CN113974575B - Sleep staging method and device, electronic equipment and storage medium - Google Patents
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4812—Detecting sleep stages or cycles
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4815—Sleep quality
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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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
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
Abstract
The embodiment of the invention relates to a sleep stage method, a sleep stage device, electronic equipment and a storage medium, which comprise the following steps: acquiring a physiological data sequence of a target user in a sleeping process, wherein the physiological data sequence is acquired by a data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process; determining a physiological energy sequence of the target user in the sleeping process according to the physiological data sequence, wherein the physiological energy sequence comprises physiological energy of the target user in a plurality of preset time periods; and carrying out sleep staging on the sleep process of the target user according to the physiological energy sequence. Therefore, the physiological energy can be determined according to the physiological data of the user, and the sleep stage of the user can be estimated according to the physiological energy of the user.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a sleep stage method, a sleep stage device, electronic equipment and a storage medium.
Background
With the development of economy and the improvement of living standard of people, people are paying more attention to health status. Among them, sleep monitoring is an important scenario in the field of health monitoring.
In the prior art, a pressure sensing measurement mode is mainly adopted to measure physiological data of a user in the sleeping process, for example, a sleep monitor can periodically measure the user so as to realize sleep monitoring.
However, in practical application, it cannot be guaranteed that physiological data of a user in a sleep process can be accurately obtained at each acquisition time, so that sleep monitoring cannot be accurately performed.
Disclosure of Invention
In view of this, in order to solve the above-mentioned technical problem that physiological data of a user in a sleep process cannot be obtained accurately at each acquisition time, and thus sleep monitoring cannot be performed accurately, the embodiment of the invention provides a sleep staging method, a sleep staging device, an electronic device and a storage medium.
In a first aspect, an embodiment of the present invention provides a sleep stage method, including:
acquiring a physiological data sequence of a target user in a sleeping process, wherein the physiological data sequence is acquired by a data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process;
determining a physiological energy sequence of the target user in the sleeping process according to the physiological data sequence, wherein the physiological energy sequence comprises physiological energy of the target user in a plurality of preset time periods;
And carrying out sleep staging on the sleep process of the target user according to the physiological energy sequence.
In a possible embodiment, the determining the physiological energy sequence of the target user during sleep according to the physiological data sequence includes:
for each group of physiological data in the physiological data sequence, one or more of the following operations are executed to obtain a physiological energy sequence of the target user in the sleeping process:
determining the heartbeat energy of the target user in a corresponding preset period according to the heartbeat data in the physiological data;
determining the respiratory energy of the target user in a corresponding preset period according to the respiratory data in the physiological data;
determining the body energy of the target user in a corresponding preset period according to the body movement data in the physiological data;
and determining the physiological energy of the target user in a corresponding preset period according to the determined heartbeat energy, and/or the respiratory energy and/or the physical energy.
In a possible embodiment, the method further comprises:
determining effective acquisition time lengths of the data acquisition module in the preset time periods according to the physiological data sequence;
The sleep stage for the sleep process of the target user according to the physiological energy sequence comprises the following steps:
dividing the physiological energy by the effective acquisition duration of the target user in a corresponding preset period of time for each physiological energy in the physiological energy sequence to obtain the effective average physiological energy of the target user in the corresponding preset period of time;
and carrying out sleep stage on the sleep process of the target user according to the effective average physiological energy of the target user in the preset time periods.
In a possible implementation manner, the determining, according to the physiological data sequence, an effective acquisition duration of the data acquisition module in the plurality of preset time periods includes:
for each group of physiological data in the physiological data sequence, one or more of the following operations are executed to obtain effective acquisition duration of the data acquisition module in the preset time periods:
determining the heartbeat acquisition time length of the data acquisition module in a corresponding preset period according to the heartbeat data in the physiological data;
determining the breath collection duration of the data collection module in a corresponding preset period according to the breath data in the physiological data;
Determining the body movement acquisition duration of the data acquisition module in a corresponding preset period from the physiological data;
and determining the effective acquisition time length of the data acquisition module in a corresponding preset time period according to the determined heartbeat acquisition time length, and/or the breath acquisition time length and/or the body movement acquisition time length.
In a possible implementation manner, the sleep stage of the sleep process of the target user according to the effective average physiological energy of the target user in the preset time periods includes:
denoising the effective average physiological energy sequences in the preset time periods by using a preset average smoothing algorithm to obtain denoised average physiological energy data;
and carrying out sleep stage on the sleep process of the target user according to the denoised average physiological energy data.
In a possible implementation manner, the sleep stage of the sleep process of the target user according to the denoised average physiological energy data includes:
clustering the denoised average physiological energy data by using a preset clustering algorithm to obtain a preset number of clusters;
Average value calculation is carried out on the average physiological energy data in each class cluster, and the physiological energy data average value corresponding to each class cluster is determined;
and determining the sleep period of the target user in the sleep process based on the physiological energy data average value corresponding to each class cluster.
