Physiological signal analysis method and system based on multi-scale cross-guided neural network

Abstract

The present application belongs to the technical field of physiological signal analysis, and discloses a physiological signal analysis method and system based on a multi-scale cross-guided neural network, which comprises the steps of acquiring and cutting physiological signals into multiple data partitions; multiple different scale parallel convolutional neural network submodules are used to perform high-dimensional mapping on the physiological signals of the multiple data partitions to obtain nonlinear vectors, learn the correlation between different convolutional neural network submodules and quantify the correlation into a weight vector, perform parallel operation on the nonlinear vectors based on the weight vector, perform point convolution operation on the parallel operation results to obtain information vectors; the information vectors are decoded to obtain feature vectors; and the physiological signals are analyzed by using the feature vectors. The present application has the ability of classification and regression, does not need to re-optimize hyperparameters when generalized to different tasks, is lightweight, can be deployed on edge devices, and has good accuracy and effectiveness in different clinical scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of physiological signal analysis, and in particular to a physiological signal analysis method and system based on a multi-scale cross-guidance neural network. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Physiological signals are an important research topic in the medical field. By analyzing physiological signals, doctors can understand a patient's specific activities, emotions, and health status, which has far-reaching implications for clinical diagnosis, treatment monitoring, and drug testing. The use of wearable devices has led to longer-term physiological signal recording. At the same time, the widespread use of electronic health records has diversified the types of physiological signal analysis tasks. Physiological signal analysis in clinical practice primarily relies on manual annotation by experts, which is inefficient and prone to fatigue errors. The long-term duration and diverse tasks further increase the burden of physiological signal analysis. Therefore, artificial intelligence (AI) is needed in clinical practice to improve the accuracy and efficiency of physiological signal analysis.

[0004] Although AI has clinical applications in multiple fields, including disease detection, physiological parameter monitoring, and disease prediction, in physiological signal analysis, from the perspective of task nature, AI primarily performs two basic tasks: classification and regression. Classification involves dividing input signals into predefined categories based on their characteristics, while regression involves finding the relationship between independent and dependent variables. These differences have led to existing research focusing on developing different specialized artificial intelligence (ANI). For example, analyzing electrocardiogram (ECG) signals to identify atrial fibrillation (AF) and analyzing photoplethysmography (PPG) signals to achieve non-invasive blood pressure (BP) estimation require specific classification and regression models, respectively. In specific clinical scenarios, ANI alleviates the clinical burden of physiological signal analysis and provides patients with better treatment options.

[0005] However, in clinical practice, comorbidity is a common phenomenon, which means that patients suffer from two or more diseases at the same time, such as AF and hypertension. When facing patients with comorbidities, AI needs to complete various types of physiological signal analysis tasks to provide comprehensive decision support, especially for patients in the intensive care unit. AI needs to complete regression tasks to achieve heart rate estimation, blood oxygen saturation estimation, and BP estimation, and needs to complete classification tasks to achieve organ failure warning, myocardial ischemia identification, and arrhythmia detection. This requires the algorithm to have both classification and regression capabilities. Therefore, existing ANIs have limitations that cannot be ignored: (1) When the same ANI is generalized to different tasks, the performance will be significantly reduced; (2) Integrating different ANIs to solve multiple tasks is a heavyweight solution and has limited flexibility in actual deployment; (3) ANIs need to continuously optimize hyperparameters when facing complex clinical environments. For example, the hyperparameter designs of machine learning (ML) algorithms such as random forest (RF), CatBoost, and extreme learning machine cannot be shared in different fields. Specifically, in AF identification and BP estimation, the trees, nodes, and depth of random forest (RF) are different. Deep learning (DL) algorithms such as Transformer, Mamba, and Kolmogorov-Arnold Networks are used in natural language processing, image recognition, and time series prediction. However, these tasks require different network structures, optimization methods, and basic operators. Furthermore, these models are heavyweight and difficult to deploy on edge devices. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a physiological signal analysis method and system based on a multi-scale cross-guidance neural network, which has the capabilities of classification and regression. It does not require re-optimization of hyperparameters when generalized to different tasks and has good robustness. It has a lightweight design and can be deployed on edge devices. It has good accuracy and effectiveness in different clinical scenarios.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a physiological signal analysis method based on a multi-scale cross-guidance neural network, comprising the following steps:

[0009] Acquire physiological signals and divide the physiological signals into multiple data partitions;

[0010] Multiple parallel convolutional neural network submodules of different scales are used to perform high-dimensional mapping on the physiological signals of multiple data partitions to obtain nonlinear vectors. The correlation between different convolutional neural network submodules is learned and quantified into weight vectors. The nonlinear vectors are then operated in parallel based on the weight vectors. Point convolution operations are performed on the parallel operation results to obtain information vectors.

[0011] Decode the information vector to obtain a feature vector that meets the specific physiological signal analysis task;

[0012] Physiological signal analysis is performed using feature vectors that meet specific physiological signal analysis tasks.

[0013] As an optional implementation, sparse synaptic connections are used to strengthen the information interaction between different convolutional neural network sub-modules, and the correlation between different convolutional neural network sub-modules is learned and quantized into a weight vector by embedding global information and adaptive correction.

[0014] As an optional implementation, a multi-scale depth-wise separable convolutional neural network is used to perform parallel operations on nonlinear vectors, and a point convolution operation is performed on the parallel operation results.

[0015] As an optional implementation, the multi-scale deep convolutional neural network introduces skip connections, and the output of the shallow deep convolutional neural network is directly added to the input of the deep deep convolutional neural network to slow down gradient changes.

[0016] As an optional implementation, the multi-scale deep convolutional neural network is constructed by a DC operator, which applies the idea of ​​channel-by-channel convolution.

[0017] As an optional implementation, a TCNN is used to decode the information vector, where the TCNN is constructed using a transposed convolution operator.

[0018] In a second aspect, the present invention provides a physiological signal analysis system based on a multi-scale cross-guidance neural network, comprising:

[0019] The data acquisition and partitioning module is configured to: acquire physiological signals and divide the physiological signals into multiple data partitions;

[0020] The encoding module is configured to: use multiple parallel convolutional neural network submodules of different scales to perform high-dimensional mapping on the physiological signals of multiple data partitions to obtain nonlinear vectors, learn the correlations between different convolutional neural network submodules and quantize them into weight vectors, perform parallel operations on the nonlinear vectors based on the weight vectors, and perform point convolution operations on the parallel operation results to obtain an information vector;

[0021] The decoding module is configured to: decode the information vector to obtain a feature vector that meets the specific physiological signal analysis task;

[0022] The analysis module is configured to perform physiological signal analysis using a feature vector that meets a specific physiological signal analysis task.

[0023] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0025] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which implements the method described in the first aspect when executed by a processor.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present disclosure proposes a physiological signal analysis method and system based on a multi-scale cross-guided neural network, which has both classification and regression capabilities and can complete physiological signal analysis tasks with two different attributes, classification and regression, without changing any hyperparameters. The present disclosure adopts a multi-scale mechanism for feature extraction, in which a small-scale receptive field is used to learn local detail information and a large-scale receptive field is used to learn global semantic information. The multi-scale mechanism enables the present method to extract diverse features, thereby extracting the features required for different tasks, and exhibits good versatility in different tasks.

[0028] This disclosure proposes a physiological signal analysis method and system based on a multi-scale cross-guidance neural network, using a distributed approach to address the high computational cost of multi-scale mechanisms. The backbone of this method is divided into multiple independent submodules. Each submodule is constructed using a lightweight convolution operator. Optimization of the backbone and operators reduces the computational cost of physiological signal analysis. The distributed architecture enables MSCG-Net to achieve lower computational costs and faster inference speed, enabling deployment on edge devices.

[0029] This paper proposes a physiological signal analysis method and system based on a multi-scale cross-guidance neural network, which introduces sparse synaptic connections to enhance information interaction between submodules in a distributed neural network and solve the problem of information islands. The submodules of MSCG-Net are regarded as biological neurons, and an attention mechanism with negligible computational cost is used to simulate synapses. Any submodule is weighted by the remaining submodules to achieve cross-guidance. The cross-guidance mechanism based on sparse synaptic connections gives MSCG-Net a stronger information interaction capability, thereby selecting complementary and non-redundant features.

