CN109567750B - Sleep stage determination method and device and computer equipment - Google Patents
Sleep stage determination method and device and computer equipment Download PDFInfo
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Abstract
The application relates to a sleep stage determination method, a sleep stage determination device and computer equipment. As the computer equipment acquires the electromyographic signals and the body movement signals of the user, judges the electromyographic signals and the body movement signals according to the baseline parameters, and obtains the stage that the body movement amplitude and the electromyographic amplitude of the user are small according to the judgment result, the REM period of the user can be determined.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a method and a device for determining sleep stages and computer equipment.
Background
In recent years, as the living standard of people is improved, more and more people begin to pay attention to the sleeping condition of the people. Currently, the internationally accepted sleep evaluation method is the Polysomnography (PSG) technology, which performs sleep medical research and sleep disease diagnosis by continuously and synchronously recording and analyzing electroencephalograms, electrooculograms, mandibular electromyograms, electrocardiograms, respiratory activities, and other physiological and physical activities. In order to facilitate the implementation of sleep monitoring, the sleep condition is judged by acquiring the activity intensity data of the body through bracelet equipment in the conventional technology.
However, since the bracelet device generally collects only the body movement data and the heart rate data, the REM period in the sleep stage cannot be determined.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus and a computer device for determining sleep stages of REM periods.
A method of determining sleep stages, the method comprising:
acquiring electromyographic signals and body movement signals of a user;
acquiring a baseline parameter according to the electromyographic signal and the body movement signal; the baseline parameter is used for representing the signal characteristics of the electromyographic signal and the body movement signal;
and determining a rapid eye movement REM period according to the baseline parameters.
In one embodiment, the obtaining of baseline parameters from the electromyographic signals and the body movement signals includes:
carrying out low-pass filtering processing on the electromyographic signals and the body movement signals to obtain filtered electromyographic signals and filtered body movement signals;
and acquiring the baseline parameters according to the filtered electromyographic signals and the filtered body movement signals.
In one embodiment, the obtaining the baseline parameter according to the filtered electromyographic signal and the filtered body motion signal includes:
acquiring electromyographic feature data according to the filtered electromyographic signals;
acquiring body motion characteristic data according to the filtered body motion signals;
obtaining a correlation coefficient between the filtered electromyographic signal and the filtered body motion signal;
and determining the baseline parameters according to the electromyographic characteristic data, the body movement characteristic data and the correlation coefficient.
In one embodiment, the determining a rapid eye movement REM period based on the baseline parameter comprises:
determining a sleep stage of the user according to the baseline parameters;
acquiring myoelectricity baseline data according to the sleep data of the sleep stage;
comparing the magnitude relation between the value of the electromyographic baseline data and the value of the electromyographic signal to obtain a comparison result;
determining the REM period according to the comparison result.
In one embodiment, said determining a sleep stage of the user based on said baseline parameters comprises:
classifying the sleep data of the user according to the body movement characteristic data to acquire a first type of each sleep data; the sleep data comprises body movement data and myoelectric data;
correcting the first type of each sleep data according to the electromyographic feature data to obtain a corrected second type of each sleep data;
and determining the sleep stage according to the corrected second type of each sleep data.
In one embodiment, the classifying the sleep data of the user according to the body movement characteristic data to obtain a first type of each sleep data includes:
if the value of the body movement data is smaller than a preset first threshold value, determining the sleep data as potential sleep data;
if the value of the body movement data is larger than a preset second threshold value, determining the sleep data as potential wakeful data;
and if the value of the body motion data is greater than or equal to the first threshold and less than or equal to the second threshold, determining the sleep data as potential sleep-wake data.
In one embodiment, the correcting the first type of each sleep data according to the electromyographic feature data to obtain the corrected second type of each sleep data includes:
comparing the magnitude relation between the value of the electromyographic data corresponding to each first type and the value of the electromyographic feature data to obtain a comparison result;
and correcting the first type of each sleep data according to the comparison result and the correlation coefficient to obtain a corrected second type of each sleep data.
An apparatus for sleep staging, the apparatus comprising:
the first module is used for acquiring a myoelectric signal and a body movement signal of a user;
the second module is used for acquiring baseline parameters according to the electromyographic signals and the body movement signals; the baseline parameter is used for representing the signal characteristics of the electromyographic signal and the body movement signal.
A third module for determining a rapid eye movement REM period based on the baseline parameter.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method for determining sleep stages when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of determining a sleep stage.
According to the sleep stage determining method, the sleep stage determining device and the computer equipment, the computer equipment obtains the electromyographic signals and the body movement signals of the user, then obtains the baseline parameters according to the electromyographic signals and the body movement signals, and determines the REM stage according to the baseline parameters. As the computer equipment acquires the electromyographic signals and the body movement signals of the user, judges the electromyographic signals and the body movement signals according to the baseline parameters, and obtains the stage that the body movement amplitude and the electromyographic amplitude of the user are small according to the judgment result, the REM period of the user can be determined.