In a possible implementation manner, the determining a sleep period of the target user during sleep based on the physiological energy data average value corresponding to each cluster includes:
determining a sleep time period corresponding to a first cluster as a rapid eye movement sleep REM period, wherein the first cluster is a cluster with the largest physiological energy data mean value in the corresponding clusters;
determining a sleep time period corresponding to a second class cluster as a deep sleep period, wherein the second class cluster is a class cluster with the largest physiological energy data average value in the corresponding class clusters;
and determining the time periods corresponding to the other clusters except the first cluster and the second cluster as shallow sleep periods.
In a second aspect, an embodiment of the present invention provides a sleep session device, including:
the data acquisition module is used for acquiring a physiological data sequence of a target user in the sleeping process, wherein the physiological data sequence is acquired by the data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process;
An energy determining module, configured to determine a physiological energy sequence of the target user during sleep according to the physiological data sequence, where the physiological energy sequence includes physiological energy of the target user in the plurality of preset time periods;
and the sleep stage module is used for carrying out sleep stage on the sleep process of the target user according to the physiological energy sequence.
In a possible embodiment, the energy determining module includes:
the first execution sub-module is used for executing one or more of the following operations aiming at each group of physiological data in the physiological data sequence to obtain a physiological energy sequence of the target user in the sleeping process:
determining the heartbeat energy of the target user in a corresponding preset period according to the heartbeat data in the physiological data;
determining the respiratory energy of the target user in a corresponding preset period according to the respiratory data in the physiological data;
determining the body energy of the target user in a corresponding preset period according to the body movement data in the physiological data;
and the energy determination submodule is used for determining the physiological energy of the target user in a corresponding preset period according to the determined heartbeat energy, the breathing energy and/or the body energy.
In a possible embodiment, the apparatus further comprises:
the duration determining module is used for determining effective acquisition duration of the data acquisition module in the preset time periods according to the physiological data sequence;
the sleep stage module comprises:
a first determining submodule, configured to divide, for each physiological energy in the sequence of physiological energies, the physiological energy by an effective acquisition duration of the target user in a corresponding preset period, so as to obtain an effective average physiological energy of the target user in the corresponding preset period;
and the sleep stage sub-module is used for carrying out sleep stage on the sleep process of the target user according to the effective average physiological energy of the target user in the preset time periods.
In a possible implementation manner, the duration determining module includes:
the second execution sub-module is used for executing one or more of the following operations for each group of physiological data in the physiological data sequence to obtain effective acquisition duration of the data acquisition module in the preset time periods:
determining the heartbeat acquisition time length of the data acquisition module in a corresponding preset period according to the heartbeat data in the physiological data;
Determining the breath collection duration of the data collection module in a corresponding preset period according to the breath data in the physiological data;
determining the body movement acquisition duration of the data acquisition module in a corresponding preset period from the physiological data;
the time length sub-determining module is used for determining the effective acquisition time length of the data acquisition module in a corresponding preset time period according to the determined heartbeat acquisition time length, and/or the breath acquisition time length and/or the body movement acquisition time length.
In a possible implementation manner, the sleep stage submodule is specifically configured to:
denoising the effective average physiological energy sequences in the preset time periods by using a preset average smoothing algorithm to obtain denoised average physiological energy data;
and carrying out sleep stage on the sleep process of the target user according to the denoised average physiological energy data.
In a possible implementation manner, according to the denoised average physiological energy data, sleep stages are performed on the sleep process of the target user, and the sleep stage is specifically used for:
clustering the denoised average physiological energy data by using a preset clustering algorithm to obtain a preset number of clusters;
Average value calculation is carried out on the average physiological energy data in each class cluster, and the physiological energy data average value corresponding to each class cluster is determined;
and determining the sleep period of the target user in the sleep process based on the physiological energy data average value corresponding to each class cluster.
In a possible implementation manner, the determining, based on the physiological energy data average value corresponding to each cluster, a sleep period of the target user during sleep is specifically used for:
determining a sleep time period corresponding to a first cluster as a rapid eye movement sleep REM period, wherein the first cluster is a cluster with the largest physiological energy data mean value in the corresponding clusters;
determining a sleep time period corresponding to a second class cluster as a deep sleep period, wherein the second class cluster is a class cluster with the largest physiological energy data average value in the corresponding class clusters;
and determining the time periods corresponding to the other clusters except the first cluster and the second cluster as shallow sleep periods.
In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor and a memory, the processor being configured to execute a sleep staging program stored in the memory to implement the sleep staging method of any one of the first aspects.
In a fourth aspect, embodiments of the present invention provide a storage medium storing one or more programs executable by one or more processors to implement the sleep staging method of any one of the first aspects.
According to the technical scheme provided by the embodiment of the invention, the physiological data sequence of the target user in the sleeping process is acquired, the physiological data sequence is acquired by the data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process, then, the physiological energy sequence of the target user in the sleeping process is determined according to the physiological data sequence, the physiological energy sequence comprises the physiological energy of the target user in the plurality of preset time periods, and the sleeping process of the target user is subjected to sleeping stage according to the physiological energy sequence. Therefore, under the condition that the data acquisition module cannot accurately acquire the physiological data of the target user in the sleeping process at each moment, the corresponding physiological energy sequence is calculated according to the acquired physiological data sequence, and sleeping stage of the sleeping process of the target user is realized.