[0030] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0032] Figure 1 Flowchart of the physiological signal analysis method based on multi-scale cross-guidance neural network provided in Example 1 of the present invention;

[0033] Figure 2 An experimental flow chart for verifying the physiological signal analysis method based on a multi-scale cross-guidance neural network provided in Example 1 of the present invention;

[0034] Figure 3 A comparison chart of ROC curves and AUC values ​​of the physiological signal analysis method based on a multi-scale cross-guidance neural network provided in Example 1 of the present invention and other methods in classification tasks;

[0035] Figure 4 A graph showing the regression results of the physiological signal analysis method based on a multi-scale cross-guidance neural network provided in Example 1 of the present invention;

[0036] Figure 5 Class activation map for multi-scale mechanism in classification task;

[0037] Figure 6 These are the experimental results of estimating SBP using the physiological signal analysis method based on a multi-scale cross-guidance neural network provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0040] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0041] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0042] Example 1

[0043] like Figure 1 As shown, this embodiment provides a physiological signal analysis method based on a multi-scale cross-guidance neural network, comprising the following steps:

[0044] S1 acquires physiological signals and divides the physiological signals into multiple data partitions;

[0045] S2 uses multiple parallel convolutional neural network submodules of different scales to perform high-dimensional mapping on the physiological signals of multiple data partitions to obtain nonlinear vectors. It learns the correlation between different convolutional neural network submodules and quantizes them into weight vectors. It performs parallel operations on the nonlinear vectors based on the weight vectors and performs point convolution operations on the parallel operation results to obtain information vectors.

[0046] S3 decodes the information vector to obtain a feature vector that meets the specific physiological signal analysis task;

[0047] S4 performs physiological signal analysis using feature vectors that meet specific physiological signal analysis tasks.

[0048] This paper proposes a versatile multi-scale cross-guidance neural network (MSCG-Net), which does not require re-optimization of hyperparameters when applied to classification and regression and has good robustness when generalized to different tasks.

[0049] like Figure 1 As shown in (a), the input physiological signal undergoes data partitioning and is then fed sequentially into a multi-scale convolutional neural network (CNN) for high-dimensional mapping, an encoder consisting of a multi-scale depthwise separable convolutional neural network (DCNN), and a decoder constructed from a transposed convolutional neural network (TCNN) to achieve feature extraction based on a multi-scale mechanism. The multi-scale mechanism is used to parse physiological signals to extract features required for different tasks.

[0050] Then, different CNNs, DCNNs, and TCNNs were computed in parallel, and the multi-scale DCNNs used channel-by-channel convolution to optimize computational costs based on distributed thinking. A distributed architecture was used to optimize the convolution operator and network backbone to reduce the computational cost of the multi-scale mechanism.

[0051] Finally, a squeeze and excitation network is used to learn feature correlations between sub-modules and quantize them into weights to construct a cross-guidance mechanism based on the squeeze and excitation network. The squeeze and excitation network is used to strengthen the information interaction of sub-modules in the distributed architecture to improve robustness. The squeeze and excitation network is part of the sparse synaptic connection.

[0052] MSCG-Net uses multi-scale CNN to map physiological signals to high dimensions to facilitate DCNN analysis, such as Figure 1 (b) is shown. The calculation process of CNN is shown in formula (1).

[0053]

[0054] * represents convolution calculation, sk represents the scale of the receptive field, BN stands for batch normalization, y scale,sk Represents the output of CNN with scale sk. After projection by CNN, the physiological signal is converted into a high-dimensional nonlinear vector, where the dimension changes to

[0055] The role of multi-scale DCNN is to encode the temporal changes and spatial distribution of physiological signals into information vectors. MSCG-Net uses multi-scale DCNN to encode the temporal changes and spatial distribution of physiological signals into information vectors. scale,sk Perform parallel operations, as shown in formula (2-3),

[0056]

[0057] represents the tensor product, w DC,sk represents the weight of DC, b DC,sk represents the DC bias, represents the weight of the point convolution, Represents the bias of the point convolution, y DC,sk Represents the output of DC with scale sk, y DCNN,sk Represents the output of the DCNN with scale sk. Each scale of DCNN repeats the above operation to decompose the nonlinear vector output by CNN.