Drawings
FIG. 1 is a diagram of an exemplary system for determining sleep stages;
FIG. 2 is a flow diagram illustrating a method for determining sleep stages in one embodiment;
FIG. 3 is a flowchart illustrating a method for determining sleep stages according to another embodiment;
FIG. 4 is a flowchart illustrating a method for determining sleep stages according to another embodiment;
FIG. 5 is a flowchart illustrating a method for determining sleep stages according to another embodiment;
FIG. 6 is a block diagram of an apparatus for determining sleep stages in one embodiment;
FIG. 7 is a block diagram showing the construction of a sleep stage determining apparatus according to another embodiment;
FIG. 8 is a block diagram showing the construction of a sleep stage determining apparatus according to another embodiment;
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The sleep stage determination method provided by the application can be applied to the application environment shown in fig. 1. The collecting device 110 is connected to the computer device 120 through a network, and may be a bracelet, a sleep meter, or other devices for collecting the electromyographic signals and the body movement signals of the user; the computer device 120 may be a server, and may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
It should be noted that the execution subject of the embodiment of the present application may be a sleep stage determination apparatus, which may be implemented as part of or all of a computer device by software, hardware, or a combination of software and hardware. The method embodiments described below are described by taking as an example that the execution subject is a computer device.
In one embodiment, as shown in fig. 2, a method for determining sleep stages is provided, which is described by taking the method as an example applied to the computer device 120 in fig. 1, and includes:
s101, acquiring a myoelectric signal and a body movement signal of a user.
The electromyographic signal is a signal obtained by collecting an electric signal accompanying muscle contraction of a user during sleep, and the body movement signal is a signal generated by body movement of the user during sleep. The sleep stage is to divide each stage of sleep of a user for research convenience, and can be divided into a rapid eye movement (rapid eye movement) stage and a Rapid Eye Movement (REM) stage.
When acquiring the electromyographic signals and the body movement signals of the user, the computer device may acquire the electromyographic signals and the body movement signals through an acquisition device, where the acquisition device may be a bracelet, or may be a sleep meter, or optionally may also acquire the electromyographic signals and the body movement signals through an acquisition electrode sheet, and the acquisition modes of the electromyographic signals and the body movement signals are not limited herein. For the electromyographic signals and the body movement signals obtained by the method, the electromyographic signals and the body movement signals can also have different forms, for example, a computer device can collect the electromyographic signals of a user through two collecting electrodes, the obtained electromyographic signals can be a differential signal, and the amplitude of the electromyographic signals fluctuates above and below 0V; in addition, the computer device may further collect the electromyographic signals of the user through three collecting electrodes, and the obtained electromyographic signals may be in a form of a differential signal plus a baseline value, for example, the baseline value may be 1.5V, and the amplitude of the electromyographic signals fluctuates around 1.5V. The forms of the myoelectric signal and the body motion signal are not limited herein.
S102, acquiring baseline parameters according to the electromyographic signals and the body movement signals; the baseline parameters are used to represent signal characteristics of electromyographic signals and body motion signals.
Specifically, the computer device may obtain the baseline parameters after obtaining the electromyographic signals and the body movement signals. The baseline parameter is a baseline value for determining the myoelectric signal and the body motion signal. The baseline parameter is used to represent signal characteristics of the electromyographic signal and the body movement signal, the signal characteristics may be a maximum value, a minimum value, an average value of signal amplitudes, a signal frequency, and the like, and the type of the signal characteristic corresponding to the baseline parameter is not limited herein.
Further, the baseline parameter may be a signal feature extracted from the electromyographic signal and the body movement signal by the computer device, or another parameter acquired on the basis of the signal feature, or alternatively, a signal feature extracted after the electromyographic signal and the body movement signal are processed. For example, the computer device may extract signal features such as a maximum value, a minimum value, an average value, a frequency and the like from the electromyographic signal and the body movement signal, and the baseline parameter may include the maximum electromyographic amplitude value and may also include the average electromyographic amplitude value; the computer equipment can also extract the average value of the amplitude of the electromyographic signals and the body movement signals in each minute, and the average value of the electromyographic signals and the body movement signals in the whole acquisition time can be obtained on the average value of the amplitude of the signals in each minute, namely the average value of the electromyographic amplitudes and the average value of the body movement signals; the correlation coefficient of the electromyographic signal and the body movement signal can be obtained by calculating the average value of the amplitudes of the electromyographic signal and the body movement signal in each minute, and the correlation coefficient is a coefficient reflecting the degree of closeness of the correlation between the electromyographic signal and the body movement signal. The manner of obtaining the baseline parameters is not limited herein.
And S103, determining a rapid eye movement REM period according to the baseline parameters.
The REM period is a stage of sleep, in the sleep of one night, one person usually has four to five sleep cycles, each sleep cycle may include the REM period, and as the sleep of the user enters the REM period, the physical movement is weakened and is represented as smaller physical movement amplitude; meanwhile, the muscle tension is obviously reduced, the muscle is in a completely relaxed state, and the myoelectric signal shows that the myoelectric amplitude is smaller.