Drawings
FIG. 1 is a flowchart of an embodiment of a sleep stage method according to an embodiment of the present invention;
FIG. 2 is an example of a data acquisition module acquiring a sequence of physiological data of a target user during sleep;
FIG. 3 is a flowchart of another embodiment of a sleep stage method according to an embodiment of the present invention;
fig. 4 is a block diagram of an embodiment of a sleep stage device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The sleep stage method provided by the invention is further explained with reference to the drawings in the following by using specific embodiments, which are not limiting examples of the invention.
Referring to fig. 1, a flowchart of an embodiment of a sleep stage method is provided in an embodiment of the present invention. As one embodiment, the process may be applied to electronic devices, including but not limited to servers, terminal devices (e.g., smartphones, tablets, etc.), and the like. As shown in fig. 1, the process may include the steps of:
s101, acquiring a physiological data sequence of a target user in the sleeping process, wherein the physiological data sequence is acquired by a data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process.
The target user refers to a user who is currently waiting to sleep stage the sleeping process.
The data acquisition module may be a wearable device worn by the target user, such as a smart bracelet, a smart watch, a sleep monitor, etc., or may be other devices, such as a smart mattress, a smart pillow, etc., which the present invention is not limited to.
The physiological data includes, but is not limited to: heartbeat data, respiration data, body movement data, wherein the heartbeat data includes, but is not limited to: heart beat count, heart rate, heart beat intensity; respiration data includes, but is not limited to: respiration count, respiration rate, respiration intensity; body movement data includes, but is not limited to: length of body movement, body movement intensity. It should be noted that, the physiological data herein refers to physiological data of the target user within a certain preset period, for example, the target user is about 23 on 14 days of 8 months: 13 to 8 months 14 days 23: physiological data within 15 of these two minutes.
In the example of the sleep monitor, since respiration and heartbeat of the user can cause slight vibration of the body of the user, a PVDF (Polyvinylidene fluoride ) pressure sensor in the sleep monitor can detect the slight vibration and convert the slight vibration into a voltage signal. Further, the sleep detector is able to separate the above exemplified physiological data from the acquired voltage signal.
In an embodiment, the data acquisition module may periodically acquire physiological data of the target user during the sleeping process according to a preset acquisition time interval, so as to obtain multiple groups of physiological data of the target user during the sleeping process, and a sequence formed by the multiple groups of physiological data according to the sequence of the acquisition time is called a physiological data sequence. That is, the physiological data sequence includes physiological data of the target user over a plurality of preset periods of time during sleep. It can be understood that the duration of the preset period is the acquisition time interval of the data acquisition module.
As shown in fig. 2, one example of a sequence of physiological data of a target user during sleep is acquired by a data acquisition module. Wherein, each row in the table shown in fig. 2 represents a set of physiological data of the target user during a certain preset period of time during sleep. For example, line 1 represents 23 of the target user during sleep: 09:41 to 23:11:41, line 2 represents 23 of the target user during sleep: 11:41 to 23:13:41 for these two minutes, and so on, line 7 represents 23 of the target user during sleep: 21:39 to 23:23:38 for these two minutes.
Further, the data acquisition module can send the acquired physiological data sequence of the target user in the sleeping process to the electronic equipment, so that the electronic equipment can acquire the physiological data sequence of the target user in the sleeping process acquired by the data acquisition module.
S102, determining a physiological energy sequence of the target user in the sleeping process according to the physiological data sequence, wherein the physiological energy sequence comprises physiological energy of the target user in a plurality of preset time periods.
In one embodiment, determining the physiological energy sequence of the target user during sleep according to the physiological data sequence of the target user during sleep may include: for each group of physiological data in the physiological data sequence, one or more of the following operations are performed to obtain the physiological energy of the target user in a plurality of preset time periods: (I) Determining the heartbeat energy of the target user in a corresponding preset period according to the heartbeat data in the physiological data; (II) determining the respiratory energy of the target user within a corresponding preset period of time from the respiratory data in the physiological data; and (III) determining the body energy of the target user in a corresponding preset period according to the body movement data in the physiological data. And then, determining the physiological energy of the target user in a corresponding preset period according to the determined heartbeat energy, and/or respiratory energy and/or physical energy.
The specific implementation of determining the heartbeat energy of the target user in the corresponding preset period according to the heartbeat data in the physiological data may be: and substituting the heart rate, heart rate count and heart rate intensity in the physiological data as parameters into a heart rate energy calculation formula exemplified by the following formula (I) to obtain the heart rate energy of the target user in a corresponding preset period.
Wherein h is E For the heartbeat energy of the target user in the corresponding preset period, h r Is heart rate, f s Sampling rate, h, set for data acquisition module c For heart rate count, h s Is heart rate intensity.
Similarly, the specific implementation of determining the breathing energy of the target user in the corresponding preset period according to the breathing data in the physiological data may be: substituting the respiration rate, respiration count and respiration intensity in the physiological data as parameters into a respiration energy calculation formula exemplified by the following formula (II) to obtain the respiration energy of the target user in a corresponding preset period:
wherein b E For the respiratory energy of the target user in a corresponding preset period of time b r For respiration rate b c For respiration counting, b s Is the respiration intensity.
