Sleep spindle wave intelligent identification method, system, equipment, medium and product

Abstract

The invention discloses a sleep spindle wave intelligent identification method, system and device, a medium and a product, and relates to the field of electroencephalogram characteristic wave identification, and the method comprises the following steps: obtaining a sleep electroencephalogram signal time sequence; performing band-pass filtering processing on the sleep electroencephalogram signal time sequence to obtain a filtered electroencephalogram signal time sequence; respectively segmenting the sleep electroencephalogram signal time sequence and the filtering electroencephalogram signal time sequence into overlapped window signals with different lengths to obtain a time slice data set; according to the time slice data set, a spindle wave recognition model is adopted, and whether spindle waves exist in the sleep electroencephalogram signal time sequence or not and the starting time and the duration time of the spindle waves are determined; wherein the spindle wave recognition model is constructed based on a convolutional neural network, and is obtained by adopting a training sample set for training in advance. The spindle waves with different durations and amplitudes can be detected, and the recognition accuracy of the sleep spindle waves is improved.

Description

Technical Field

[0001] The present application relates to the field of EEG characteristic wave recognition, and in particular to a multi-window filtering coupled sleep spindle intelligent recognition method, system, device, medium and product. Background Art

[0002] Sleep spindles are explosive sequences of 11-16 Hz sinusoidal cycles that can be identified in sleep EEG. The American Academy of Sleep Medicine defines sleep spindles as "clearly discernible sinusoidal waves with a frequency of 11-16 Hz (usually 12-14 Hz) and a duration of at least 0.5 s, with the highest amplitude achieved in the central lead." Sleep spindles play a vital role in sleep structure and are associated with various cognitive functions, such as memory consolidation and learning ability. In addition, abnormal spindle characteristics are associated with neurological and psychiatric disorders, including schizophrenia, depression, and neurodegenerative diseases, so accurate detection of spindles is of clinical significance.

[0003] The development of automated sleep spindle detection technology has become an area of ​​growing interest due to the need for objective, valid, and reproducible assessments of sleep-related neural activity. Advances in machine learning, signal processing, and artificial intelligence have led to the emergence of new algorithms for high-precision identification of sleep spindles. Related studies have adopted various architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models that combine spatial and temporal features to improve detection accuracy. These models have shown effectiveness in distinguishing spindle events from non-spindle electroencephalogram (EEG) activities. However, these models usually use a fixed-length time window to extract EEG signals. There is uncertainty and individual differences in the duration of spindles, and fixed time windows cannot adapt to such individual differences. Too long or too short time windows may lead to inaccurate positioning or incomplete spindle segmentation, resulting in missed detection or false detection. Summary of the invention

[0004] The purpose of this application is to provide a method, system, device, medium and product for intelligently identifying sleep spindles, which can improve the recognition accuracy of sleep spindles.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for intelligently identifying sleep spindles, comprising:

[0007] Obtain sleep EEG signal time series;

[0008] Performing bandpass filtering on the sleep EEG signal time series to obtain a filtered EEG signal time series;

[0009] Respectively dividing the sleep EEG signal time series and the filtered EEG signal time series into overlapping window signals of different lengths to obtain a time segment data set;

[0010] According to the time segment data set, using a spindle wave recognition model, determining whether there are spindle waves in the sleep EEG signal time series and the start time and duration of the spindle waves;

[0011] The spindle recognition model is constructed based on a convolutional neural network and is pre-trained using a training sample set; the training sample set includes multiple time segment sample sets and spindle labels for each time segment sample set; the spindle labels include whether there are spindles, the start time and duration of the spindles.

[0012] In a second aspect, the present application provides a sleep spindle intelligent recognition system, comprising:

[0013] A signal acquisition module, used to acquire a time series of sleep EEG signals;

[0014] A signal filtering module, used for performing bandpass filtering on the sleep EEG signal time series to obtain a filtered EEG signal time series;

[0015] A signal segmentation module, used to segment the sleep EEG signal time series and the filtered EEG signal time series into overlapping window signals of different lengths to obtain a time segment data set;

[0016] A signal recognition module, for determining whether there are spindles in the sleep EEG signal time series and the start time and duration of the spindles by using a spindle recognition model according to the time segment data set;

[0017] The spindle recognition model is constructed based on a convolutional neural network and is pre-trained using a training sample set; the training sample set includes multiple time segment sample sets and spindle labels for each time segment sample set; the spindle labels include whether there are spindles, the start time and duration of the spindles.