[0058] Each DCNN uses two sets of DC and two sets of point convolution, and the depth of MSCG-Net is 24. In order to avoid gradient vanishing and exploding, this paper introduces skip connections. The output of the shallow DCNN is directly added to the input of the deep DCNN to slow down the gradient change. The details are as follows: Assume that the input of the mth DCNN is First, Z is output through formula (2-3) Then, Input the m+1th DCNN to get the output at last, Input the m+2th DCNN to get the output

[0059] TCNN decodes the information vector into a feature vector that meets the specific physiological signal analysis task. MSCG-Net uses the transposed convolution operator to construct TCNN. Assume that the output of DCNN is The set composed of TCNN is TCNN i right Decode to get feature vector Different feature vectors are flattened and element-wise summed to obtain a feature set for physiological signal classification and regression. The process is shown in formula (4-6):

[0060]

[0061] is the transposed convolution, is the weight matrix, k inv is the size of the deconvolution kernel, θ(*) is the ELU, is element-wise addition. Different TCNNs use the same receptive field to equally decode vectors of different scales.

[0062] The multi-scale mechanism faces the problems of high computational cost and slow inference speed. This paper applies the distributed concept to data partitioning, convolution operator optimization and backbone network design to solve the above problems. The idea of ​​distributed computing is to split a highly complex system into multiple simple submodules to achieve parallel computing. Specifically: (1) Based on Embedding, any physiological signal segment is divided into s data partitions, such as Figure 1 (b) (2) The DC operator is used to replace the standard convolution (RC) operator to construct a multi-scale DCNN. (3) CNNs, DCNNs, and TCNNs of different scales are calculated in parallel.

[0063] Assume that any physiological signal segment is After data partitioning, X becomes CNN analysis of s scales X and X seg The FLOPs are shown in formula (7-8),

[0064]

[0065]

[0066] K i is the size of the receptive field, C iis the number of convolution operators, and n is the number of sampling points of the physiological signal. After data partitioning, FLOPs is reduced to 1 / s.

[0067] DCNN is constructed by DC operator. DC operator applies the idea of ​​channel-by-channel convolution, and its parameter amount and FLOPs are smaller than RC operator. The parameter amount Par DC and FLOPs DC As shown in formula (9-10).

[0068] Par DC =K DC ×I DC +C Po ×I DC (9)

[0069] FLOPs DC =K DC ×n×I DC +I DC ×n×C Po (10)

[0070] K DC Represents the receptive field size of DC, I DC Represents the number of input channels, C Po Represents the number of point convolution operators. Parameters Par when RC analyzes physiological signals RC and FLOPs RC As shown in formula (11-12).

[0071] Par RC =I RC ×K RC ×C RC (11)

[0072] FLOPs RC =I RC ×K RC ×n×C Rm (12)

[0073] K RC Represents the receptive field size of RC, I RC Represents the number of input channels, C RC Represents the number of RC operators. When the hyperparameters of DC and RC are consistent, that is, K DC =K RC , I DC =I RC , C Po =C RC ,

[0074]

[0075] According to formula (13-14), when analyzing the same physiological signal, the number of parameters and FLOPs of the DC operator is only (1 / C RC +1 / K RC ). In the present disclosure, C RC >>2, K RC ≥3.

[0076] Parallel computing is used between different scales. The FLOPs of parallel (PC) and non-parallel (NPC) are shown in formula (15-16).

[0077]

[0078] FLOPs NPC =I in ×K coder ×n×C out (16)

[0079] I in is the number of input channels, C out is the number of output channels, K coder is the size of the receptive field. After adopting the parallel method, FLOPs is reduced to 1 / s of the original.