Similarly, the specific implementation of determining the body energy of the target user in the corresponding preset period according to the body movement data in the physiological data may be: substituting the body movement duration and the body movement intensity in the physiological data as parameters into a body movement energy calculation formula exemplified by the following formula (III) to obtain the body movement energy of the target user in a corresponding preset period:
t E ＝t t ×f s ×t s Formula (III)
Wherein t is E For the physical energy of the target user in a corresponding preset period of time, t t Length of body movement, t s Is body movement intensity.
Further, taking the example of determining the physiological energy of the target user in the corresponding preset time period according to the determined heartbeat energy, respiratory energy and body energy, the following formula (four) can be used for determining the physiological energy S of the target user in the corresponding preset time period according to the determined heartbeat energy, respiratory energy and body energy E ：
S E ＝h E +b E +t E Formula (IV)
It should be noted that, determining the physiological energy of the target user in the corresponding preset period according to the determined heartbeat energy, respiratory energy and body energy is merely an example. In practical applications, the physiological energy of the target user in the corresponding preset period may be determined according to any one or both of the heartbeat energy, the respiratory energy and the physical energy, which is not limited by the embodiment of the present invention.
Of course, compared with determining the physiological energy of the target user in the corresponding preset period according to any one or both of the heartbeat energy, the respiratory energy and the physical energy, determining the physiological energy of the target user in the corresponding preset period according to the three, so that the determined physiological energy contains information with multiple dimensions, the sleep state of the target user can be reflected more accurately, and the accuracy of the sleep stage result of the sleep process of the target user based on the sleep energy of the target user in the sleep process is improved.
By the processing mode, the heartbeat energy, the breathing energy and the body energy of the target user in the corresponding preset time period can be determined according to each group of physiological data in the physiological data sequence, and further the physiological energy of the target user in the corresponding preset time period is determined.
S103, sleep stage is carried out on the sleep process of the target user according to the physiological energy sequence.
In an embodiment, since physiological data of the target user is not effectively collected at each collection time, sleep staging of the sleep process of the target user according to the physiological energy sequence may be achieved through the flow shown in fig. 3. As shown in fig. 3, the following steps may be included:
s301, determining effective acquisition time lengths of the data acquisition module in a plurality of preset time periods according to the physiological data sequence.
In an embodiment, determining the effective acquisition duration of the data acquisition module in a plurality of preset time periods according to the physiological data sequence comprises: for each group of physiological data in the physiological data sequence, one or more of the following operations are executed to obtain effective acquisition duration of the data acquisition module in a plurality of preset time periods: (I) Determining the heartbeat acquisition time length of the data acquisition module in a corresponding preset time period according to the heartbeat data in the physiological data; (II) determining the breath collection duration of the data collection module in a corresponding preset period according to the breath data in the physiological data; (III) determining the body movement acquisition duration of the data acquisition module in the corresponding preset period from the physiological data. And then, according to the determined heartbeat acquisition time length, and/or respiratory acquisition time length and/or body movement acquisition time length, determining the effective acquisition time length of the data acquisition module in the corresponding preset time period.
The specific implementation of determining the heartbeat acquisition time length of the data acquisition module in the corresponding preset time period according to the heartbeat data in the physiological data can be as follows: substituting heart rate and heart rate count in physiological data as parameters into a heart beat duration calculation formula exemplified by the following formula (five) to obtain a heart beat acquisition duration h of the data acquisition module in a corresponding preset period t ：
Similarly, rootThe specific implementation of determining the breath collection duration of the data collection module in the corresponding preset period according to the breath data in the physiological data can be as follows: substituting the respiration rate and the respiration rate in the physiological data as parameters into a respiration duration calculation formula exemplified by the following formula (six) to obtain a respiration acquisition duration b of the data acquisition module in a corresponding preset period t ：
Further, taking the example of determining the effective collection time length of the data collection module in the corresponding preset time period according to the determined heartbeat collection time length, breath collection time length and body movement collection time length, the effective collection time length of the data collection module in the corresponding preset time period can be determined according to the determined heartbeat collection time length, breath collection time length and body movement collection time length through the following formula (seven):
S s ＝h t +b t +t t Formula (seven)
Wherein S is s For effective acquisition duration of the data acquisition module in a corresponding preset period, t t The body movement time of the data acquisition module in the corresponding preset time period is set.
It should be noted that, the above-mentioned determining the effective collection duration of the data collection module in the corresponding preset period according to the determined heartbeat collection duration, respiratory collection duration and body movement collection duration is only an example. In practical application, the effective acquisition duration of the data acquisition module in the corresponding preset period can be determined according to any one or any two of the heartbeat acquisition duration, the respiratory acquisition duration and the body movement acquisition duration, and the embodiment of the invention is not limited to this.
Of course, compared with determining the effective acquisition duration of the data acquisition module in the corresponding preset time period according to any one or any two of the heartbeat acquisition duration, the respiratory acquisition duration and the body movement acquisition duration, the effective acquisition duration of the data acquisition module in the corresponding preset time period is determined according to the three, so that the determined effective acquisition duration contains information of multiple dimensions, the sleeping state of the target user can be reflected more accurately, and the accuracy of the sleeping stage result of the sleeping process of the target user is improved.