[0018] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned sleep spindle intelligent identification method.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned sleep spindle intelligent identification method.

[0020] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned sleep spindle intelligent identification method when executed by a processor.

[0021] According to the specific embodiments provided in this application, this application has the following technical effects:

[0022] The present application provides a method, system, device, medium and product for intelligent identification of sleep spindles. By performing bandpass filtering on the sleep EEG signal time series, the interference of muscle movement, eye movement and other non-neural activities is reduced, and the accuracy of spindle identification is improved. By dividing the sleep EEG signal time series and the filtered EEG signal time series into overlapping window signals of different lengths, the spindle identification model can capture short-term and long-term features. The spindle identification model can detect spindles with different durations and amplitudes, thereby improving the identification accuracy of sleep spindles. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 This is a diagram of an application environment of a sleep spindle intelligent identification method in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of a process for intelligently identifying sleep spindles provided in one embodiment of the present application;

[0026] Figure 3 A block diagram of a sleep spindle intelligent identification method provided in an embodiment of the present application;

[0027] Figure 4 This is a schematic diagram of the processing process of the spindle wave recognition model in one embodiment of the present application;

[0028] Figure 5 A schematic diagram of the functional modules of a sleep spindle intelligent recognition system provided in one embodiment of the present application;

[0029] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The identification methods of spindle waves can be divided into the following categories:

[0032] (1) Template matching method: Based on the characteristics of spindles, the specific features of spindles are extracted from the EEG signal through signal technology, including time threshold and frequency threshold analysis.

[0033] (2) Traditional machine learning methods: extract time threshold, frequency domain or nonlinear features from EEG signals, and learn spindle features from a large amount of labeled data by constructing feature vectors. Common algorithms include support vector machines, random forests, K-nearest neighbors, etc. Compared with template matching, it can better adapt to individual differences between signals, but the learned features are single and cannot consider the interaction between multiple windows.

[0034] (3) Methods based on deep learning: There is no need to explicitly extract features, and spindle features can be learned directly from the original signal. This includes convolutional neural networks (extracting spatial features) and recurrent neural networks (extracting time series features), which can avoid feature engineering.

[0035] The purpose of this application is to study the integration of multiple EEG datasets, use advanced filtering techniques, and explore the effectiveness of multi-window architectures, in an effort to develop a more generalizable, robust, and accurate automatic spindle identification method.

[0036] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0037] The sleep spindle intelligent recognition method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send a sleep EEG signal time series to be identified to the server 104. After the server 104 receives the sleep EEG signal time series to be identified, it performs bandpass filtering, time series segmentation and identification in sequence to determine whether there are spindles in the sleep EEG signal time series and the start time and duration of the spindles. The server 104 can feedback the identification result of the spindles to the terminal 102. In addition, in some embodiments, the sleep spindle intelligent identification method can also be implemented by the server 104 or the terminal 102 alone.

[0038] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0039] In an exemplary embodiment, Figure 2 As shown, a sleep spindle intelligent identification method is provided, which is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 204.

[0040] Step 201, obtaining a sleep EEG signal time series. Specifically, a polysomnography instrument is used to collect the sleep EEG signal time series, and the monitoring, collection and storage are performed according to the standards of the American Academy of Sleep Medicine.

[0041] Step 202: bandpass filter the sleep EEG signal time series to obtain a filtered EEG signal time series.

[0042] In an exemplary embodiment, the sleep EEG signal time series is first digitized, and the baseline and noise waves are removed to obtain a preprocessed EEG signal time series, and then a Butterworth filter is used to filter the preprocessed EEG signal time series to obtain a filtered EEG signal time series.

[0043] Among them, the extremely low-frequency baseline, high-frequency and low-frequency noise waves in the sleep EEG signal time series are removed. The sampling frequency is 256HZ, and the signal preprocessing is completed. The Butterworth filter is used to perform frequency calibration at 11HZ~16HZ to verify the validity of the data in the EEG signal time series. The calculation formula is:

[0044]

[0045] Where x[t] is the time series of the preprocessed EEG signal, y[t] is the impulse response of the bandpass filter, N is the order of the Butterworth filter, and b k is the filter coefficient calculated based on the order of the Butterworth filter, a m is the filter coefficient calculated based on the cutoff frequency.

