CN113812965B - Sleep state identification method, sleep state identification device, electronic device and storage medium - Google Patents
Sleep state identification method, sleep state identification device, electronic device and storage medium Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
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- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
Abstract
The application relates to a sleep state identification method, wherein the sleep state identification method comprises the following steps: acquiring an electroencephalogram signal of a person to be acquired; acquiring signal quality information based on the electroencephalogram signals; and inputting the electroencephalogram signals and the signal quality information into a trained first neural network model to obtain the sleep state of the acquired person. By the method and the device, the problems that the sleep state output by the neural network in real time is unstable and the error rate is high are solved, the fact that other sleep state judging equipment is not required to be additionally added is achieved, the stable output of the sleep state is ensured, and the sleep state output error rate is reduced.
Description
Technical Field
The present application relates to the technical field of electroencephalogram recognition, and in particular, to a sleep state recognition method, a sleep state recognition device, an electronic device, and a storage medium.
Background
Along with development of scientific technology, an EEG (Electroencephalogram) signal recognition technology is continuously explored, an experienced medical staff can effectively judge sleep stages of a sleeper according to characteristics of EEG signals of different sleep states and stages, and carry out targeted treatment according to different sleep conditions, besides manual judgment by the medical staff, some related researches try to input the EEG signals into a neural network by using a deep learning method, and carry out deep analysis on the EEG signals by using the neural network, so as to obtain sleep stage states similar to those of medicine.
However, in actual situations, during the process of acquiring the EEG signal by the EEG signal monitoring device, the EEG signal is easily interfered by the eye electricity, the muscle electricity, etc., and the monitored EEG signal sometimes has the conditions of unstable signal and poor signal quality, which can lead to unstable sleep state output by the neural network in real time, and has higher error rate, especially at the junction of two sleep states, the unstable output state is more serious, which has a great influence on some situations of controlling the sleep environment by using the sleep state.
Aiming at the problems of unstable sleep state and higher error rate of the neural network real-time output in the related technology, no effective solution is proposed at present.
Disclosure of Invention
The embodiment provides a sleep state identification method, a sleep state identification device, an electronic device and a storage medium, so as to solve the problems of unstable sleep state and high error rate of neural network real-time output in the related art.
In a first aspect, in this embodiment, there is provided a sleep state identification method, including: acquiring an electroencephalogram signal of a person to be acquired; acquiring signal quality information based on the electroencephalogram signals; and inputting the electroencephalogram signals and the signal quality information into a trained first neural network model to obtain the sleep state of the acquired person.
In one embodiment, before the inputting the electroencephalogram signal and the signal quality information into the trained first neural network model, further comprises: and carrying out band-pass filtering processing on the electroencephalogram signals to obtain the electroencephalogram signals in a preset frequency range.
In another embodiment, the inputting the electroencephalogram signal and the signal quality information into the trained first neural network model comprises: extracting time domain features and frequency domain features of the electroencephalogram signals, and inputting the time domain features, the frequency domain features and the signal quality information into a trained first neural network model.
In one embodiment, the extracting the time domain feature and the frequency domain feature of the electroencephalogram signal includes: inputting the electroencephalogram signals into a trained second neural network model to obtain the time domain features; and performing fast Fourier transform on the electroencephalogram signals to obtain the frequency domain characteristics.
In another embodiment, the acquiring signal quality information based on the electroencephalogram signal includes: and acquiring the signal quality information based on the amplitude information and the energy information of the electroencephalogram signals.
In one embodiment, before the inputting the electroencephalogram signal and the signal quality information into the trained first neural network model, further comprises: acquiring first training data and an initial first neural network model, wherein the first training data comprises a training electroencephalogram signal, training signal quality information corresponding to the training electroencephalogram signal and a sleep state corresponding to the training electroencephalogram signal; and training the initial neural network model based on the first training data to obtain the trained first neural network model.
In another embodiment, the obtaining the sleep state of the person to be collected further includes: based on the sleep state, the sleep music, the sleep light and the sleep temperature corresponding to the sleep state in the sleep environment adjustment database are matched and acquired, and are applied to the current environment.
In a second aspect, in this embodiment, there is provided a sleep state recognition apparatus including: an electroencephalogram signal acquisition module: the method is used for acquiring the brain electrical signals of the acquired person; the signal quality information acquisition module: the signal quality information is acquired based on the electroencephalogram signals; sleep state identification module: and the device is used for inputting the electroencephalogram signals and the signal quality information into a trained first neural network model to obtain the sleep state of the acquired person.