S302, dividing the physiological energy by the effective acquisition time length of the target user in the corresponding preset time period for each physiological energy in the physiological energy sequence to obtain the effective average physiological energy of the target user in the corresponding preset time period.
S303, sleep stage is carried out on the sleep process of the target user according to the effective average physiological energy of the target user in a plurality of preset time periods.
S302 and S303 are collectively described below:
in an embodiment, for each physiological energy in the physiological energy sequence of the target user during sleep, the effective collection duration of the physiological energy and the target user in the corresponding preset period may be substituted as parameters into an effective average physiological energy calculation formula illustrated in the following formula (eight), to obtain an effective average physiological energy σ of the target user in the corresponding preset period:
in one embodiment, according to the effective average physiological energy of the target user in a plurality of preset time periods, the specific implementation of sleep staging of the sleep process of the target user may include: firstly, denoising an effective average physiological energy sequence in a plurality of preset time periods by using a preset average smoothing algorithm to obtain denoised average physiological energy data, and then carrying out sleep stage on the sleeping process of a target user according to the denoised average physiological energy data. Through the processing, noise such as data with larger phase difference and data with abrupt turning in the data can be removed or reduced, and accuracy of sleep stage results of the sleep process of the target user based on the sleep energy of the target user in the sleep process is improved.
Specifically, the preset mean smoothing algorithm may include: the m-point is smoothed. The m-point smoothing algorithm refers to: setting a window with m-point length, sequentially passing an effective average physiological energy sequence in a plurality of preset time periods through the window as a queue, calculating the average value of m effective average physiological energies in the window each time, sequentially placing the average physiological energy data obtained by each calculation into the tail of the queue, discarding the effective average physiological energy at the head of the queue in the original data queue, and finally obtaining the denoised average physiological energy data. It will be appreciated that m is less than the length of the effective average sequence of physiological energy of the target user over a plurality of preset time periods. For example, if it is determined that the length of the effective average sequence of physiological energy of the target user over a plurality of preset time periods is 25, then m <25.
For example, assuming that the effective average physiological energy sequence in a plurality of preset time periods is 1-11, substituting the effective average physiological energy sequence 1-11 as a parameter into a preset 3-point smoothing algorithm, that is, setting a window with a length of 3, sequentially entering the effective average physiological energy sequence 1-11 as a queue into the window, when the window is filled for the first time, calculating the average value of the effective average physiological energy sequences (1, 2, 3) in the window as a, putting the average value a into the tail of the queue, and discarding the first data 1 in the queue, wherein the queue is 2-11, a; similarly, when the window is filled for the second time, an effective average physiological energy sequence (2, 3, 4) exists in the window, the average value of the effective average physiological energy sequence (2, 3, 4) in the window is calculated as b, the average value b is put into the tail of the queue, the data 2 of the head of the queue is discarded, and at the moment, the queue is 3-11, a and b; similarly, when the window is filled for the last time, an effective average physiological energy sequence (9, 10, 11) exists in the window, the average value of the effective average physiological energy sequence (9, 10, 11) in the window is calculated as i, the average value i is put into the tail of the queue, the data 9 at the head of the queue are discarded, at the moment, the queues are 10, 11 and a-i, 2 initial effective average physiological energies exist in the queue, and the condition of 3-point smooth calculation is not met, so that the rest effective average physiological energies can be discarded, and the effective average physiological energies a-i after denoising are finally obtained.
It should be clear that in the denoising process of the sequence of valid average physiological energies in a plurality of preset time periods by using the mean smoothing algorithm, the previous valid average physiological energy or energies in the sequence exist and/or the last valid average physiological energy or energies cannot be averaged by m-point smoothing, so that the valid average physiological energy is possibly discarded as noise. However, in consideration of the actual situation, the effective average physiological energy of the target user in a plurality of preset periods is sufficiently large, and therefore, the one or more effective average physiological energies that cannot be discarded by the mean smoothing algorithm do not have a great influence on the result of later sleep staging of the sleep process of the target user.
In this embodiment, according to the denoised average physiological energy data, the specific implementation of sleep staging for the sleep process of the target user may include: clustering the average physiological energy data after denoising the effective average physiological energy sequences in a plurality of preset time periods by using a preset clustering algorithm to obtain a preset number of class clusters, calculating the average physiological energy data in each class cluster to determine the corresponding physiological energy data average value of each class cluster, and determining the sleeping period of the target user in the sleeping process based on the corresponding physiological energy data average value of each class cluster.
The preset clustering algorithm may include any one of the following: k-means clustering algorithm (k-means clustering algorithm ), k-means clustering algorithm (k-medoids clustering algorithm, k-center point clustering algorithm), CLARA (Clustering LARge Applications, large application clustering) algorithm, and the like.
In one embodiment, since sleep intervals are generally defined as 3 periods: REM (Rapid Eye Movement, rapid eye movement sleep) period, light sleep period, and deep sleep period, therefore, the preset number may take a value of 3, that is, average physiological energy data obtained by denoising an effective average physiological energy sequence in a plurality of preset periods is divided into 3 clusters.
