PPG signal noise reduction and reconstruction method based on causal decoupling network

Abstract

The invention provides a PPG signal noise reduction and reconstruction method based on a causal decoupling network. The method comprises the following steps: inputting an original PPG signal into a signal preprocessing module for preprocessing; inputting the preprocessed feature vector into a causal decoupling network module, and carrying out signal feature decoupling by using a Transform encoder and a latent factor projector; and based on a signal reconstruction module, decoding and reconstructing the decoupled potential factors by using a multi-layer perceptron decoder to obtain a reconstructed PPG signal. According to the method, the causal decoupling technology is creatively applied, the mixed potential representation of the PPG signal is decomposed into a plurality of independent physiological factors, interference of complex noise such as motion artifacts and ambient light can be effectively separated and removed, meanwhile, key physiological information such as heart rate and respiration is reserved to the maximum extent, and the method is suitable for popularization and application. And the signal-to-noise ratio, the feature separability and the overall quality of the signal are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical signal processing, and in particular to a PPG signal denoising and reconstruction method based on a causal decoupling network. Background Art

[0002] The PPG signal is a non-invasive physiological signal that measures changes in human blood volume through a photoelectric sensor. It is widely used in heart rate monitoring, blood pressure estimation, blood oxygen saturation measurement and other fields. The accuracy and reliability of the PPG signal are crucial for medical diagnosis and health monitoring. However, the PPG signal is easily interfered by various noises during the acquisition process, such as ambient light noise, motion artifacts, equipment noise, etc. These noises will reduce the quality of the signal and affect the subsequent estimation of physiological parameters and disease diagnosis.

[0003] Traditional PPG signal processing methods mainly rely on filter design, signal smoothing and other techniques to remove noise. However, these methods can only process specific types of noise and may lose important physiological information while removing noise. In recent years, with the rapid development of deep learning technology, signal processing methods based on deep learning have gradually attracted attention. Deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been used for denoising and feature extraction of PPG signals. However, most of these methods focus on the processing of a single type of noise, and have limited effects on PPG signal processing in complex noise environments.

[0004] In addition, PPG signals contain information about a variety of physiological factors, such as heart rate, respiration, vascular status, etc. These factors are intertwined, making signal feature extraction and analysis more complicated. How to effectively separate these physiological factors and retain important physiological information while denoising is an important challenge facing the current PPG signal processing field.

[0005] In recent years, causal decoupling technology has made significant progress in fields such as image processing and speech signal processing. The goal of causal decoupling is to decompose the input signal into multiple independent potential factors, each of which corresponds to a specific physical or physiological process. In this way, noise can be removed more effectively while retaining the key information in the signal. However, there is no research on applying causal decoupling technology to PPG signal processing. Summary of the invention

[0006] In view of the above situation, the main purpose of the present invention is to propose a PPG signal denoising and reconstruction method based on a causal decoupling network to solve the above technical problems.

[0007] The present invention proposes a PPG signal denoising and reconstruction method based on a causal decoupling network, the method comprising the following steps: Step 1: construct a causal decoupling network module based on the Transformer architecture, and construct a signal reconstruction module based on the signal reconstructor. The signal preprocessing module, the causal decoupling network module, the signal reconstruction module and the model training module constitute the reconstruction model; The causal decoupling network module includes a Transformer encoder and a latent factor projector, the Transformer encoder includes a multi-layer perceptron and a feedforward network, and the signal reconstruction module includes a multi-layer perceptron decoder; Step 2: Input the original PPG signal into the signal preprocessing module for preprocessing to obtain the preprocessed feature vector; Step 3: Input the preprocessed feature vector into the causal decoupling network module and use the Transformer encoder and latent factor projector to decouple the signal features to obtain the decoupled latent factors; Step 4: Based on the signal reconstruction module, the decoupled latent factors are decoded and reconstructed using a multi-layer perceptron decoder to obtain a reconstructed PPG signal; Step 5: Based on the model training module, a decoupling loss is constructed according to the decoupled latent factors, and a reconstruction loss is constructed according to the original PPG signal and the reconstructed PPG signal; The joint decoupling loss and the reconstruction loss are weighted and combined, and input into the Adam optimizer to update the parameters of the reconstruction model to obtain the updated reconstruction model; The final reconstruction result is obtained based on the updated reconstruction model.