[0046] Step 203 , respectively segmenting the sleep EEG signal time series and the filtered EEG signal time series into overlapping window signals of different lengths to obtain a time segment data set.

[0047] In an exemplary embodiment, the time segment data set includes a first data set and a second data set. The first data set includes a plurality of signal segments of different time lengths of the filtered EEG signal time series. The second data set includes a plurality of signal segments of different time lengths of the sleep EEG signal time series.

[0048] Specifically, spindle waves generally exist in EEG signals for no more than 2 seconds, so the present application divides the entire sleep EEG signal time series and the entire filtered EEG signal time series into segments of different time lengths of 2 seconds, 4 seconds, and 6 seconds, respectively.

[0049] Step 204: Based on the time segment data set, a spindle wave recognition model is used to determine whether there are spindle waves in the sleep EEG signal time series and the start time and duration of the spindle waves. If there are no spindle waves, the start time and duration are both 0.

[0050] The spindle wave recognition model is constructed based on a convolutional neural network and is pre-trained using a training sample set. The training sample set includes multiple time segment sample sets and spindle wave labels of each time segment sample set. The spindle wave label includes whether there is a spindle wave, the start time of the spindle wave, and the duration of the spindle wave.

[0051] In an exemplary embodiment, Figure 3As shown, the spindle wave recognition model includes a first convolutional neural network, a second convolutional neural network and a fully connected neural network. The time segment sample set is divided into a first sample set and a second sample set. The first sample set includes a plurality of signal segments of different time lengths of the filtered EEG signal time series, and the second sample set includes a plurality of signal segments of different time lengths of the sleep EEG signal time series. A first convolutional neural network is constructed based on the first sample set, and a second convolutional neural network is constructed based on the second sample set. The features extracted by the first convolutional neural network and the second convolutional neural network are aggregated through a fully connected neural network, and further iterated and output the final recognition result.

[0052] This application extracts multiple time segments containing spindles and multiple time segments not containing spindles to form a training sample set. The training sample set is divided into a training set, a validation set, and a test set. The training set is used to train the spindle recognition model to obtain the prediction value of whether there are spindles, and the prediction value is compared with the spindle labels manually annotated by experts, and the loss function is calculated. The optimal spindle recognition model parameters are obtained through iterative training.

[0053] The loss function of the spindle wave recognition model training is the focus loss function to deal with the problem of class imbalance in target detection. The formula of the focus loss function is:

[0054] FL(p t )=-α t (1-p t ) γ log(p t );

[0055] Among them, FL(p t ) is the loss function value, p t is the recognition result output by the spindle wave recognition model, α t and γ are factors, which is a dynamically scaled cross entropy loss that gradually decreases to zero as the confidence of the spindle recognition model in the correct category increases. The focus loss function adds a regulation term to the cross entropy loss in order to focus on samples that are difficult to classify during training.

[0056] The spindle wave recognition model was optimized using the Adam optimizer, which is particularly suitable for processing non-stationary data sets (such as EEG signals). The learning rate was initially set to 0.001 and gradually decreased as the spindle wave recognition model converged, down to a minimum of 1×10 -8 .

[0057] Step 204 specifically includes the following steps 301 to 303 .

[0058] Step 301: Use the first convolutional neural network to extract features from each signal segment in the first data set to obtain a first feature set.

[0059] Step 302: Use the second convolutional neural network to extract features from each signal segment in the second data set to obtain a second feature set.

[0060] Step 303: After concatenating the first feature set with the second feature set, the fully connected neural network is used to determine whether spindles exist in the sleep EEG signal time series and the start time and duration of the spindles.