Specifically, the computer device may perform a combined determination on the electromyographic signal and the body movement signal according to each of the baseline parameters, obtain a stage in which both the body movement signal and the electromyographic signal of the user are small, and determine the REM period of the user. For the data of the whole acquisition stage, the computer equipment can determine a first stage with smaller body motion amplitude in the body motion signal according to the baseline parameters, then determine a second stage with smaller myoelectricity amplitude from the first stage, and determine the REM period of the user according to the second stage; the computer equipment can also judge a first stage with smaller body movement amplitude in the body movement signals and a second stage with smaller myoelectric amplitude in the myoelectric signals in the whole acquisition stage, then combine the first stage and the second stage and further determine the REM stage. The determination method of the REM period is not limited herein.
Taking a method for determining the REM period as an example, a computer device acquires an electromyographic signal and a body movement signal of a user through collection, and determines baseline parameters as an electromyographic amplitude average value, a body movement amplitude average value and a correlation coefficient of the electromyographic signal and the body movement signal according to the electromyographic signal and the body movement signal; and performing combined judgment on the electromyographic signals and the body movement signals according to the baseline parameters to determine the REM period of the user.
According to the sleep stage determining method, the computer equipment acquires the electromyographic signals and the body movement signals of the user, then acquires the baseline parameters according to the electromyographic signals and the body movement signals, and determines the REM stage according to the baseline parameters. The REM period is mainly characterized in that when the user sleeps in the period, the myoelectric signal amplitude is smaller, the computer device obtains the myoelectric signal and the body movement signal of the user, judges the myoelectric signal and the body movement signal according to the baseline parameter, and obtains the stage that the body movement amplitude and the myoelectric amplitude of the user are smaller according to the judgment result, so that the REM period of the user can be determined.
Fig. 3 is a schematic flowchart of a method for determining sleep stages in another embodiment, where the embodiment relates to a manner in which a computer device obtains baseline parameters according to an electromyographic signal and a physical activity signal, as shown in fig. 3, where S102 includes:
s201, low-pass filtering is conducted on the electromyographic signals and the body motion signals, and filtered electromyographic signals and filtered body motion signals are obtained.
The electromyographic signals and the body movement signals acquired by the computer device may include some interference signals, which may be caused by the acquisition device, or may be caused by improper operation during acquisition, or abnormal actions of the user during acquisition, etc. The computer equipment respectively carries out low-pass filtering processing on the electromyographic signals and the body movement signals to obtain filtered electromyographic signals and filtered body movement signals with small interference.
Specifically, the computer device may select a cut-off frequency of the low-pass filter according to frequency characteristics of the electromyographic signal and the body motion signal for the electromyographic signal and the body motion signal through the low-pass filter. For example, the cut-off frequency of the low-pass filter for electromyographic signals may be selected to be 10 Hz, and the cut-off frequency of the low-pass filter for body motion signals may be 3 Hz.
S202, acquiring baseline parameters according to the filtered electromyographic signals and the filtered body movement signals.
On the basis of low-pass filtering processing of the electromyographic signals and the body motion signals, baseline parameters can be obtained according to the filtered electromyographic signals and the filtered body motion signals. The above-mentioned baseline parameters are obtained in a similar manner as in S102, and will not be described in detail here.
Optionally, the computer device may obtain electromyographic feature data according to the filtered electromyographic signal; acquiring body motion characteristic data according to the filtered body motion signals; and acquiring a correlation coefficient between the filtered electromyographic signal and the filtered body motion signal, and determining the baseline parameter according to the electromyographic characteristic data, the body motion characteristic data and the correlation coefficient.
The electromyographic feature data refers to signal features extracted according to the filtered electromyographic signals, and may include signal features such as a maximum value, a minimum value, an average value, a frequency and the like; the body motion characteristic data refers to signal characteristics extracted according to the filtered electromyographic signals, and may include signal characteristics such as a maximum value, a minimum value, an average value, a frequency and the like.
Continuing with the example of a method for determining the REM period, the computer device performs low-pass filtering on the obtained body motion signal and the electromyographic signal, and the low-pass body motion signal and the low-pass electromyographic signal may contain multiple times of collected data per minute according to different collection frequencies. The computer equipment can obtain the body motion amplitude average value Xi in each minute according to the filtered body motion signals, wherein i identifies the minute value in the acquisition stage; then averaging the Xi in the whole acquisition time to obtain a body motion amplitude average value X; similarly, the myoelectric amplitude average value Yi per minute can be obtained according to the filtered myoelectric signal, and the Yi is averaged in the whole acquisition time length to obtain the myoelectric amplitude average value Y; then according to the formula
And acquiring a correlation coefficient Mi of the internal body movement signal and the electromyographic signal every minute. On the basis of the calculation, the baseline parameters can be obtained as a body motion amplitude average value X, a myoelectricity amplitude average value Y and a correlation coefficient Mi.
According to the sleep stage determining method, the computer equipment performs low-pass filtering processing on the myoelectric signals and the body movement signals, so that the computer equipment can acquire and judge the baseline parameters according to the myoelectric signals and the body movement signals with small interference, and the determined REM stage is more accurate.