In this embodiment, determining, based on the physiological energy data average value corresponding to each cluster, a sleep period of the target user in the sleep process may include: the sleep time period corresponding to the first type of cluster is determined as REM period, the first type of cluster is the type cluster with the largest physiological energy data average value in the corresponding type of cluster, the sleep time period corresponding to the second type of cluster is determined as deep sleep period, the second type of cluster is the type cluster with the largest physiological energy data average value in the corresponding type of cluster, and the time periods corresponding to the other type of clusters except the first type of cluster and the second type of cluster are determined as shallow sleep period.
For example, the average physiological energy data 2-10 is clustered by using a k-means clustering algorithm, and the average physiological energy data can be divided into three clusters {2,3,4}, {5,6,7}, {8,9, 10}, and the physiological energy data means of the three clusters are calculated to correspond to 3, 6, 9, respectively. It can be determined that the average value 9 of the physiological energy data of the first cluster {8,9, 10} is the largest of the three clusters, and the sleep time corresponding to the average physiological energy data in the first cluster is 23: 11. 23: 41. 24:11, it may be determined that the sleep period corresponding to the first cluster is 23:11 to 24:11, determining the target user at 23:11 to 24: the 11 sleep period is in REM phase. Determining that the physiological energy data mean value 3 of the second class cluster {2,3,4} is the smallest of the three class clusters, and respectively 1 according to sleep time corresponding to average physiological energy data in the second class cluster: 11. 1: 41. 2:11, it may be determined that the sleep period corresponding to the second class cluster is 1: 11-2: 11, determining that the target user is at 1: 11-2: the sleep period 11 is in a deep sleep period. Determining sleep time corresponding to the average physiological energy data in the third class of clusters {5,6,7} to be 4: 11. 4: 41. 5:41, it may be determined that the sleep period corresponding to the third type cluster is 4:11 to 4:41 and 5:41, determining the target user at 4:11 to 4:41 sleep period and 5:41 are in a light sleep period.
Thus, the description of the flow shown in fig. 1 is completed.
As can be seen from the flow shown in fig. 1, in the technical scheme of the present invention, a physiological data sequence of a target user during sleep is acquired, where the physiological data sequence is acquired by a data acquisition module and includes physiological data of the target user during a plurality of preset periods during sleep, and then a physiological energy sequence of the target user during sleep is determined according to the physiological data sequence, where the physiological energy sequence includes physiological energy of the target user during a plurality of preset periods, and sleep stages are performed on the sleep process of the target user according to the physiological energy sequence. Therefore, under the condition that the data acquisition module cannot accurately acquire the physiological data of the target user in the sleeping process at each moment, the corresponding physiological energy sequence is calculated according to the acquired physiological data sequence, and sleeping stage of the sleeping process of the target user is realized.
The invention also provides an embodiment block diagram of the device corresponding to the embodiment of the sleep stage method.
Referring to fig. 4, a block diagram of an embodiment of a sleep stage device according to an embodiment of the present invention is provided. As shown in fig. 4, the apparatus includes:
The data acquisition module 401 is configured to acquire a physiological data sequence of a target user during sleep, where the physiological data sequence is acquired by the data acquisition module and includes physiological data of the target user during a plurality of preset periods of time during sleep;
an energy determination module 402, configured to determine a physiological energy sequence of the target user during sleep according to the physiological data sequence, where the physiological energy sequence includes physiological energy of the target user during the plurality of preset time periods;
and the sleep stage module 403 is configured to stage the sleep process of the target user according to the physiological energy sequence.
In a possible implementation, the energy determination module 402 includes (not shown in the figure):
the first execution sub-module is used for executing one or more of the following operations aiming at each group of physiological data in the physiological data sequence to obtain a physiological energy sequence of the target user in the sleeping process:
determining the heartbeat energy of the target user in a corresponding preset period according to the heartbeat data in the physiological data;
determining the respiratory energy of the target user in a corresponding preset period according to the respiratory data in the physiological data;
Determining the body energy of the target user in a corresponding preset period according to the body movement data in the physiological data;
and the energy determination submodule is used for determining the physiological energy of the target user in a corresponding preset period according to the determined heartbeat energy, the breathing energy and/or the body energy.
In a possible embodiment, the device further comprises (not shown in the figures):
the duration determining module is used for determining effective acquisition duration of the data acquisition module in the preset time periods according to the physiological data sequence;
the sleep stage module 403 includes:
a first determining submodule, configured to divide, for each physiological energy in the sequence of physiological energies, the physiological energy by an effective acquisition duration of the target user in a corresponding preset period, so as to obtain an effective average physiological energy of the target user in the corresponding preset period;
and the sleep stage sub-module is used for carrying out sleep stage on the sleep process of the target user according to the effective average physiological energy of the target user in the preset time periods.
In a possible implementation manner, the duration determining module includes (not shown in the figure):
The second execution sub-module is used for executing one or more of the following operations for each group of physiological data in the physiological data sequence to obtain effective acquisition duration of the data acquisition module in the preset time periods:
determining the heartbeat acquisition time length of the data acquisition module in a corresponding preset period according to the heartbeat data in the physiological data;
determining the breath collection duration of the data collection module in a corresponding preset period according to the breath data in the physiological data;
determining the body movement acquisition duration of the data acquisition module in a corresponding preset period from the physiological data;
the time length sub-determining module is used for determining the effective acquisition time length of the data acquisition module in a corresponding preset time period according to the determined heartbeat acquisition time length, and/or the breath acquisition time length and/or the body movement acquisition time length.