[0061] In this application, each signal segment is processed by CNN, and the CNN architecture uses a 5×5 convolution kernel with a step size of 2. The spindle recognition model also includes a padding operation to ensure that the output dimension matches the input dimension, so as to achieve consistent feature extraction in each layer. After the signal segment is convolved in the convolutional neural network, the ReLU activation function is applied to introduce nonlinearity, enhancing the modeling ability of the spindle recognition model for complex patterns. ReLU continues to be the main activation function of these layers to maintain nonlinearity. Subsequently, maximum pooling is performed to reduce the spatial dimension and retain only the most significant features, further simplifying the calculation. After convolution and maximum pooling, the output results are flattened and passed to a fully connected neural network with 4 hidden layers. The first input layer of the fully connected neural network has 256 nodes to adapt to the complexity and diversity of the spindle pattern. The subsequent hidden layers gradually reduce the number of nodes (128 nodes, 64 nodes, 32 nodes), and gradually refine the feature representation. The final output layer contains 2 nodes, corresponding to the start time and duration of the spindle wave respectively. The 32 nodes are connected to the 2 nodes by multiplying each node by the weight, and further through the activation function operation, an output value is obtained, which is the start time. The other output is the same operation, but with a different weight, which is the duration.

[0062] like Figure 4 As shown in the figure, assuming that the duration of the input sleep EEG signal time series is 8 hours and the frequency is 64 Hz, there are a total of 28800 seconds × 64 data points. Then, a 2-second signal segment, a 4-second signal segment, and a 6-second signal segment are selected respectively. After the signal segment passes through a convolution layer with a convolution kernel of 5×5, a step size of 2, and a padding size of 1, and then passes through a convolution layer with a convolution kernel of 3×3, a step size of 1, and a padding size of 0, a vector with 256 nodes is obtained by splicing and input into the fully connected neural network. After passing through the hidden layer of the fully connected neural network, the number of nodes is reduced to 128, 64, and 32 respectively, and finally the start time and duration are output through 2 nodes.

[0063] This application uses bandpass filtering rules to screen candidate bands. Filtering can improve the stability of signals in noisy EEG environments, reduce interference from muscle movements, eye movements, and other non-neural activities, and improve the accuracy of spindle wave recognition. Using multiple time windows to segment the sleep EEG signal time series improves the accuracy of spindle wave capture. The duration of spindles is 0.5 seconds to 3 seconds. It is very important to set a reasonable size for segmenting sleep data segments for accurate identification of spindles. Segmentation that is too short or too long may cause loss of spindle identification. This application uses multiple time windows to segment sleep EEG data. The spindle identification model concatenates features from multiple windows before passing them to the fully connected layer. After the convolution branch processes the EEG signal, the extracted features are flattened and concatenated into a single feature vector. This feature vector integrates information at different time scales, allowing the spindle identification model to fuse short-term and long-term features. By fusing information from different windows, the spindle identification model can detect spindles with different durations and amplitudes, ensuring that spindles that may be missed by the single-window model are fully captured, better adapting to individual differences in spindle duration, and improving the accuracy of spindle identification.

[0064] The sleep spindle intelligent recognition method provided by this application has a recall rate of 0.92, an accuracy rate of 0.88, and an F1 score of 0.90 in its own data set. In the public data set of sleep EEG data, it was verified in the DREAM and MASS data sets. In the DREAM data set, the recall rate of this application reached 0.91, the accuracy rate reached 0.89, and the F1 score reached 0.90. In the MASS data set, the recall rate of this application reached 0.92, the accuracy rate reached 0.88, and the F1 score reached 0.90.

[0065] Based on the same inventive concept, the embodiment of the present application also provides a sleep spindle wave intelligent identification system for implementing the sleep spindle wave intelligent identification method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more sleep spindle wave intelligent identification system embodiments provided below can refer to the limitations of the sleep spindle wave intelligent identification method above, and will not be repeated here.

[0066] In an exemplary embodiment, Figure 5 As shown, a sleep spindle intelligent recognition system is provided, including: a signal acquisition module 501, a signal filtering module 502, a signal segmentation module 503 and a signal recognition module 504.

[0067] The signal acquisition module 501 is used to acquire the sleep EEG signal time series.

[0068] The signal filtering module 502 is used to perform bandpass filtering on the sleep EEG signal time series to obtain a filtered EEG signal time series.

[0069] The signal segmentation module 503 is used to segment the sleep EEG signal time series and the filtered EEG signal time series into overlapping window signals of different lengths to obtain a time segment data set.