Fig. 4 is a flowchart illustrating a method for determining sleep stages in another embodiment. The embodiment relates to a specific process of determining the REM period according to the baseline parameters by the computer device, as shown in fig. 4, the step S103 includes:
s301, determining the sleep stage of the user according to the baseline parameters.
Specifically, the electromyographic signals and the body movement signals acquired by the computer device include a sleep stage and a waking stage of the user, and the REM period is one stage of the sleep stage of the user.
When determining the sleep stage of the user, the computer device may determine the sleep stage of the user according to the body movement characteristic data, or may determine the sleep stage of the user by combining the body movement signal and the myoelectric signal, and the determination manner of the sleep stage of the user is not limited herein.
S302, acquiring myoelectricity baseline data according to sleep data of the sleep stage.
The sleep data of the sleep stage refers to data corresponding to the sleep stage after the sleep stage is determined in the previous step, and may be a body movement signal and an electromyographic signal of the sleep stage, or a filtered body movement signal and a filtered electromyographic signal of the sleep stage.
The electromyographic baseline data refers to signal characteristics extracted according to sleep data of sleep stages and is used for judging the magnitude relation between values of the electromyographic signals. The signal characteristic may be an average, maximum or minimum value and the computer device may then determine the electromyographic baseline data from the signal characteristic. For example, the computer device may obtain a sleep stage myoelectric amplitude average value according to the filtered myoelectric signal in the sleep data, and then determine the myoelectric baseline data according to the size of the myoelectric amplitude average value and a preset coefficient.
S303, comparing the size relationship between the value of the electromyographic baseline data and the value of the electromyographic signal to obtain a comparison result.
Specifically, the magnitude relation may be a proportional relation between a value of the electromyographic baseline data and a value of the electromyographic signal, and the magnitude relation between the value of the electromyographic baseline data and the value of the electromyographic signal may be determined by comparing the proportional relation with 1; the difference value obtained by subtracting the value of the electromyographic signal from the value of the electromyographic baseline data may be determined according to the magnitude relationship between the difference value and 0, or the magnitude relationship between the value of the electromyographic baseline data and the value of the electromyographic signal may be determined, and the expression form of comparing the magnitude relationship is not limited herein.
And S304, determining the REM period according to the comparison result.
And in the sleep stage, comparing the value of the electromyographic signal with the value of electromyographic baseline data, and if the value of the electromyographic signal is smaller than the value of the electromyographic baseline data, considering that the electromyographic signal of the user is smaller as the REM stage by the computer. For example, the mean value of the electromyographic magnitudes of the sleep stages acquired by the computer device according to the above steps may be 0.5, the electromyographic baseline data may be determined to be 0.2, and when the electromyographic signal of the user is less than 0.2 in the sleep stage, the electromyographic signal of the user may be considered to be much less than the mean value of the electromyographic magnitudes in the sleep stage, and may be determined as the REM period.
Optionally, the REM period of the user may be corrected. In one correction mode, the computer device may convert the electromyographic signals in the sleep data into frequency domain signals and perform high-pass filtering; if the frequency domain signal is high-pass filtered, and a pulse signal appears in a preset frequency interval, for example, between 0.5 hz and 3 hz, the computer device may consider that the user has a slow wave feature at this stage, and when the slow wave feature appears, the user is in a deep sleep period, not a REM period; further, the computer device may determine which stage the slow wave feature occurs by narrowing the range of the electromyographic signal, continuing to convert the electromyographic signal into a frequency domain signal, and then performing a determination. The computer device can remove the stage of the slow wave characteristic in the determined REM period, namely the REM period after correction.
According to the sleep stage determining method, the computer device can acquire the sleep stage of the user according to the baseline parameters, and further determine the REM stage of the user in the sleep stage. Because the electromyographic signals in the REM period are smaller, the sleep stage of the user is acquired first, and the stage with larger electromyographic signals is eliminated, so that the electromyographic signals can be judged more accurately, and the accurate REM period can be acquired.
Fig. 5 is a schematic flowchart of a method for determining sleep stages in another embodiment, where the embodiment relates to a specific process of determining a sleep stage of a user according to a baseline parameter by a computer device, as shown in fig. 5, the step S301 includes:
s401, classifying the sleep data of the user according to the body movement characteristic data to obtain a first type of each sleep data; the sleep data includes body movement data and myoelectric data.
When the computer device determines the sleep stage of the user according to the baseline parameters, the sleep data of the user can be classified according to the body movement characteristic data. The sleep data includes body motion data and electromyographic signal data, the body motion data may be a body motion signal or a filtered body motion signal, and the electromyographic data may be an electromyographic signal or a filtered electromyographic signal.
Specifically, the computer device may classify the body motion data according to the body motion characteristic data to obtain a first type of the body motion data, and then obtain the electromyographic signals at corresponding times according to different types of the body motion signals, so as to obtain the first type of the electromyographic signals. When the computer equipment acquires the first type of the body motion data according to the body motion characteristic data, the computer equipment can compare the body motion characteristic data with the body motion data and determine the first type of the body motion data according to a comparison result; the body motion threshold value can be obtained through the body motion characteristic data, and the first type of the body motion data can be determined according to the comparison between the body motion threshold value and the body motion data.