In a third aspect, in this embodiment, there is provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the sleep state identification method described in the first aspect when executing the computer program.
In a fourth aspect, in this embodiment, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the sleep state identification method described in the first aspect above.
Compared with the related art, the sleep state identification method provided in the embodiment obtains the brain electrical signal of the person to be acquired; acquiring signal quality information based on the electroencephalogram signals; the electroencephalogram signals and the signal quality information are input into a trained first neural network model to obtain the sleep state of the acquired person, so that the problems of unstable sleep state and high error rate of the neural network in real-time output are solved, the stable output of the sleep state is ensured without additionally adding other sleep state judging equipment, and the sleep state output error rate is reduced.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a block diagram of a hardware structure of a terminal of a sleep state recognition method in an embodiment of the present application.
Fig. 2 is a flow chart of a sleep state identification method according to an embodiment of the present application.
Fig. 3 is a schematic diagram of electroencephalogram acquisition site reference of an electroencephalogram acquisition device in a sleep state identification method according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a posterior judgment strategy of a sleep state recognition method according to an embodiment of the present application.
Fig. 5 is a block diagram of a sleep state recognition apparatus according to an embodiment of the present application.
Detailed Description
For a clearer understanding of the objects, technical solutions and advantages of the present application, the present application is described and illustrated below with reference to the accompanying drawings and examples.
Unless defined otherwise, technical or scientific terms used herein shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," "these," and the like in this application are not intended to be limiting in number, but rather are singular or plural. The terms "comprising," "including," "having," and any variations thereof, as used in the present application, are intended to cover a non-exclusive inclusion; for example, a process, method, and system, article, or apparatus that comprises a list of steps or modules (units) is not limited to the list of steps or modules (units), but may include other steps or modules (units) not listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference to "a plurality" in this application means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. Typically, the character "/" indicates that the associated object is an "or" relationship. The terms "first," "second," "third," and the like, as referred to in this application, merely distinguish similar objects and do not represent a particular ordering of objects.
The method embodiments provided in the present embodiment may be executed in a terminal, a computer, or similar computing device. For example, the sleep state recognition method is executed on a terminal, and fig. 1 is a block diagram of a hardware structure of the terminal in the sleep state recognition method according to an embodiment of the present application. As shown in fig. 1, the terminal may include one or more (only one is shown in fig. 1) processors 102 and a memory 104 for storing data, wherein the processors 102 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, or the like. The terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and is not intended to limit the structure of the terminal. For example, the terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to the sleep state identification method in the present embodiment, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, to implement the above-described method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. The network includes a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.
In this embodiment, a sleep state identification method is provided, and fig. 2 is a schematic flow chart of the sleep state identification method in an embodiment of the present application, as shown in fig. 2, where the flow chart includes the following steps:
step S201, acquiring brain electrical signals of a person to be acquired.
It can be understood that the present invention acquires the current sleep state of the person to be acquired by identifying the brain electrical signal of the person to be acquired, so that the brain electrical signal of the person to be acquired needs to be acquired first, in this embodiment, the method for acquiring the brain electrical signal of the person to be acquired may be that brain electrical signal acquisition is performed by brain-contacting electrodes through brain-contacting brain electrical signal acquisition sites may be at any position, as shown in fig. 3, and fig. 3 is a brain electrical signal acquisition site reference schematic diagram of the brain electrical signal acquisition device in the sleep state identification method in an embodiment of the present application. In this embodiment, the Fpz site is selected as a reference, and a single-channel forehead EEG electroencephalogram of the Fp1 site is collected, and it can be understood that in this embodiment, the Fp1 site is selected only for a certain electroencephalogram collection device to be suitable for obtaining an electroencephalogram of a person to be collected, and in other embodiments, other collection sites for electroencephalogram collection devices in other forms can be selected to be used for obtaining an electroencephalogram of the person to be collected, and only a collection effect with relatively better signal quality on electroencephalogram data of the person to be collected through the collection site needs to be ensured.
Step S202, acquiring signal quality information based on the brain electrical signals.
In this embodiment, after acquiring the electroencephalogram signal of the person to be acquired, signal quality information of the electroencephalogram signal is also required to be acquired, that is, signal quality of the electroencephalogram signal is determined, it is to be understood that, when acquiring the electroencephalogram signal, acquired electroencephalogram signals may include not only electroencephalogram signals, but also electric waves such as electrooculogram signals and muscle signals, and interference of some noises, and the existence of these non-electroencephalograms may interfere with subsequent recognition according to the sleep state, so that the recognized sleep state is frequently converted, converted more or more inaccurately, on the other hand, the signal quality level also reflects some actions of the user and information such as action amplitude and the like occurring in the process of being acquired, and these information also contributes to determination and recognition of the sleep state.
