment and prediction of neonatal gastric motility status through flexible electrode acquisition structure, topological feature extraction, deep neural network collaborative learning and manifold prediction technology, providing a scientific basis for clinical intervention and significantly improving the quality of neonatal gastrointestinal health management.

[0124] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A deep learning based intelligent monitoring and intervention system for neonatal gastric motility, characterized in that, The method comprises the following steps: A data acquisition module is used to collect a gastric electrical signal of a newborn through a flexible electrode and collect a body surface abdominal sound signal of the newborn through an acoustic sensor; A cloud storage module is in communication connection with the data acquisition module, and is used to store the gastric electrical signal and the body surface abdominal sound signal, and compare and analyze the currently collected gastric electrical signal and body surface abdominal sound signal with the stored historical data; A data processing module is in communication connection with the cloud storage module, and is used to process the gastric electrical signal and the body surface abdominal sound signal based on a topological data analysis method, and the data processing module comprises: A topological feature extraction unit is used to convert the gastric electrical signal and the body surface abdominal sound signal into point cloud data in a high-dimensional feature space, construct a multi-scale simple complex body sequence, calculate a persistent homology group, and generate a topological descriptor representing a gastric motility state; A collaborative learning unit is used to fuse the topological descriptor and a traditional time-frequency feature, and identify the gastric motility state of the newborn through a deep neural network; A time series prediction unit is used to construct a gastric motility state manifold, analyze a trajectory on the manifold, and predict a change trend of the gastric motility state; A decision evaluation and intervention module is in communication connection with the data processing module, and is used to evaluate a gastric motility level based on the gastric motility state and the predicted trend of the newborn, in combination with basic characteristics of the newborn, and generate a corresponding intervention scheme.

2. The system of claim 1, wherein, The flexible electrode acquisition structure in the data acquisition module comprises: A flexible electrode array, wherein the flexible electrode array adopts an integrated design of a contact electrode and a stretchable elastic substrate, and includes six gastric electrical signal acquisition electrodes uniformly distributed on the abdomen of the newborn; A signal conditioning unit is used to preamplify, filter and process power frequency notch of the signal acquired by the flexible electrode array; An array fixing assembly, wherein the array fixing assembly is designed with a skin-friendly non-conductive material and coated with an antibacterial resin, and can be stretched and deformed to be attached to the abdomen of the newborn.

3. The system of claim 2, wherein, The array fixing assembly comprises an elastic stretchable material and a silica gel pad, and the electrode and the stretchable elastic substrate are integrally electroplated with a conductive material.

4. The system of claim 1, wherein, The processing steps of the topological feature extraction unit comprise: The gastric electrical signal and the body surface abdominal sound signal are divided according to a predetermined time window; A multi-dimensional feature vector is extracted from each time window to form a point cloud set in a high-dimensional feature space; A Vietoris-Rips complex body is constructed based on a preset distance threshold sequence; The persistent homology group of the complex body is calculated to record the appearance and disappearance of topological features; A persistent diagram and a topological descriptor are generated to represent the structural characteristics of the gastric motility state.

5. The system of claim 1, wherein, The collaborative learning unit comprises: A topologically aware convolutional network structure containing multiple parallel branches to process different dimensional topological features respectively; A multi-scale feature fusion mechanism is used to integrate gastric motility patterns of different time scales; An attention mechanism is used to dynamically adjust the weights of different topological dimensional features; An adaptive training strategy is used to balance the generalization ability and individual performance of the model.

6. The system of claim 1, wherein, The time series prediction unit comprises: A state manifold construction component is configured to represent the gastric motility state as a point on a manifold, and to construct a geometric representation of state transition; A trajectory analysis component is configured to analyze the moving trajectory of the gastric motility state on the manifold; A critical point identification component is configured to detect topological singular points on the manifold, and to predict state mutations; An early warning generation component is configured to generate hierarchical early warning information based on the topological change rate.

7. The system of claim 1, wherein, The decision evaluation and intervention module is configured to: Receive the gastric motility state evaluation and prediction results output by the data processing module; Combine the basic characteristics of the newborn, such as gestational age, body weight, race, and feeding method, to comprehensively evaluate the level of gastric motility abnormality; Generate an intervention plan based on the causes and severity of gastric motility abnormalities; Adjust the monitoring parameters and intervention measures dynamically according to the real-time monitoring data to achieve personalized intervention.

8. The system of claim 1, wherein, The cloud storage module is configured to: Store the gastric motility history data and intervention effect data of the newborn; Extract the corresponding reference indicators according to the characteristic attributes of the newborn; Classify and label the gastric motility data to establish a gastric motility feature database; Incorporate the gastric motility state analysis results of the newborn into the electronic medical record system to support the doctor's diagnosis and treatment decisions.

9. The system of claim 1, wherein, The data processing module further includes a personalized topological pattern library, which is configured to: Select an initial template based on the basic characteristics of the newborn; Continuously update the individual-specific topological patterns as monitoring data accumulates; Record the topological feature change pattern before the gastric motility state transition; Establish a pattern similarity measure based on topological distance to support efficient retrieval.

10. The system of claim 1, wherein, The system further includes a feedback module, which is configured to: Receive feedback information on the implementation of intervention measures from guardians or medical personnel; Send the intervention effect data to the cloud storage module; Update the expert knowledge base and the personalized topological pattern library based on the feedback information; Improve the recognition accuracy of the system for the gastric motility characteristics of a specific newborn and the effectiveness of the intervention.

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