Alternatively, the first threshold value and the second threshold value may be determined based on the body motion characteristic signal. The first type of the sleep data may include: potential sleep data, potential wakefulness data, and potential sleep-wakefulness data. If the value of the body movement data is smaller than a preset first threshold value, determining the sleep data as potential sleep data; if the value of the body movement data is larger than a preset second threshold value, determining the sleep data as potential wakeful data; and if the value of the body motion data is greater than or equal to the first threshold and less than or equal to the second threshold, determining the sleep data as potential sleep-awake data.
Continuing with the example of a method for determining the REM period, the computer device sets the body motion amplitude average value as body motion characteristic data, the filtered body motion signal as body motion data, and determines a first threshold and a second threshold according to the body motion amplitude average value, and then judges the low-pass body motion signal according to the first threshold and the second threshold. For example, the average value of the body motion amplitude may be 0.5, the first threshold may be preset to be 0.2, the second threshold is 0.8, if the value of the filtered body motion signal is smaller than the second threshold, the computer device considers that the body motion amplitude of the user is small, the user may be in a sleep stage, and the body motion data may be determined as potential sleep data; if the value of the filtered body motion signal is greater than the second threshold, the computer device considers that the body motion amplitude of the user is large, the user is likely to be in a waking stage, and the body motion data can be determined to be potential waking data; if the value of the filtered body motion signal is greater than or equal to the first threshold and less than or equal to the second threshold, the computer considers that the user may be in a wake phase and may also be in a sleep phase, and may determine that the body motion data is potential sleep-wake data.
S402, correcting the first type of each sleep data according to the electromyographic feature data, and acquiring the corrected second type of each sleep data.
On the basis of the above steps, the computer device determines potential sleep data, potential wakeful data and potential sleep-wakeful data, and further corrects the type of the above data according to the electromyographic characteristic data, and may determine a second type of the above data, for example, may correct the potential sleep data, and may determine which of the above potential sleep data are sleep stage data and which are wakeful stage data, that is, data that the computer device considers that the user is in a sleep state and data that is in a wakeful stage.
Specifically, when the computer device corrects the first type of each sleep data, the computer device may correct the first type of each sleep data according to the electromyographic feature data, or may correct the first type of each sleep data according to the electromyographic feature data and the correlation coefficient in combination.
Optionally, the computer device may compare the magnitude relationship between the value of the electromyographic data corresponding to each first type and the value of the electromyographic feature data, and obtain a comparison result; and then, according to the comparison result and the correlation coefficient, correcting the first type of each sleep data to obtain a corrected second type of each sleep data.
Continuing with the example of a method for determining the REM period, when the body motion amplitude is smaller and the myoelectric amplitude is smaller in the potential sleep data, the potential wakeful data, and the potential sleep wakeful data, the computer device may determine that the user is in a sleep stage, and the corresponding body motion signal and the corresponding myoelectric signal are sleep stage data. However, for other data, if the body motion amplitude is large or the myoelectric amplitude is large, the correlation coefficient is also large, the computer device considers that the body motion signal and the myoelectric signal are closely related, the body motion signal is likely to be caused by the large myoelectric signal, the myoelectric signal is likely to be caused by the large body motion signal, for example, muscle contraction caused by turning over of the user during sleep is strong, which is reflected in that the myoelectric signal is increased due to the large body motion signal; the data may be judged again after values of the body motion signal and the myoelectric signal corresponding to a value having a larger correlation ratio are removed, and if the body motion amplitude and the myoelectric amplitude are smaller, the data may be regarded as sleep stage data.
When the potential sleep data are corrected, if the electromyographic signals in the potential sleep data are smaller than the electromyographic characteristic data, the potential sleep data are determined to be sleep stage data; if the electromyographic signals are larger than the electromyographic characteristic data in a certain stage of the potential sleep stages, further judging the value of a correlation coefficient corresponding to the electromyographic signals, if the correlation coefficient is larger than a preset threshold value, setting the value of the electromyographic signals corresponding to the correlation coefficient larger than the threshold value to be 0, judging whether the electromyographic signals in the stage are smaller than the electromyographic characteristic data again, if so, determining the data to be the sleep stage data, and if not, determining the data to be the waking stage data.
When the potential waking data is corrected, if the electromyographic signals in the data of the potential waking stage are larger than the electromyographic characteristic data, the data are determined to be the waking stage data; if the electromyographic signals are smaller than the electromyographic characteristic data in a certain stage of the potential waking data, further judging the value of a correlation coefficient corresponding to the electromyographic signals, if the correlation coefficient is larger than a preset threshold value, considering that the correlation coefficient is larger, setting the value of the body motion signal corresponding to the correlation coefficient larger than the threshold value as 0, judging whether the body motion signals in the stage are smaller than the body motion characteristic data again, if so, determining the data as the sleep stage data, and if not, determining the data as the waking stage data.