In a possible implementation manner, the sleep stage submodule is specifically configured to:
denoising the effective average physiological energy sequences in the preset time periods by using a preset average smoothing algorithm to obtain denoised average physiological energy data;
And carrying out sleep stage on the sleep process of the target user according to the denoised average physiological energy data.
In a possible implementation manner, according to the denoised average physiological energy data, sleep stages are performed on the sleep process of the target user, and the sleep stage is specifically used for:
clustering the denoised average physiological energy data by using a preset clustering algorithm to obtain a preset number of clusters;
average value calculation is carried out on the average physiological energy data in each class cluster, and the physiological energy data average value corresponding to each class cluster is determined;
and determining the sleep period of the target user in the sleep process based on the physiological energy data average value corresponding to each class cluster.
In a possible implementation manner, the determining, based on the physiological energy data average value corresponding to each cluster, a sleep period of the target user during sleep is specifically used for:
determining a sleep time period corresponding to a first cluster as a rapid eye movement sleep REM period, wherein the first cluster is a cluster with the largest physiological energy data mean value in the corresponding clusters;
determining a sleep time period corresponding to a second class cluster as a deep sleep period, wherein the second class cluster is a class cluster with the largest physiological energy data average value in the corresponding class clusters;
And determining the time periods corresponding to the other clusters except the first cluster and the second cluster as shallow sleep periods.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and an electronic device 500 shown in fig. 5 includes: at least one processor 501, memory 502, at least one network interface 504, and other user interfaces 503. The various components in the electronic device 500 are coupled together by a bus system 505. It is understood that bus system 505 is used to enable connected communications between these components. The bus system 505 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for clarity of illustration the various buses are labeled as bus system 505 in fig. 5.
The user interface 503 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, a trackball), a touch pad, or a touch screen, etc.
It will be appreciated that the memory 502 in embodiments of the invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a programmable Read-only memory (ProgrammableROM, PROM), an erasable programmable Read-only memory (ErasablePROM, EPROM), an electrically erasable programmable Read-only memory (ElectricallyEPROM, EEPROM), or a flash memory, among others. The volatile memory may be a random access memory (RandomAccessMemory, RAM) that acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic random access memory (DynamicRAM, DRAM), synchronous dynamic random access memory (SynchronousDRAM, SDRAM), double data rate synchronous dynamic random access memory (ddr SDRAM), enhanced Synchronous Dynamic Random Access Memory (ESDRAM), synchronous link dynamic random access memory (SynchlinkDRAM, SLDRAM), and direct memory bus random access memory (DirectRambusRAM, DRRAM). The memory 502 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some implementations, the memory 502 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 5021 and application programs 5022.
The operating system 5021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application 5022 includes various application programs such as a media player (MediaPlayer), a Browser (Browser), and the like for implementing various application services. A program for implementing the method according to the embodiment of the present invention may be included in the application 5022.
In the embodiment of the present invention, the processor 501 is configured to execute the method steps provided by the method embodiments by calling a program or an instruction stored in the memory 502, specifically, a program or an instruction stored in the application 5022, for example, including:
acquiring a physiological data sequence of a target user in a sleeping process, wherein the physiological data sequence is acquired by a data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process;
determining a physiological energy sequence of the target user in the sleeping process according to the physiological data sequence, wherein the physiological energy sequence comprises physiological energy of the target user in a plurality of preset time periods;
And carrying out sleep staging on the sleep process of the target user according to the physiological energy sequence.
The method disclosed in the above embodiment of the present invention may be applied to the processor 501 or implemented by the processor 501. The processor 501 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in the processor 501. The processor 501 may be a general purpose processor, a digital signal processor (DigitalSignalProcessor, DSP), an application specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), an off-the-shelf programmable gate array (FieldProgrammableGateArray, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software elements in a decoding processor. The software elements may be located in a random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 502, and the processor 501 reads information in the memory 502 and, in combination with its hardware, performs the steps of the method described above.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (ApplicationSpecificIntegratedCircuits, ASIC), digital signal processors (DigitalSignalProcessing, DSP), digital signal processing devices (dspev), programmable logic devices (ProgrammableLogicDevice, PLD), field programmable gate arrays (Field-ProgrammableGateArray, FPGA), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
The electronic device provided in this embodiment may be an electronic device as shown in fig. 5, and may perform all steps of the sleep stage method as shown in fig. 1 and 3, so as to achieve the technical effects of the sleep stage method as shown in fig. 1 and 3, and detailed descriptions with reference to fig. 1 and 3 are omitted herein for brevity.
The embodiment of the invention also provides a storage medium (computer readable storage medium). The storage medium here stores one or more programs. Wherein the storage medium may comprise volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid state disk; the memory may also comprise a combination of the above types of memories.
When one or more programs in the storage medium are executable by one or more processors, the sleep stage method executed on the electronic device side is implemented.