[0080] The distributed computing architecture inevitably destroys the temporal correlation of physiological signals and weakens the information interaction within MSCG-Net. Inspired by the feedforward neural network in the brain nervous system, this paper designs a cross-guidance mechanism based on sparse synaptic connections to strengthen the information interaction between MSCG-Net submodules, such as Figure 1 (c) shown.

[0081] The present disclosure cleverly applies the sparse synaptic connections of the brain nervous system to the sub-modules of the distributed network, thereby increasing the information interaction capability of the distributed network at a low computational cost. In MSCG-Net, different sub-modules are regarded as biological neurons, and an attention mechanism with negligible computational cost is used to simulate synapses. Specifically: any set of nonlinear transformations is fed into SE-Net after batch normalization and ELU processing. SE-Net learns the correlation between different sub-modules by embedding global information and adaptive correction and quantizes it into a weight vector. Each sub-module is weighted by the remaining sub-modules to achieve cross-guidance.

[0082] Assume that the nonlinear transformation output of any group of submodules is a represents the length of the vector, and b represents the dimension of the vector. SE-Net will Convert to weight vector

[0083] first, After compression operation F sq (·) is converted into a global information vector The process is shown in formula (17):

[0084]

[0085] After the above operations, we get

[0086] Then, SE-Net Perform the excitation operation F ex (·) to learn the importance of each vector dimension and obtain the weight vector of the corresponding submodule The process is shown in formula (18):

[0087]

[0088] and is a trainable parameter vector, σ(·) is Sigmiod, and δ(·) is Relu. After the above operations, the weight set is obtained

[0089] Finally, the (s-1) weight vectors are summed and weighted to the output of the specified submodule, thereby obtaining the output of the information interaction of multiple submodules The process is shown in formula (19):

[0090]

[0091] This paper verifies the performance of MSCG-Net in classification and regression tasks based on AF recognition experiments and BP estimation experiments. The experimental process is as follows: Figure 2 As shown in Figure 2, the MIT-BIH Atrial Fibrillation Database (AFDB) and the Standardized Patient Heart Dataset (SPHDB) created in this paper were used in AF recognition experiments. The Medical Information Mart for Intensive Care Database (MIMICDB) was used in BP estimation experiments. AFDB, SPHDB, and MIMICDB suffer from class imbalance, scarce labeled data, and noise, which are consistent with clinical scenarios.

[0092] The evaluation indicators of the AF recognition experiment are accuracy (Acc), specificity (Spe), sensitivity (Sen), precision (Pre) and F1 score. The calculation process is shown in formula (20-24).

[0093]

[0094] TP is true positive, TN is true negative, FN is false negative, and FP is false positive. This disclosure uses Receiver Operating Characteristic (ROC) curve and Area Under Curve (AUC) value to evaluate the clinical significance of MSCG-Net in classification.

[0095] The evaluation indicators of the BP estimation experiment are MAE and RMSE, and the calculation process is shown in formulas (25) and (26). This paper uses the standards of the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) to evaluate the clinical application value of MSCG-Net in regression.

[0096]

[0097] BP truei Represents the real BP value, BP outi Represents the estimated BP value.

[0098] The experimental data results of MSCG-Net in the classification task show that MSCG-Net is less affected by category imbalance and has good stability. MSCG-Net can complete the classification of physiological signals in clinical scenarios with category imbalance and maintain high generalization in clinical scenarios with scarce labeled data. This paper evaluates the clinical application value of MSCG-Net in physiological signal classification through ROC curve and AUC value. The results are as follows Figure 3 The ROC curve of AFDB is shown as Figure 3 As shown in (a), the AUC value range is [99.89%, 99.94%]. Figure 3 As shown in (bf), the AUC values ​​range from [97.55% to 99.81%]. The ROC curve and AUC values ​​demonstrate that MSCG-Net has high clinical application value in the classification of physiological signals and can adapt to complex and changing clinical environments.