When the potential sleep waking data are corrected, two reference values are determined through the electromyographic characteristic data, if the electromyographic signal in the potential sleep waking data is smaller than a first reference value, the electromyographic signal is considered to be far smaller than the electromyographic characteristic data, and the potential sleep waking data is determined to be sleep stage data; if the electromyographic signal is larger than a second reference value, the electromyographic signal is considered to be far larger than the electromyographic characteristic data, and the data is determined to be waking-stage data; and for other data, setting the electromyographic signals and the body movement signals of the corresponding stages as 0 by judging the size of the correlation coefficient when the correlation coefficient is larger than the corresponding threshold value, and further judging to determine whether the data is sleep stage data or waking stage data.
And S403, determining the sleep stage according to the corrected second type of the sleep data.
On the basis of the steps, the computer equipment corrects the first type of each sleep data through the electromyographic characteristic data to obtain the sleep stage data of the user; according to the sleep stage data, the sleep stage of the user can be determined.
According to the method for determining the sleep data, the computer device determines the first type of the sleep data through the body movement characteristic data, and then corrects the first type through the electromyographic characteristic data, so that the sleep stage determined by the computer device can be more accurate, and the accurate REM period can be determined.
It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-5 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 6, there is provided a sleep stage determination apparatus including: a first obtaining module 10, a second obtaining module 20 and a determining module 30, wherein:
the first acquisition module 10 is used for acquiring a myoelectric signal and a body movement signal of a user;
the second acquisition module 20 is configured to acquire a baseline parameter according to the electromyographic signal and the body movement signal; the baseline parameters are used for representing the signal characteristics of the electromyographic signals and the body movement signals;
a determination module 30 for determining a rapid eye movement REM period based on the baseline parameters.
The sleep stage determination apparatus provided in the embodiment of the present invention may implement the method embodiments described above, and its implementation principle and technical effect are similar, which are not described herein again.
In one embodiment, as shown in fig. 7, the second obtaining module 20 includes a filtering unit 201 and a obtaining unit 202, where:
a filtering unit 201, configured to perform low-pass filtering processing on the electromyographic signal and the body motion signal to obtain a filtered electromyographic signal and a filtered body motion signal;
a first obtaining unit 202, configured to obtain a baseline parameter according to the filtered electromyographic signal and the filtered body motion signal.
In an embodiment, the first obtaining unit 202 is specifically configured to: acquiring electromyographic feature data according to the filtered electromyographic signals; acquiring body motion characteristic data according to the filtered body motion signals; obtaining a correlation coefficient between the filtered electromyographic signal and the filtered body motion signal; and determining the baseline parameters according to the electromyographic characteristic data, the body movement characteristic data and the correlation coefficient.
In one embodiment, as shown in fig. 8, the determining module 30 includes a first determining unit 301, a second obtaining unit 302, a comparing unit 303, and a second determining unit 304, wherein:
a first determining unit 301, configured to determine a sleep stage of the user according to the baseline parameter;
a second obtaining unit 302, configured to obtain myoelectric baseline data according to sleep data of a sleep stage;
a comparison unit 303, configured to compare a magnitude relationship between a value of the electromyographic baseline data and a value of the electromyographic signal, and obtain a comparison result;
a second determining unit 304, configured to determine the REM period according to the comparison result.
In an embodiment, the first determining unit 301 is specifically configured to: classifying the sleep data of the user according to the body movement characteristic data to obtain a first type of each sleep data; the sleep data comprises body movement data and myoelectric data; correcting the first type of each sleep data according to the electromyographic feature data to obtain a second type of each sleep data after correction; and determining the sleep stage according to the corrected second type of the sleep data.
In an embodiment, the "classifying the sleep data of the user according to the body movement characteristic data by the first determining unit 301 to obtain the first type of each sleep data" includes: the first determining unit 301 is specifically configured to determine that the sleep data is potential sleep data if the value of the body movement data is smaller than a preset first threshold; if the value of the body movement data is larger than a preset second threshold value, determining the sleep data as potential wakeful data; and if the value of the body motion data is greater than or equal to the first threshold and less than or equal to the second threshold, determining the sleep data as potential sleep-awake data.
In one embodiment, the "first determination unit 301 sleep data includes body movement data and myoelectric data; correcting the first type of each sleep data according to the electromyographic feature data to obtain a corrected second type of each sleep data, comprising the following steps: the first determining unit 301 is specifically configured to compare magnitude relationships between values of the electromyographic data corresponding to each first type and values of the electromyographic feature data, and obtain a comparison result; and correcting the first type of each sleep data according to the comparison result and the correlation coefficient to obtain a second type of each sleep data after correction.
For specific limitations of the sleep stage determination device, reference may be made to the above limitations of the sleep stage determination method, which are not described herein again. The various modules in the sleep staging determination apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the determination data of the sleep stage. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of sleep staging determination.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring electromyographic signals and body movement signals of a user;
acquiring a baseline parameter according to the electromyographic signal and the body movement signal; the baseline parameters are used for representing the signal characteristics of the electromyographic signals and the body movement signals;
based on the baseline parameters, the rapid eye movement REM period is determined.