[0008] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention innovatively applies causal decoupling technology to decompose the mixed potential representation of PPG signals into multiple independent physiological factors, which can effectively separate and remove the interference of complex noises such as motion artifacts and ambient light, while maximally retaining key physiological information such as heart rate and breathing, significantly improving the signal-to-noise ratio, feature separability and overall quality of the signal; 2. The present invention adopts a context-aware Transformer architecture and combines it with conditional layer normalization technology to effectively capture the long-range temporal dependencies and dynamic change characteristics in PPG signals. It also incorporates external contextual conditions such as demographics and treatment information for adaptive processing, thereby improving the accuracy of feature representation and the robustness to signal changes in different individuals and under different states. 3. The present invention not only ensures the high consistency (high fidelity) between the reconstructed signal and the original clean signal by jointly optimizing the reconstruction loss and the decoupling loss function, but also enforces the mutual independence of the potential representations of various physiological factors, which is conducive to the subsequent deeper, more reliable and more interpretable analysis of specific physiological components (such as heart rate variability, respiratory modulation, etc.), thereby improving the accuracy of downstream applications (such as disease diagnosis and health status monitoring).

[0009] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description or learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flowchart of the steps of the PPG signal denoising and reconstruction method based on the causal decoupling network proposed in the present invention.

[0011] Figure 2 This is the overall framework diagram of the PPG signal denoising and reconstruction method based on the causal decoupling network proposed in the present invention.

[0012] Figure 3 This is a signal preprocessing module framework diagram of the PPG signal denoising and reconstruction method based on a causal decoupling network proposed in the present invention.

[0013] Figure 4 This is a causal decoupling module architecture diagram of the PPG signal denoising and reconstruction method based on a causal decoupling network proposed in the present invention.

[0014] Figure 5 This is a diagram of the context-aware encoder architecture of the PPG signal denoising and reconstruction method based on a causal decoupling network proposed in the present invention.

[0015] Figure 6 This is a latent factor decomposition architecture diagram of the PPG signal denoising and reconstruction method based on causal decoupling network proposed in the present invention.

[0016] Figure 7 This is a signal reconstruction module architecture diagram of the PPG signal denoising and reconstruction method based on a causal decoupling network proposed in the present invention.

[0017] Figure 8 This is a diagram of the model training module architecture of the PPG signal denoising and reconstruction method based on a causal decoupling network proposed in the present invention. DETAILED DESCRIPTION

[0018] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout are the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0019] These and other aspects of the embodiments of the present invention will be apparent with reference to the following description and accompanying drawings. In these descriptions and accompanying drawings, some specific implementations of the embodiments of the present invention are specifically disclosed to provide some ways to implement the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0020] See also Figure 1 The embodiment of the present invention proposes a PPG signal denoising and reconstruction method based on a causal decoupling network, the method comprising the following steps: Step 1: construct a causal decoupling network module based on the Transformer architecture, and construct a signal reconstruction module based on the signal reconstructor. The signal preprocessing module, the causal decoupling network module, the signal reconstruction module and the model training module constitute the reconstruction model; Among them, the causal decoupling network module includes a Transformer encoder and a latent factor projector, the Transformer encoder includes a multi-layer perceptron and a feedforward network, and the signal reconstruction module includes a multi-layer perceptron decoder.

[0021] Step 2: Input the original PPG signal into the signal preprocessing module for preprocessing to obtain the preprocessed feature vector.

[0022] See also Figure 2 and Figure 3 In step 2, the original PPG signal is input into the signal preprocessing module for preprocessing to obtain the preprocessed feature vector, which specifically includes the following steps: The original PPG signal is input into the signal preprocessing module, and the original PPG signal is divided and processed using one-dimensional convolution to obtain the divided local feature vector; Add the position code to obtain the added position code; The divided local feature vector and the added position code are added to obtain the local feature vector after adding the position code.

[0023] The original PPG signal is input into the signal preprocessing module, and the original PPG signal is divided and processed using one-dimensional convolution to obtain the local feature vector after division. The relationship between the corresponding process is as follows: ; in, represents the local feature vector after division, It means that after one-dimensional convolution operation, represents the original PPG signal, represents the convolution kernel size, represents the step length; In the step of adding the position code and obtaining the added position code, the relationship between the corresponding process is as follows: ; in, Indicates at location and even-numbered feature dimension index The positional encoding value at represents the sine function, represents the exponential part, Indicates at location and odd feature dimensions The positional encoding value at represents the cosine function, represents the feature dimension index, represents the position encoding function, Represents the position index, Represents the model dimension; It should be noted that Used to scale frequency.