Step S203, the brain electrical signal and the signal quality information are input into the trained first neural network model, and the sleep state of the acquired person is obtained.
It can be understood that after the electroencephalogram signal and the signal quality information of the electroencephalogram signal are obtained, the sleep state of the person to be collected can be identified and judged based on the electroencephalogram signal and the signal quality information of the electroencephalogram signal, in this embodiment, the sleep state of the person to be collected is identified and judged through a trained neural network model, the trained neural network model can input the electroencephalogram signal and the signal quality information of the electroencephalogram signal, and then the sleep state of the person to be collected, which is obtained based on the electroencephalogram signal and the signal quality information, is easy to understand, and through the electroencephalogram signal and the signal quality information, the neural network model can be more sensitive to the electroencephalogram signal with higher signal quality information when judging the sleep state, so that the neural network model can judge and recognize the sleep state more accurately, and the sleep state of the person to be collected can be obtained more accurately.
Through the steps, the brain electrical signal of the person to be acquired is obtained; acquiring signal quality information based on the brain electrical signals; the brain electrical signal and the signal quality information are input into the trained first neural network model to obtain the sleep state of the acquired person, so that the problems of unstable sleep state and high error rate of the neural network output in real time are solved, the stable output of the sleep state is ensured, and the sleep state output error rate is reduced without additionally adding other sleep state judging equipment.
In one embodiment, before inputting the electroencephalogram signal and the signal quality information into the trained first neural network model, further comprises: and carrying out band-pass filtering processing on the electroencephalogram signals to obtain electroencephalogram signals in a preset frequency range.
It can be understood that some drift and power frequency noise exist in the electroencephalogram signals acquired based on the electroencephalogram collecting device, in order to reduce the recognition of unnecessary information and improve the recognition accuracy, filtering processing is needed before the electroencephalogram signals are input into the neural network model to judge the sleep state, in the embodiment, the band-pass filtering module is used for acquiring the electroencephalogram signals to be filtered, the electroencephalogram signals in the range of 2-45 Hz are acquired, most of drift and power frequency noise can be filtered, and only main signal data of the electroencephalogram signals are reserved. By carrying out band-pass filtering processing on the electroencephalogram data, the data quality of the electroencephalogram data can be improved, unnecessary interference is removed, and the accuracy of subsequent sleep state identification can be improved.
In another embodiment, inputting the electroencephalogram signal and the signal quality information into the trained first neural network model comprises: extracting time domain features and frequency domain features of the electroencephalogram signals, and inputting the time domain features, the frequency domain features and the signal quality information into the trained first neural network model.
In this embodiment, before the electroencephalogram signal and the signal quality information are input to the neural network model, the time domain features of the electroencephalogram signal need to be extracted to obtain the frequency domain features and the features, it can be understood that, in the electroencephalogram signal, the features which can most reflect the current sleep state of the person to be acquired are the time domain features and the frequency domain features, when the current sleep state of the person to be acquired is identified, if all the electroencephalogram signals are identified, the occupied operand and the occupied time are too long, based on the fact, in order to improve efficiency in identifying the sleep state and reduce calculation load, the electroencephalogram signal can be respectively extracted to obtain the time domain features and the frequency domain features, and then the time domain features, the frequency domain features and the signal quality information are simultaneously input to the trained first neural network model, and the first neural network model can be input the time domain features, the frequency domain features and the signal quality information which corresponds to the frequency domain features together, and output the corresponding sleep state, and the signal quality information is extracted to obtain the time domain features and the frequency domain features to judge the sleep state of the person to improve efficiency in identifying the sleep state of the person to be acquired.
In one embodiment, extracting the time domain features and the frequency domain features of the electroencephalogram signal includes: inputting the electroencephalogram signals into a trained second neural network model to obtain time domain features; and performing fast Fourier transform on the electroencephalogram signals to obtain frequency domain characteristics.
In this embodiment, the method for extracting the time domain features may be to input the electroencephalogram signal into the trained second neural network model, and then extract the time domain signals of the electroencephalogram signal based on the trained second neural network model, which may be understood that the trained second neural network model may be a convolutional neural network model, may also be one of convolutional neural network layers in the neural network model, may also be any feature extractor capable of extracting the time domain features of the electroencephalogram signal, only needs to ensure that the time domain features corresponding to the electroencephalogram signal can be extracted based on the electroencephalogram signal, and the method for extracting the frequency domain features may be to perform fast fourier transform on the electroencephalogram signal to extract the frequency domain features of the electroencephalogram signal.