In one embodiment, the processor, when executing the computer program, further performs the steps of: performing low-pass filtering processing on the electromyographic signals and the body movement signals to obtain filtered electromyographic signals and filtered body movement signals; and acquiring baseline parameters according to the filtered electromyographic signals and the filtered body motion signals.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring electromyographic feature data according to the filtered electromyographic signals; acquiring body motion characteristic data according to the filtered body motion signals; obtaining a correlation coefficient between the filtered electromyographic signal and the filtered body motion signal; and determining the baseline parameters according to the electromyographic characteristic data, the body movement characteristic data and the correlation coefficient.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a sleep stage of the user according to the baseline parameters; acquiring myoelectricity baseline data according to sleep data of a sleep stage; comparing the magnitude relation between the value of the electromyographic baseline data and the value of the electromyographic signal to obtain a comparison result; and determining the REM period according to the comparison result.
In one embodiment, the processor, when executing the computer program, further performs the steps of: classifying the sleep data of the user according to the body movement characteristic data to obtain a first type of each sleep data; the sleep data comprises body movement data and myoelectric data; correcting the first type of each sleep data according to the electromyographic feature data to obtain a second type of each sleep data after correction; and determining the sleep stage according to the corrected second type of the sleep data.
In one embodiment, the processor, when executing the computer program, further performs the steps of: if the value of the body movement data is smaller than a preset first threshold value, determining the sleep data as potential sleep data; if the value of the body movement data is larger than a preset second threshold value, determining the sleep data as potential wakeful data; and if the value of the body motion data is greater than or equal to the first threshold and less than or equal to the second threshold, determining the sleep data as potential sleep-awake data.
In one embodiment, the processor, when executing the computer program, further performs the steps of: comparing the magnitude relation between the value of the electromyographic data corresponding to each first type and the value of the electromyographic feature data to obtain a comparison result; and correcting the first type of each sleep data according to the comparison result and the correlation coefficient to obtain a second type of each sleep data after correction.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring electromyographic signals and body movement signals of a user;
acquiring a baseline parameter according to the electromyographic signal and the body movement signal; the baseline parameters are used for representing the signal characteristics of the electromyographic signals and the body movement signals;
based on the baseline parameters, the rapid eye movement REM period is determined.
In one embodiment, the computer program when executed by the processor further performs the steps of: performing low-pass filtering processing on the electromyographic signals and the body movement signals to obtain filtered electromyographic signals and filtered body movement signals; and acquiring baseline parameters according to the filtered electromyographic signals and the filtered body motion signals.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring electromyographic feature data according to the filtered electromyographic signals; acquiring body motion characteristic data according to the filtered body motion signals; obtaining a correlation coefficient between the filtered electromyographic signal and the filtered body motion signal; and determining the baseline parameters according to the electromyographic characteristic data, the body movement characteristic data and the correlation coefficient.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a sleep stage of the user according to the baseline parameters; acquiring myoelectricity baseline data according to sleep data of a sleep stage; comparing the magnitude relation between the value of the electromyographic baseline data and the value of the electromyographic signal to obtain a comparison result; and determining the REM period according to the comparison result.
In one embodiment, the computer program when executed by the processor further performs the steps of: classifying the sleep data of the user according to the body movement characteristic data to obtain a first type of each sleep data; the sleep data comprises body movement data and myoelectric data; correcting the first type of each sleep data according to the electromyographic feature data to obtain a second type of each sleep data after correction; and determining the sleep stage according to the corrected second type of the sleep data.
In one embodiment, the computer program when executed by the processor further performs the steps of: if the value of the body movement data is smaller than a preset first threshold value, determining the sleep data as potential sleep data; if the value of the body movement data is larger than a preset second threshold value, determining the sleep data as potential wakeful data; and if the value of the body motion data is greater than or equal to the first threshold and less than or equal to the second threshold, determining the sleep data as potential sleep-awake data.
In one embodiment, the computer program when executed by the processor further performs the steps of: comparing the magnitude relation between the value of the electromyographic data corresponding to each first type and the value of the electromyographic feature data to obtain a comparison result; and correcting the first type of each sleep data according to the comparison result and the correlation coefficient to obtain a second type of each sleep data after correction.
The implementation principle and technical effect of the computer-readable storage medium provided by this embodiment are similar to those of the above-described method embodiment, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for determining sleep stages, the method comprising:
acquiring electromyographic signals and body movement signals of a user;
acquiring a baseline parameter according to the electromyographic signal and the body movement signal; the baseline parameter is used for representing the signal characteristics of the electromyographic signal and the body movement signal;
determining a sleep stage of the user according to the baseline parameters;
determining a rapid eye movement REM period according to the sleep data of the sleep stage;
wherein, still include the step to carry on the correction to the said sleep data:
classifying sleep data of the sleep stage of the user according to the body movement signal to acquire a first type of the sleep data; the sleep data comprises body movement data and myoelectric data; the first type of sleep data comprises: potential sleep data, potential wakefulness data, and potential sleep-wakefulness data;
comparing the magnitude relation between the value of the electromyographic data corresponding to the first type of each sleep data and the value of the electromyographic feature data to obtain a comparison result;
and correcting the first type of each sleep data according to the comparison result, the filtered electromyographic signals and the filtered body movement signals to obtain a second type of each sleep data after correction.