[0024] In the step of adding the divided local feature vector and the added position code to obtain the local feature vector after adding the position code, the corresponding process has the following relationship: ; in, Represents the local feature vector after adding position encoding.

[0025] Step 3: Input the preprocessed feature vector into the causal decoupling network module and use the Transformer encoder and latent factor projector to decouple the signal features to obtain the decoupled latent factors.

[0026] See also Figure 4 , Figure 5 and Figure 6 In step 3, the preprocessed feature vector is input into the causal decoupling network module, and the signal feature is decoupled using the Transformer encoder and the latent factor projector to obtain the decoupled latent factor, which specifically includes the following steps: S101, input external condition information and use a multi-layer perceptron to encode context information to generate a final condition vector; S102, inputting the local feature vector after adding the position encoding into the Transformer encoder for tensor representation to obtain a first represented tensor; S103, performing a conditional layer normalization operation on the first represented tensor and the final conditional vector to obtain a first conditional layer normalized output; S104, performing multi-head self-attention mechanism processing on the normalized output of the first conditional layer to obtain the output of the multi-head self-attention mechanism; S105, performing regularization processing on the output of the multi-head self-attention mechanism to obtain a regularized result of the output of the multi-head self-attention mechanism, applying a residual connection to the regularized result of the output of the multi-head self-attention mechanism and the first represented tensor, and obtaining an output tensor processed by the self-attention sub-layer and applying the residual connection; S106, performing a tensor representation on the output tensor after being processed by the self-attention sub-layer and applying the residual connection to obtain a second represented tensor; S107, performing a conditional layer normalization operation on the second represented tensor and the final conditional vector to obtain a second conditional layer normalized output; S108, using a feedforward network to process the normalized output of the second conditional layer to obtain a feedforward network output; S109, performing regularization processing on the feedforward network output to obtain a regularized result of the feedforward network output, applying a residual connection to the regularized result of the feedforward network output and the second represented tensor, to obtain an output tensor processed by the feedforward network sublayer and applying the residual connection; S110, taking the output tensor processed by the feedforward network sublayer and applying the residual connection as input, and iteratively repeating steps S102 to S109 to obtain a final output tensor; S111, performing final conditional normalization operation and average pooling operation on the final output tensor in sequence to obtain a mixed potential representation; S112, performing latent factor decomposition processing on the mixed latent representation to obtain a decomposition result; S113, extracting shared features from the decomposition results using a multi-layer perceptron to obtain extracted features; S114. Project the extracted features using a latent factor projector to obtain an output factor list.

[0027] The input external condition information uses a multi-layer perceptron to encode the context information and generate the final condition vector. The corresponding process has the following relationship: ; in, represents the coded demographic and treatment related information, represents a small multilayer perceptron used to encode contextual information, Represents a splicing operation, represents characteristics relevant to treatment, represents characteristics related to demographics, represents the embedding vector of the state index, represents an embedding layer, The index representing the state, represents the final conditional vector; In the step of inputting the local feature vector after adding the position encoding into the Transformer encoder for tensor representation and obtaining the first represented tensor, the corresponding process has the following relationship: ; in, represents the input tensor before entering the self-attention sublayer, Represents the local feature vector after adding position encoding as input ; In the step of performing conditional layer normalization operation on the first represented tensor and the final conditional vector to obtain the output of the first conditional layer normalization, the corresponding process has the following relationship: ; in, represents the normalized output of the first conditional layer, Indicates that the conditional layer has been normalized; In the step of processing the normalized output of the first conditional layer by the multi-head self-attention mechanism to obtain the output of the multi-head self-attention mechanism, the corresponding process has the following relationship: ; in, represents the output of the multi-head self-attention mechanism, represents a multi-head self-attention operation, represents the query vector, represents the key vector, represents a value vector; In the step of regularizing the output of the multi-head self-attention mechanism to obtain the regularized result of the output of the multi-head self-attention mechanism, applying residual connection to the regularized result of the output of the multi-head self-attention mechanism and the first represented tensor, and obtaining the output tensor after being processed by the self-attention sub-layer and applying the residual connection, the relationship between the corresponding processes is as follows: ; in, represents the output tensor after being processed by the self-attention sublayer and applying the residual connection, Represents the output regularization of the multi-head self-attention mechanism.