[0099] The experimental data results of MSCG-Net in the regression task show that MSCG-Net can maintain accurate and stable regression performance when facing a variety of subjects in clinical environments, and meet clinical standards in the regression of physiological signals. This paper conducts statistical analysis on the regression results, such as Figure 4As shown in the figure, the mean error (ME) is as low as 0.19 mmHg, and the 95% limit of agreement (LoA) is [-9.89, 10.26] mmHg. This shows that the regression results of MSCG-Net are highly consistent with the true values. The Pearson Correlation Coefficient (PCC) between the regression results and the true values ​​reached 0.9570, and the p-value was less than 10. -15 This shows that the regression results of MSCG-Net have a high correlation with the true value and are statistically significant.

[0100] The class activation diagram of the multi-scale mechanism in the classification task is as follows Figure 5 As shown in the figure, scales 3 and 5 focus only on a single RR interval or a complete heartbeat, while scales 7 and 9 capture two adjacent heartbeats and three or more consecutive heartbeats, respectively. P wave recognition requires a complete heartbeat, while determining an absolutely irregular RR interval requires observing at least three or more heartbeats. Multi-scale mechanisms complement each other in classification, enabling MSCG-Net to mimic the diagnostic mechanisms of clinical experts.

[0101] This paper analyzes the effects of different scales on various regression tasks. The most representative example is the experiment of estimating SBP, such as Figure 6 As shown. SBP is located at the junction of the ascending and descending branches, which requires MSCG-Net to capture both the rapid rise and slow fall of the slope. Therefore, this requires both small and large scales to jointly analyze physiological signals. Across all subjects, ablating small scale K=5 and large scale K=9 resulted in errors of 29.91% and 30.62%, respectively. Similarly, DBP had similar results to SBP, with ablating scales K=3 and K=7 resulting in errors of 30.98% and 27.22%, respectively. MAP represents the average level of BP, which requires MSCG-Net to capture outliers and periodicity of BP. Therefore, the four scales have similar importance, and the errors caused by ablating the four scales are all around 25%.

[0102] Furthermore, across the four categories of subjects, the higher the severity of hypertension, the greater the degree of BP oscillation, and the greater the effect of small scales. Conversely, the effect of large scales was higher. For example, ablation scales K = 3 and K = 5 resulted in a 77.22% error in SBP for subjects in category S2. Ablation scales K = 7 and K = 9 resulted in a 65.09% error in SBP for subjects in category N. Furthermore, the error distributions generated by the four scales within each category varied somewhat, demonstrating that the multi-scale mechanism can capture the specificity of different subjects. The multi-scale mechanism plays a complementary role in regression, enabling MSCG-Net to better address diverse clinical needs.

[0103] The MSCG-Net proposed in the present disclosure is a versatile lightweight algorithm that can complete physiological signal analysis tasks of two different attributes, classification and regression, without changing any hyperparameters. The multi-scale mechanism enables MSCG-Net to have the ability to extract diverse features, thereby showing good versatility in different tasks. The distributed architecture enables MSCG-Net to have lower computing costs and faster reasoning speed, so that it can be deployed on edge devices. The cross-guidance mechanism based on Sparsesynaptic connectivity enables MSCG-Net to have stronger information interaction capabilities to select complementary and non-redundant features. Compared with existing ANI, MSCG-Net has better versatility and robustness in physiological signal analysis, and has higher flexibility and cost-effectiveness in integrated deployment. The present disclosure contributes to the development of general artificial intelligence and has important clinical significance.

[0104] Example 2

[0105] This embodiment provides a physiological signal analysis system based on a multi-scale cross-guidance neural network, including:

[0106] The data acquisition and partitioning module is configured to: acquire physiological signals and divide the physiological signals into multiple data partitions;

[0107] The encoding module is configured to: use multiple parallel convolutional neural network submodules of different scales to perform high-dimensional mapping on the physiological signals of multiple data partitions to obtain nonlinear vectors, learn the correlations between different convolutional neural network submodules and quantize them into weight vectors, perform parallel operations on the nonlinear vectors based on the weight vectors, and perform point convolution operations on the parallel operation results to obtain an information vector;

[0108] The decoding module is configured to: decode the information vector to obtain a feature vector that meets the specific physiological signal analysis task;

[0109] The analysis module is configured to perform physiological signal analysis using a feature vector that meets a specific physiological signal analysis task.