In another embodiment, acquiring signal quality information based on an electroencephalogram signal includes: and based on the amplitude information and the energy information, judging the signal quality of the electroencephalogram signal, and obtaining the signal quality information.
It can be understood that the signal quality information of the electroencephalogram signal is obtained based on the amplitude information and the energy information of the electroencephalogram signal, in this embodiment, the amplitude information and the energy information of the electroencephalogram signal are firstly obtained, then the signal quality is classified by the amplitude information and the energy information, wherein the signal quality is classified into three classes of "good", "poor" and "poor", the signal quality information obtained based on the amplitude information and the energy information can objectively reflect the signal quality of the electroencephalogram signal, so as to be used as an effective basis for identifying the sleep state, in other embodiments, a method for judging the signal quality based on other information in the electroencephalogram signal can be used, for example, the signal quality can be judged according to the energy information of the signal or according to the amplitude information of the signal, or according to the characteristic information of other signals, for example, the signal quality can be classified into 1, 2, 3, 4 and 5 classes, then the signal class is judged according to the characteristic of the signal, and the embodiment only includes one of the classes, but not specifically limited to other modes, and the signal quality is accurately identified when the signal quality is identified by the amplitude information and the quality is judged to improve the sleep state.
In one embodiment, before inputting the electroencephalogram signal and the signal quality information into the trained first neural network model, further comprises: acquiring first training data and an initial first neural network model, wherein the first training data comprises a training electroencephalogram signal, training signal quality information corresponding to the training electroencephalogram signal and a sleep state corresponding to the training electroencephalogram signal; the initial neural network model is trained based on the first training data, resulting in a trained first neural network model.
In this embodiment, before the electroencephalogram signal and the signal quality information are input into the trained first neural network model, the initial first neural network model is required to be trained to obtain the trained first neural network model capable of obtaining the sleep state based on the electroencephalogram signal and the signal quality information, therefore, the initial first neural network model is required to be obtained first, then the first neural network model is required to be trained based on first training data, the first training data comprises training electroencephalogram signals, training signal quality information corresponding to the training electroencephalogram signals and the sleep state corresponding to the training electroencephalogram signals, based on the training data, the first neural network model is trained until the first neural network model can obtain the accurate sleep state through the electroencephalogram signals and the signal quality information, in other embodiments, the first neural network model capable of judging the sleep state through the time domain features, the frequency domain features and the signal quality information is obtained, the initial first training model is trained through the first training data, and the accuracy rate of judging the sleep state is guaranteed.
In another embodiment, the method after acquiring the sleep state further comprises: based on the sleep state, the sleep music, the sleep light and the sleep temperature corresponding to the sleep state in the sleep environment adjustment database are matched and acquired, and are applied to the current environment.
It is easy to understand that in different sleep states, the sleep quality of the user in different sleep states can be improved or the user can enter the next sleep state more quickly through different sleep music, sleep light and sleep temperature, so in the method, after the sleep state is acquired, the corresponding sleep music, sleep light and sleep temperature which are applicable to the current sleep state of the user in the sleep environment adjustment database are matched based on the sleep state and are applied to the current environment, wherein the sleep environment adjustment database should comprise various sleep music, sleep light and sleep temperature which are applicable to the current sleep state, and the corresponding sleep music, sleep light and sleep temperature are applied to the environment where the user is based on the sleep state, so that the sleep quality of the user can be improved.
In addition, in some other embodiments, the sleep state directly determined by the neural network model has a certain error rate, especially at the junction of the two sleep states, the output sleep state may have an unstable phenomenon, so as to improve the stability of the sleep state output and the accuracy of the analysis conclusion of the subsequent sleep state, in this embodiment, a final sleep state determination policy is provided, which is a posterior determination policy after the neural network model outputs the sleep state, as shown in fig. 4, and fig. 4 is a schematic diagram of a posterior determination policy of the sleep state recognition method in an embodiment of the present application. The length of each small square is the trigger step length T of the algorithm, the broken line in the graph is the final sleep state output after posterior judgment is carried out on the basis of the sleep state output by the neural network model, the broken line is S1, when the measurement is started, the S1 is in an awake state, when the sleep state is switched according to the variation of S0 in the process of acquiring and processing signals in real time, a sliding window is adopted, W small squares (5 in the graph) are intercepted, when the S0 state corresponding to K small squares (3 in the graph) in the sliding window is inconsistent with the current S1 state, S1=S0 is caused, in other embodiments, the values of K and W can be set by comprehensively considering the instability, judgment accuracy and actual allowable delay of the output of the neural network model, in the embodiment, the judgment states of the final sleep states S1 and S0 are the maximum delay D=W after the posterior judgment strategy is carried out, the maximum delay of the output is the maximum delay in the three-dimensional delay of D=W=62 in the sliding window, the output experience is greatly reduced by the method, and the output accuracy of the sleep state is greatly reduced by the method is improved, and the real-time delay experience is greatly improved.