[0070] The signal recognition module 504 is used to determine whether there are spindles in the sleep EEG signal time series and the start time and duration of the spindles by using a spindle recognition model according to the time segment data set.

[0071] The spindle wave recognition model is constructed based on a convolutional neural network and is pre-trained using a training sample set. The training sample set includes multiple time segment sample sets and spindle wave labels of each time segment sample set. The spindle wave label includes whether there is a spindle wave, the start time of the spindle wave, and the duration of the spindle wave.

[0072] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store sleep EEG signal time series, and the input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for intelligently identifying sleep spindles is implemented.

[0073] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0074] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0075] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0076] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0077] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0078] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0079] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0080] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A sleep spindle intelligent identification method, characterized in that: The sleep spindle intelligent identification method comprises: Obtain sleep EEG signal time series; Performing bandpass filtering on the sleep EEG signal time series to obtain a filtered EEG signal time series; Respectively dividing the sleep EEG signal time series and the filtered EEG signal time series into overlapping window signals of different lengths to obtain a time segment data set; According to the time segment data set, using a spindle wave recognition model, determining whether there are spindle waves in the sleep EEG signal time series and the start time and duration of the spindle waves; The spindle recognition model is constructed based on a convolutional neural network and is pre-trained using a training sample set; the training sample set includes multiple time segment sample sets and spindle labels for each time segment sample set; the spindle labels include whether there are spindles, the start time and duration of the spindles.

2. The sleep spindle intelligent identification method according to claim 1, characterized in that: Obtain sleep EEG signal time series, including: The time series of sleep EEG signals were collected using a polysomnographic monitoring instrument.

3. The sleep spindle intelligent identification method according to claim 1, characterized in that: The sleep EEG signal time series is subjected to bandpass filtering to obtain a filtered EEG signal time series, specifically comprising: Digitizing the sleep EEG signal time series and removing the baseline and noise waves to obtain a preprocessed EEG signal time series; The preprocessed EEG signal time series is filtered using a Butterworth filter to obtain a filtered EEG signal time series.

4. The sleep spindle intelligent identification method according to claim 1, characterized in that: The time segment data set includes a first data set and a second data set; the first data set includes multiple signal segments of different time lengths of the filtered EEG signal time series; the second data set includes multiple signal segments of different time lengths of the sleep EEG signal time series.

5. The sleep spindle intelligent identification method according to claim 4, characterized in that: The spindle wave recognition model includes a first convolutional neural network, a second convolutional neural network and a fully connected neural network; According to the time segment data set, a spindle wave recognition model is used to determine whether there are spindle waves in the sleep EEG signal time series and the start time and duration of the spindle waves, specifically including: Using the first convolutional neural network to extract features from each signal segment in the first data set to obtain a first feature set; Using the second convolutional neural network to extract features from each signal segment in the second data set to obtain a second feature set; After the first feature set is concatenated with the second feature set, the fully connected neural network is used to determine whether spindles exist in the sleep EEG signal time series and the start time and duration of the spindles.

6. The sleep spindle intelligent identification method according to claim 1, characterized in that: The loss function during the training of the spindle wave recognition model is a focal loss function.

7. A sleep spindle intelligent recognition system, applied to the sleep spindle intelligent recognition method according to any one of claims 1 to 6, characterized in that: The sleep spindle intelligent recognition system comprises: A signal acquisition module, used to acquire a time series of sleep EEG signals; A signal filtering module, used for performing bandpass filtering on the sleep EEG signal time series to obtain a filtered EEG signal time series; A signal segmentation module, used to segment the sleep EEG signal time series and the filtered EEG signal time series into overlapping window signals of different lengths to obtain a time segment data set; A signal recognition module, configured to determine whether there are spindles in the sleep EEG signal time series and the start time and duration of the spindles by using a spindle recognition model according to the time segment data set; The spindle recognition model is constructed based on a convolutional neural network and is pre-trained using a training sample set; the training sample set includes multiple time segment sample sets and spindle labels for each time segment sample set; the spindle labels include whether there are spindles, the start time and duration of the spindles.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the sleep spindle intelligent identification method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the sleep spindle intelligent identification method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the sleep spindle intelligent identification method according to any one of claims 1 to 6 is implemented.

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