[0110] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0111] In further embodiments, there is also provided:

[0112] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed by the processor, wherein when the computer instructions are executed by the processor, the method described in Example 1 is performed. For the sake of brevity, no further details are given here.

[0113] It should be understood that in this embodiment, the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0114] The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0115] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is performed.

[0116] The method in Example 1 can be directly implemented as a hardware processor, or can be implemented using a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, it will not be described in detail here.

[0117] A computer program product includes a computer program, which implements the method described in embodiment 1 when executed by a processor.

[0118] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions contained in program modules, which are executed in a device on a real or virtual processor of a target to perform the process / method described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided between program modules as needed. The machine-executable instructions for the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0119] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the computer or other programmable data processing device, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0120] In the context of the present invention, computer program code or related data can be carried by any appropriate carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0121] Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A physiological signal analysis method based on a multi-scale cross-guidance neural network, characterized in that: The following steps are involved: Acquire physiological signals and divide the physiological signals into a plurality of data partitions, wherein the physiological signals include blood pressure signals and electrocardiogram signals; Multiple parallel convolutional neural network submodules of different scales are used to perform high-dimensional mapping of physiological signals of multiple data partitions to obtain nonlinear vectors. The correlation between different convolutional neural network submodules is learned and quantified into weight vectors. Specifically: Sparse synaptic connections are used to strengthen the information interaction between different convolutional neural network submodules. SE-Net is used to embed global information and adaptive correction to learn the correlation between different submodules and quantize it into a weight vector. Each submodule is weighted by the other submodules to achieve cross-guidance. Based on the weight vector, the nonlinear vector is parallelized and the point convolution operation is performed on the parallel operation result to obtain the information vector, specifically: A multi-scale depth-wise separable convolutional neural network is used to perform parallel operations on nonlinear vectors, and point convolution operations are performed on the parallel operation results; The multi-scale depth-wise separable convolutional neural network introduces skip connections, where the output of the shallow depth-wise convolutional neural network is directly added to the input of the deep depth-wise convolutional neural network to slow down gradient changes. The multi-scale depth-separable convolutional neural network is constructed by a DC operator, which applies a channel-by-channel convolution calculation method; Decode the information vector to obtain a feature vector that meets the specific physiological signal analysis task; Perform physiological signal analysis using feature vectors that meet specific physiological signal analysis tasks; The multi-scale cross-guidance neural network includes: a multi-scale convolutional neural network for high-dimensional mapping, an encoder composed of a multi-scale depth-separable convolutional neural network, and a decoder constructed by a transposed convolutional neural network.

2. The physiological signal analysis method based on a multi-scale cross-guidance neural network according to claim 1, characterized in that: The information vector is decoded using TCNN, which is constructed using a transposed convolution operator.

3. A physiological signal analysis system based on a multi-scale cross-guidance neural network, characterized in that: The method for analyzing physiological signals based on a multi-scale cross-guidance neural network according to any one of claims 1 to 2 comprises: The data acquisition and partitioning module is configured to: acquire physiological signals and divide the physiological signals into multiple data partitions; The encoding module is configured to: use multiple parallel convolutional neural network submodules of different scales to perform high-dimensional mapping on the physiological signals of multiple data partitions to obtain nonlinear vectors, learn the correlations between different convolutional neural network submodules and quantize them into weight vectors, perform parallel operations on the nonlinear vectors based on the weight vectors, and perform point convolution operations on the parallel operation results to obtain an information vector; The decoding module is configured to: decode the information vector to obtain a feature vector that meets the specific physiological signal analysis task; The analysis module is configured to perform physiological signal analysis using a feature vector that meets a specific physiological signal analysis task.

4. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 2 is completed.

5. A computer-readable storage medium, characterized in that Used to store computer instructions, which, when executed by a processor, complete the method according to any one of claims 1 to 2.

6. A computer program product, characterized in that The invention comprises a computer program, which is used to implement the method according to any one of claims 1 to 2 when executed by a processor.

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