It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment also provides a sleep state identification device, which is used for implementing the above embodiment and the preferred implementation, and is not described in detail. The terms "module," "unit," "sub-unit," and the like as used below may refer to a combination of software and/or hardware that performs a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.
Fig. 5 is a block diagram of a sleep state recognition apparatus according to an embodiment of the present application, and as shown in fig. 5, the apparatus includes: the device comprises an electroencephalogram signal acquisition module 10, a signal quality information acquisition module 20, a sleep state identification module 30, a filtering processing module, a characteristic extraction module, a first neural network model training module and a sleep environment adjustment module.
The electroencephalogram signal acquisition module 10: the method is used for acquiring the brain electrical signals of the acquired person;
signal quality information acquisition module 20: for acquiring signal quality information based on the brain electrical signals.
Sleep state identification module 30: the method is used for inputting the electroencephalogram signals and the signal quality information into the trained first neural network model to obtain the sleep state of the acquired person.
And the filtering processing module is used for: the method is used for carrying out band-pass filtering processing on the electroencephalogram signals to obtain the electroencephalogram signals within a preset frequency range.
And the feature extraction module is used for: the method is used for extracting time domain features and frequency domain features of the electroencephalogram signals and inputting the time domain features, the frequency domain features and the signal quality information into the trained first neural network model.
And the feature extraction module is used for: the first training data comprises training electroencephalogram signals, training signal quality information corresponding to the training electroencephalogram signals and sleep states corresponding to the training electroencephalogram signals; the initial neural network model is trained based on the first training data, resulting in a trained first neural network model.
The first neural network model training module: the method comprises the steps of acquiring first training data and an initial first neural network model, wherein the first training data comprises training electroencephalogram signals, training signal quality information corresponding to the training electroencephalogram signals and sleep states corresponding to the training electroencephalogram signals; the initial neural network model is trained based on the training data, resulting in a trained first neural network model.
Sleep environment adjustment module: the sleep environment adjusting database is used for matching and acquiring sleep music, sleep light and sleep temperature corresponding to the sleep state based on the sleep state and applying the sleep music, the sleep light and the sleep temperature to the current environment.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
There is also provided in this embodiment an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s1, acquiring brain electrical signals of a person to be acquired.
S2, acquiring signal quality information based on the electroencephalogram signals.
S3, inputting the electroencephalogram signals and the signal quality information into the trained first neural network model to obtain the sleep state of the person to be acquired.
It should be noted that, specific examples in this embodiment may refer to examples described in the foregoing embodiments and alternative implementations, and are not described in detail in this embodiment.
In addition, in combination with the sleep state identification method provided in the above embodiment, a storage medium may be provided in this embodiment. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements any of the sleep state identification methods of the above embodiments.
It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to be limiting. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present application, are within the scope of the present application in light of the embodiments provided herein.
It is evident that the drawings are only examples or embodiments of the present application, from which the present application can also be adapted to other similar situations by a person skilled in the art without the inventive effort. In addition, it should be appreciated that while the development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as an admission of insufficient detail.