[0028] The output tensor after the self-attention sub-layer processing and the residual connection is represented as a tensor to obtain the second tensor. The corresponding process has the following relationship: ; in, Represents the input tensor before entering the feed-forward sublayer; In the step of performing conditional layer normalization operation on the second represented tensor and the final conditional vector to obtain the normalized output of the second conditional layer, the corresponding process has the following relationship: ; in, represents the normalized output of the second conditional layer; In the step of using the feedforward network to process the normalized output of the second conditional layer and obtain the output of the feedforward network, the corresponding process has the following relationship: ; in, represents the output of the feedforward network, Indicates that it has been processed by a feedforward network; In the steps of regularizing the feedforward network output to obtain the regularized result of the feedforward network output, applying residual connection to the regularized result of the feedforward network output and the second represented tensor, and obtaining the output tensor after being processed by the feedforward network sublayer and applying the residual connection, the corresponding process has the following relationship: ; in, represents the output tensor after being processed by the feedforward network sublayer and applying the residual connection, Represents regularization of the feedforward network output; In the step of performing the final conditional normalization operation and average pooling operation on the final output tensor in sequence to obtain the mixed potential representation, the corresponding process has the following relationship: ; in, Indicates passing The output of the layer Transformer encoder, represents the final normalization layer, represents the output of the last layer of Transformer encoder, represents the mixed latent representation, represents the calculation of the mean, Indicates that the mean is calculated on the second dimension of the tensor; In the step of performing latent factor decomposition on the mixed latent representation to obtain the decomposition result, the corresponding process has the following relationship: ; ; in, represents the dimension of the conditional vector, represents the input of the shared multilayer perceptron used to generate latent factors; In the step of extracting shared features from the decomposition results using a multi-layer perceptron to obtain the extracted features, the corresponding process has the following relationship: ; in, Represented by shared perceptron Extracted features; In the step of projecting the extracted features using the latent factor projector to obtain the output factor sequence, the corresponding process has the following relationship: ; in, Indicates potential factors, Indicates Independent linear projection heads, represents the output factor sequence, represents the first latent factor, express potential factor.

[0029] Step 4: Based on the signal reconstruction module, the decoupled latent factors are decoded and reconstructed using a multi-layer perceptron decoder to obtain a reconstructed PPG signal.

[0030] See also Figure 7 In step 4, based on the signal reconstruction module, the decoupled latent factors are decoded and reconstructed using a multi-layer perceptron decoder to obtain a reconstructed PPG signal, which specifically includes the following steps: The potential factors of the output factor sequence are concatenated to obtain the concatenated vector. The corresponding relationship in the process is as follows: ; in, A vector representing the concatenation of all latent factors; The concatenated vectors are reconstructed using a multi-layer perceptron to obtain the reconstructed PPG signal. The corresponding relationship in the process is as follows: ; in, represents the reconstructed PPG signal, Represents a multi-layer perceptron.

[0031] It should be noted that In this step, reconstruction is performed as a decoder.

[0032] Step 5: Based on the model training module, a decoupling loss is constructed according to the decoupled latent factors, and a reconstruction loss is constructed according to the original PPG signal and the reconstructed PPG signal; The joint decoupling loss and the reconstruction loss are weighted and combined, and input into the Adam optimizer to update the parameters of the reconstruction model to obtain the updated reconstruction model; The final reconstruction result is obtained based on the updated reconstruction model.

[0033] See also Figure 8 In step 5, the decoupling loss is constructed according to the decoupled potential factors, which specifically includes the following steps: Based on the output factor sequence, the concatenated vector of all potential factors is obtained, and the concatenated vector of all potential factors is centrally factored to obtain all the centralized potential factors. The corresponding relationship in the process is as follows: ; in, represents the total dimension of all latent factors, represents the dimension of each latent factor, represents all potential factors after centering, In the batch dimension Calculate the mean of the latent factors on The covariance matrix of all the latent factors after centralization is calculated to obtain the covariance matrix of the latent factors. The relationship between the corresponding process is as follows: ; in, represents the covariance matrix of the latent factors, Represents all potential factors after centering Transpose , Indicates the batch size; The covariance matrix of the latent factors is squared for off-diagonal elements to obtain the decoupling loss. The corresponding process has the following relationship: ; in, represents the decoupling loss, Represents the covariance matrix Square the off-diagonal elements of .