2. The method of claim 1, wherein the obtaining baseline parameters from the electromyographic signals and the body movement signals comprises:
carrying out low-pass filtering processing on the electromyographic signals and the body movement signals to obtain filtered electromyographic signals and filtered body movement signals;
and acquiring the baseline parameters according to the filtered electromyographic signals and the filtered body movement signals.
3. The method of claim 2, wherein said deriving the baseline parameters from the filtered electromyographic signals and the filtered body motion signals comprises:
acquiring electromyographic feature data according to the filtered electromyographic signals;
acquiring body motion characteristic data according to the filtered body motion signals;
and determining the baseline parameters according to the electromyographic characteristic data, the body movement characteristic data and the correlation coefficient.
4. The method of claim 3, wherein determining a Rapid Eye Movement (REM) period based on the sleep data for the sleep stage comprises:
acquiring myoelectricity baseline data according to the sleep data of the sleep stage;
comparing the magnitude relation between the value of the electromyographic baseline data and the value of the electromyographic signal to obtain a comparison result;
determining the REM period according to the comparison result.
5. The method of claim 3, wherein determining the sleep stage of the user based on the baseline parameters comprises:
classifying the sleep data of the user according to the body movement characteristic data to acquire a first type of each sleep data; the sleep data comprises body movement data and myoelectric data;
correcting the first type of each sleep data according to the electromyographic feature data to obtain a corrected second type of each sleep data;
and determining the sleep stage according to the corrected second type of each sleep data.
6. The method of claim 5, wherein the classifying the sleep data of the user according to the body movement characteristic data to obtain the first type of each sleep data comprises:
if the value of the body movement data is smaller than a preset first threshold value, determining the sleep data as potential sleep data;
if the value of the body movement data is larger than a preset second threshold value, determining the sleep data as potential wakeful data;
and if the value of the body motion data is greater than or equal to the first threshold and less than or equal to the second threshold, determining the sleep data as potential sleep-wake data.
7. The method of claim 1, wherein the correcting the first type of each of the sleep data according to the comparison result and a correlation coefficient between the filtered electromyographic signal and the filtered body movement signal to obtain a corrected second type of each of the sleep data comprises:
if the electromyographic signals in the potential sleep data are smaller than the electromyographic feature data, determining that the sleep data are sleep stage data; if the electromyographic signal in the potential sleep data is larger than the electromyographic feature data, determining a value of a correlation coefficient corresponding to the electromyographic signal, if the value of the correlation coefficient is larger than a preset threshold value, setting the value of the electromyographic signal corresponding to the correlation coefficient larger than the preset threshold value to be 0, judging whether the electromyographic signal is smaller than the electromyographic feature data, if the electromyographic signal is smaller than the electromyographic feature data, determining that the sleep data is sleep stage data, and if not, determining that the sleep data is waking stage data;
if the electromyographic signals in the potential waking data are larger than the electromyographic feature data, determining that the sleep data are waking stage data; if the electromyographic signals in the potential waking-state data are smaller than the electromyographic characteristic data, determining values of correlation coefficients corresponding to the electromyographic signals, if the correlation coefficients are larger than a preset threshold value, setting values of body motion signals corresponding to the correlation coefficients larger than the preset threshold value to be 0, judging whether the body motion signals are smaller than the body motion characteristic data, if the body motion signals are smaller than the body motion characteristic data, determining that the sleep data are sleep stage data, and if not, determining that the sleep data are waking-state stage data;
if the electromyographic signals in the potential sleep-waking data are smaller than a first reference value, determining that the sleep data are sleep stage data; and if the electromyographic signals in the potential sleep-wake data are larger than a second reference value, determining that the sleep data are wake-wake stage data.
8. An apparatus for sleep staging, the apparatus comprising:
the first module is used for acquiring a myoelectric signal and a body movement signal of a user;
the second module is used for acquiring baseline parameters according to the electromyographic signals and the body movement signals; the baseline parameter is used for representing the signal characteristics of the electromyographic signal and the body movement signal;
a third module for determining a sleep stage of the user based on the baseline parameters;
a fourth module for determining a rapid eye movement REM period according to the sleep data of the sleep stage;
wherein the apparatus further comprises a fifth module,
the fifth module is used for classifying the sleep data of the sleep stage of the user according to the body movement signal to acquire a first type of the sleep data; the sleep data comprises body movement data and myoelectric data; the first type of sleep data comprises: potential sleep data, potential wakefulness data, and potential sleep-wakefulness data; comparing the magnitude relation between the value of the electromyographic data corresponding to the first type of each sleep data and the value of the electromyographic feature data to obtain a comparison result; and correcting the first type of each sleep data according to the comparison result, the filtered electromyographic signals and the filtered body movement signals to obtain a second type of each sleep data after correction.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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