The term "embodiment" in this application means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive. It will be clear or implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the patent. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A sleep state identification method, comprising:
acquiring an electroencephalogram signal of a person to be acquired;
acquiring signal quality information based on the electroencephalogram signals; wherein, obtaining signal quality information based on the electroencephalogram signal includes: determining signal quality information of the electroencephalogram signals according to the duty ratio of the electroencephalogram signals in all acquired signals; the signal quality information reflects the action and the action amplitude of the collected person;
inputting the electroencephalogram signals and the signal quality information into a trained first neural network model to obtain the sleep state of the acquired person;
performing posterior judgment on the sleep state of the acquired person according to a preset sliding window and a trigger step length;
taking the sleep state obtained by the posterior judgment as the final sleep state of the person to be acquired;
the posterior judgment of the sleep state of the acquired person is carried out according to a preset sliding window and a trigger step length, and the posterior judgment comprises the following steps:
dividing a first time window in which the sleep state of the person to be acquired is located into a plurality of time intervals with the length of a preset step length;
determining a first preset number of time intervals at the front end of the first time window as a second time window, and determining an awake state as a sleep state of the second time window;
determining the length of a first preset number of time intervals as the length of a sliding window;
sliding the sliding window along the first time window one by one time interval, determining the sleep state of each time interval after the second time window in the first time window according to the sleep state in the sliding window, including:
if the sleep state of at least the second preset number of time intervals in the first preset number of time intervals in which the sliding window is positioned is the preset sleep state, determining the preset sleep state as the sleep state of the first time interval behind the sliding window; the preset sleep states comprise a waking state, a shallow sleep state and a deep sleep state.
2. The sleep state identification method of claim 1 wherein the inputting of the electroencephalogram signal and the signal quality information into the trained first neural network model is preceded by:
and carrying out band-pass filtering processing on the electroencephalogram signals to obtain the electroencephalogram signals in a preset frequency range.
3. The sleep state identification method as claimed in claim 1 or 2, characterized in that, the inputting the electroencephalogram signal and the signal quality information into a trained first neural network model comprises:
extracting time domain features and frequency domain features of the electroencephalogram signals, and inputting the time domain features, the frequency domain features and the signal quality information into a trained first neural network model.
4. The sleep state identification method as set forth in claim 3, wherein the extracting the time domain features and the frequency domain features of the electroencephalogram signal includes:
inputting the electroencephalogram signals into a trained second neural network model to obtain the time domain features;
and performing fast Fourier transform on the electroencephalogram signals to obtain the frequency domain characteristics.
5. The sleep state identification method as set forth in claim 1, wherein the acquiring signal quality information based on the electroencephalogram signal includes:
and acquiring the signal quality information based on the amplitude information and the energy information of the electroencephalogram signals.
6. The sleep state identification method of claim 1 wherein the inputting of the electroencephalogram signal and the signal quality information into the trained first neural network model is preceded by:
acquiring first training data and an initial first neural network model, wherein the first training data comprises a training electroencephalogram signal, training signal quality information corresponding to the training electroencephalogram signal and a sleep state corresponding to the training electroencephalogram signal;
and training the initial neural network model based on the first training data to obtain the trained first neural network model.
7. The sleep state identification method according to claim 1, wherein the obtaining the sleep state of the person to be collected further comprises:
based on the sleep state, the sleep music, the sleep light and the sleep temperature corresponding to the sleep state in the sleep environment adjustment database are matched and acquired, and are applied to the current environment.
8. A sleep state identification device, comprising:
an electroencephalogram signal acquisition module: the method is used for acquiring the brain electrical signals of the acquired person;
the signal quality information acquisition module: the signal quality information is acquired based on the electroencephalogram signals; wherein, obtaining signal quality information based on the electroencephalogram signal includes: determining signal quality information of the electroencephalogram signals according to the duty ratio of the electroencephalogram signals in all acquired signals; the signal quality information reflects the action and the action amplitude of the collected person;
sleep state identification module: the method comprises the steps of inputting the electroencephalogram signals and the signal quality information into a trained first neural network model to obtain the sleep state of the acquired person;
performing posterior judgment on the sleep state of the acquired person according to a preset sliding window and a trigger step length;
taking the sleep state obtained by the posterior judgment as the final sleep state of the person to be acquired;
the posterior judgment of the sleep state of the acquired person is carried out according to a preset sliding window and a trigger step length, and the posterior judgment comprises the following steps:
dividing a first time window in which the sleep state of the person to be acquired is located into a plurality of time intervals with the length of a preset step length;
determining a first preset number of time intervals at the front end of the first time window as a second time window, and determining an awake state as a sleep state of the second time window;
determining the length of a first preset number of time intervals as the length of a sliding window;
sliding the sliding window along the first time window one by one time interval, determining the sleep state of each time interval after the second time window in the first time window according to the sleep state in the sliding window, including:
if the sleep state of at least the second preset number of time intervals in the first preset number of time intervals in which the sliding window is positioned is the preset sleep state, determining the preset sleep state as the sleep state of the first time interval behind the sliding window; the preset sleep states comprise a waking state, a shallow sleep state and a deep sleep state.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the sleep state identification method of any one of claims 1 to 7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the sleep state identification method of any one of claims 1 to 7.
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