[0034] The reconstruction loss is constructed based on the original PPG signal and the reconstructed PPG signal. The relationship between the corresponding process is as follows: ; in, represents the reconstruction loss, Indicates the number of output channels, Indicates the signal length, means solving for the square of the Frobenius norm, represents the Frobenius norm; In the step of weighted combination of joint decoupling loss and reconstruction loss, the total loss is related as follows: ; in, represents the total loss, represents the weight of the reconstruction loss, Represents the weight of the decoupling loss.

[0035] The joint decoupling loss and the reconstruction loss are weighted and combined, and input into the Adam optimizer to update the parameters of the reconstruction model to obtain the updated reconstruction model. The corresponding process has the following relationship: ; in, Indicates The reconstruction model parameters of the step, represents the learning rate, Represents the reconstruction model parameters exist Gradient computation for a time step.

[0036] Furthermore, the update rule of the Adam optimizer is as follows: ; in, Indicates The first-order moment estimate of the gradient of the step, Indicates The second-order moment estimate of the gradient of the step, Represents the total loss function for the reconstruction model parameters The gradient of and Respectively represent the two decay rate parameters of the Adam optimizer, represents the numerical stability term, represents the bias-corrected first-order moment estimate, represents the bias-corrected second-order moment estimate, Expressing the The bias correction term of the first-order moment estimate of the gradient of the step, Expressing the The bias correction term for the estimate of the second moment of the gradient at step .

[0037] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0038] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0039] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A PPG signal denoising and reconstruction method based on a causal decoupling network, characterized in that: The method comprises the following steps: Step 1: construct a causal decoupling network module based on the Transformer architecture, and construct a signal reconstruction module based on the signal reconstructor. The signal preprocessing module, the causal decoupling network module, the signal reconstruction module and the model training module constitute the reconstruction model; The causal decoupling network module includes a Transformer encoder and a latent factor projector, the Transformer encoder includes a multi-layer perceptron and a feedforward network, and the signal reconstruction module includes a multi-layer perceptron decoder; Step 2: Input the original PPG signal into the signal preprocessing module for preprocessing to obtain the preprocessed feature vector; Step 3: Input the preprocessed feature vector into the causal decoupling network module, use the Transformer encoder and latent factor projector to decouple the signal features, and obtain the decoupled latent factors; Step 4: Based on the signal reconstruction module, the decoupled latent factors are decoded and reconstructed using a multi-layer perceptron decoder to obtain a reconstructed PPG signal; Step 5: Based on the model training module, a decoupling loss is constructed according to the decoupled latent factors, and a reconstruction loss is constructed according to the original PPG signal and the reconstructed PPG signal; The joint decoupling loss and the reconstruction loss are weighted and combined, and input into the Adam optimizer to update the parameters of the reconstruction model to obtain the updated reconstruction model; The final reconstruction result is obtained based on the updated reconstruction model.

2. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 1, characterized in that: In step 2, the original PPG signal is input into a signal preprocessing module for preprocessing to obtain a preprocessed feature vector, which specifically includes the following steps: The original PPG signal is input into the signal preprocessing module, and the original PPG signal is divided and processed using one-dimensional convolution to obtain the divided local feature vector; Add the position code to obtain the added position code; The divided local feature vector and the added position code are added to obtain the local feature vector after adding the position code.

3. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 2, characterized in that: The original PPG signal is input into the signal preprocessing module, and the original PPG signal is divided and processed using one-dimensional convolution to obtain the local feature vector after division. The relationship between the corresponding process is as follows: ； in, represents the local feature vector after partitioning, It means that after one-dimensional convolution operation, represents the original PPG signal, represents the convolution kernel size, represents the step length; In the step of adding the position code and obtaining the added position code, the relationship between the corresponding process is as follows: ； in, Indicates at location and even-numbered feature dimension index The positional encoding value at represents the sine function, represents the exponential part, Indicates at location and odd feature dimensions The positional encoding value at represents the cosine function, represents the feature dimension index, represents the position encoding function, Represents the position index, Represents the model dimension; In the step of adding the divided local feature vector and the added position code to obtain the local feature vector after adding the position code, the corresponding process has the following relationship: ； in, Represents the local feature vector after adding position encoding.

4. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 3 is characterized in that: In step 3, the preprocessed feature vector is input into the causal decoupling network module to perform signal feature decoupling using the Transformer encoder and the latent factor projector to obtain the decoupled latent factor, which specifically includes the following steps: S101, input external condition information and use a multi-layer perceptron to encode context information to generate a final condition vector; S102, inputting the local feature vector after adding the position encoding into the Transformer encoder for tensor representation to obtain a first represented tensor; S103, performing a conditional layer normalization operation on the first represented tensor and the final conditional vector to obtain a first conditional layer normalized output; S104, performing multi-head self-attention mechanism processing on the normalized output of the first conditional layer to obtain the output of the multi-head self-attention mechanism; S105, performing regularization processing on the output of the multi-head self-attention mechanism to obtain a regularized result of the output of the multi-head self-attention mechanism, applying a residual connection to the regularized result of the output of the multi-head self-attention mechanism and the first represented tensor, and obtaining an output tensor processed by the self-attention sub-layer and applying the residual connection; S106, performing a tensor representation on the output tensor after being processed by the self-attention sub-layer and applying the residual connection to obtain a second represented tensor; S107, performing a conditional layer normalization operation on the second represented tensor and the final conditional vector to obtain a second conditional layer normalized output; S108, using a feedforward network to process the normalized output of the second conditional layer to obtain a feedforward network output; S109, performing regularization processing on the feedforward network output to obtain a regularized result of the feedforward network output, applying a residual connection to the regularized result of the feedforward network output and the second represented tensor, to obtain an output tensor processed by the feedforward network sublayer and applying the residual connection; S110, taking the output tensor processed by the feedforward network sublayer and applying the residual connection as input, and iteratively repeating steps S102 to S109 to obtain a final output tensor; S111, performing final conditional normalization operation and average pooling operation on the final output tensor in sequence to obtain a mixed potential representation; S112, performing latent factor decomposition processing on the mixed latent representation to obtain a decomposition result; S113, extracting shared features from the decomposition results using a multi-layer perceptron to obtain extracted features; S114. Project the extracted features using a latent factor projector to obtain an output factor list.

5. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 4, characterized in that: The input external condition information uses a multi-layer perceptron to encode the context information and generate the final condition vector. The corresponding process has the following relationship: ； in, represents the coded demographic and treatment related information, represents a small multilayer perceptron used to encode contextual information, Represents a splicing operation, Indicates characteristics relevant to treatment, represents characteristics related to demographics, represents the embedding vector of the state index, represents an embedding layer, The index representing the state, represents the final conditional vector; In the step of inputting the local feature vector after adding the position encoding into the Transformer encoder for tensor representation and obtaining the first represented tensor, the corresponding process has the following relationship: ； in, represents the input tensor before entering the self-attention sublayer, Represents the local feature vector after adding position encoding as input ; In the step of performing conditional layer normalization operation on the first represented tensor and the final conditional vector to obtain the normalized output of the first conditional layer, the corresponding process has the following relationship: ； in, represents the normalized output of the first conditional layer, Indicates that the conditional layer has been normalized; In the step of processing the normalized output of the first conditional layer by the multi-head self-attention mechanism to obtain the output of the multi-head self-attention mechanism, the corresponding process has the following relationship: ； in, represents the output of the multi-head self-attention mechanism, represents a multi-head self-attention operation, represents the query vector, represents the key vector, represents a value vector; In the step of regularizing the output of the multi-head self-attention mechanism to obtain the regularized result of the output of the multi-head self-attention mechanism, applying residual connection to the regularized result of the output of the multi-head self-attention mechanism and the first represented tensor, and obtaining the output tensor after being processed by the self-attention sub-layer and applying the residual connection, the relationship between the corresponding processes is as follows: ； in, represents the output tensor after being processed by the self-attention sublayer and applying the residual connection, Represents the output regularization of the multi-head self-attention mechanism.

6. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 5, characterized in that: The output tensor after the self-attention sub-layer processing and the residual connection is represented as a tensor to obtain the second tensor. The corresponding process has the following relationship: ； in, Represents the input tensor before entering the feed-forward sublayer; In the step of performing conditional layer normalization operation on the second represented tensor and the final conditional vector to obtain the normalized output of the second conditional layer, the corresponding process has the following relationship: ； in, represents the normalized output of the second conditional layer; In the step of using the feedforward network to process the normalized output of the second conditional layer and obtain the output of the feedforward network, the corresponding process has the following relationship: ； in, represents the output of the feedforward network, Indicates that it has been processed by a feedforward network; In the steps of regularizing the feedforward network output to obtain the regularized result of the feedforward network output, applying residual connection to the regularized result of the feedforward network output and the second represented tensor, and obtaining the output tensor after being processed by the feedforward network sublayer and applying the residual connection, the corresponding process has the following relationship: ； in, represents the output tensor after being processed by the feedforward network sublayer and applying the residual connection, Represents regularization of the feedforward network output; In the step of performing the final conditional normalization operation and average pooling operation on the final output tensor in sequence to obtain the mixed potential representation, the corresponding process has the following relationship: ； in, Indicates passing The output of the layer Transformer encoder, represents the final normalization layer, represents the output of the last layer of Transformer encoder, represents the mixed latent representation, Indicates the calculation of the mean, Indicates that the mean is calculated on the second dimension of the tensor; In the step of performing latent factor decomposition on the mixed latent representation to obtain the decomposition result, the corresponding process has the following relationship: ； ； in, represents the dimension of the conditional vector, represents the input of the shared multilayer perceptron used to generate latent factors; In the step of extracting shared features from the decomposition results using a multi-layer perceptron to obtain the extracted features, the corresponding process has the following relationship: ； in, Represented by shared perceptron Extracted features; In the step of projecting the extracted features using the latent factor projector to obtain the output factor sequence, the corresponding process has the following relationship: ； in, Indicates potential factors, Indicates Independent linear projection heads, represents the output factor sequence, represents the first latent factor, express potential factor.

7. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 6, characterized in that: In step 4, based on the signal reconstruction module, the decoupled potential factors are decoded and reconstructed using a multi-layer perceptron decoder to obtain a reconstructed PPG signal, which specifically includes the following steps: The potential factors of the output factor sequence are concatenated to obtain the concatenated vector. The corresponding relationship in the process is as follows: ； in, A vector representing the concatenation of all latent factors; The concatenated vectors are reconstructed using a multi-layer perceptron to obtain the reconstructed PPG signal. The corresponding relationship in the process is as follows: ； in, represents the reconstructed PPG signal, Represents a multilayer perceptron.

8. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 7, characterized in that: In step 5, the decoupling loss is constructed according to the decoupled potential factors, which specifically includes the following steps: Based on the output factor sequence, the concatenated vector of all potential factors is obtained, and the concatenated vector of all potential factors is centrally factored to obtain all the centralized potential factors. The corresponding relationship in the process is as follows: ； in, represents the total dimension of all latent factors, represents the dimension of each latent factor, represents all potential factors after centering, In the batch dimension Calculate the mean of the latent factors on ; The covariance matrix of all the latent factors after centralization is calculated to obtain the covariance matrix of the latent factors. The relationship between the corresponding process is as follows: ； in, represents the covariance matrix of the latent factors, Represents all potential factors after centering Transpose , Indicates the batch size; The covariance matrix of the latent factors is squared for off-diagonal elements to obtain the decoupling loss. The corresponding process has the following relationship: ； in, represents the decoupling loss, Represents the covariance matrix Square the off-diagonal elements of .

9. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 8, characterized in that: The reconstruction loss is constructed based on the original PPG signal and the reconstructed PPG signal. The relationship between the corresponding process is as follows: ； in, represents the reconstruction loss, Indicates the number of output channels, Indicates the signal length, represents solving the square of the Frobenius norm, represents the Frobenius norm; In the step of weighted combination of joint decoupling loss and reconstruction loss, the total loss is related as follows: ； in, represents the total loss, represents the weight of the reconstruction loss, Represents the weight of the decoupling loss.

10. The PPG signal denoising and reconstruction method based on a causal decoupling network according to claim 9, characterized in that: The joint decoupling loss and reconstruction loss are weighted and combined, and input into the Adam optimizer to update the parameters of the reconstruction model. The corresponding process has the following relationship: ； in, Indicates The reconstruction model parameters of the step, represents the learning rate, Represents the reconstruction model parameters exist Gradient computation for a time step.

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