The processor is configured to execute a sleep stage program stored in the memory, so as to implement the following steps of a sleep stage method executed on the electronic device side:
acquiring a physiological data sequence of a target user in a sleeping process, wherein the physiological data sequence is acquired by a data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process;
determining a physiological energy sequence of the target user in the sleeping process according to the physiological data sequence, wherein the physiological energy sequence comprises physiological energy of the target user in a plurality of preset time periods;
And carrying out sleep staging on the sleep process of the target user according to the physiological energy sequence.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of function in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. A sleep staging method, comprising:
acquiring a physiological data sequence of a target user in a sleeping process, wherein the physiological data sequence is acquired by a data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process;
determining a physiological energy sequence of the target user in the sleeping process according to the physiological data sequence, wherein the physiological energy sequence comprises physiological energy of the target user in the preset time periods, the physiological energy comprises heartbeat energy, and the heartbeat energy is obtained by substituting the heart rate, the heartbeat count and the heartbeat intensity in the physiological data as parameters into a heartbeat energy calculation formula;
According to the physiological data sequence, determining effective acquisition time lengths of the data acquisition module in a plurality of preset time periods;
sleep staging a sleep process of the target user according to the physiological energy sequence, comprising: dividing the physiological energy by the effective acquisition duration of the target user in a corresponding preset period of time for each physiological energy in the physiological energy sequence to obtain the effective average physiological energy of the target user in the corresponding preset period of time; according to the effective average physiological energy of the target user in the preset time periods, carrying out sleep stage on the sleep process of the target user, wherein the sleep stage is to carry out denoising treatment on the effective average physiological energy in the preset time periods by using a preset average smoothing algorithm to obtain denoised average physiological energy data, and further to cluster the denoised average physiological energy data by using a preset clustering algorithm to obtain a preset number of class clusters, carrying out average calculation on the average physiological energy data in each class cluster, determining a corresponding physiological energy data average value, and determining the sleep stage of the sleep process of the target user based on the physiological energy data average value corresponding to each class cluster so as to realize sleep stage on the sleep process.
2. The method of claim 1, wherein said determining a sequence of physiological energy of the target user during sleep from the sequence of physiological data comprises:
for each group of physiological data in the physiological data sequence, one or more of the following operations are executed to obtain a physiological energy sequence of the target user in the sleeping process:
determining the heartbeat energy of the target user in a corresponding preset period according to the heartbeat data in the physiological data;
determining the respiratory energy of the target user in a corresponding preset period according to the respiratory data in the physiological data;
determining the body energy of the target user in a corresponding preset period according to the body movement data in the physiological data;
and determining the physiological energy of the target user in a corresponding preset period according to the determined heartbeat energy, and/or the respiratory energy and/or the physical energy.
3. The method of claim 1, wherein said determining the effective acquisition duration of the data acquisition module over the plurality of preset time periods from the physiological data sequence comprises:
for each group of physiological data in the physiological data sequence, one or more of the following operations are executed to obtain effective acquisition duration of the data acquisition module in the preset time periods:
Determining the heartbeat acquisition time length of the data acquisition module in a corresponding preset period according to the heartbeat data in the physiological data;
determining the breath collection duration of the data collection module in a corresponding preset period according to the breath data in the physiological data;
determining the body movement acquisition duration of the data acquisition module in a corresponding preset period from the physiological data;
and determining the effective acquisition time length of the data acquisition module in a corresponding preset time period according to the determined heartbeat acquisition time length, and/or the breath acquisition time length and/or the body movement acquisition time length.
4. The method of claim 1, wherein said sleep staging the sleep process of the target user based on the effective average physiological energy of the target user over the plurality of preset time periods comprises:
denoising the effective average physiological energy sequences in the preset time periods by using a preset average smoothing algorithm to obtain denoised average physiological energy data;
and carrying out sleep stage on the sleep process of the target user according to the denoised average physiological energy data.
5. The method of claim 4, wherein said sleep staging the sleep process of the target user based on the denoised average physiological energy data comprises:
clustering the denoised average physiological energy data by using a preset clustering algorithm to obtain a preset number of clusters;
average value calculation is carried out on the average physiological energy data in each class cluster, and the physiological energy data average value corresponding to each class cluster is determined;
and determining the sleep period of the target user in the sleep process based on the physiological energy data average value corresponding to each class cluster.
6. The method of claim 5, wherein determining a sleep period of the target user during sleep based on the physiological energy data mean value corresponding to each of the class clusters, comprises:
determining a sleep time period corresponding to a first cluster as a rapid eye movement sleep REM period, wherein the first cluster is a cluster with the largest physiological energy data mean value in the corresponding clusters;
determining a sleep time period corresponding to a second class cluster as a deep sleep period, wherein the second class cluster is a class cluster with the largest physiological energy data average value in the corresponding class clusters;
And determining the time periods corresponding to the other clusters except the first cluster and the second cluster as shallow sleep periods.
7. A sleep staging device, comprising:
the data acquisition module is used for acquiring a physiological data sequence of a target user in the sleeping process, wherein the physiological data sequence is acquired by the data acquisition module and comprises physiological data of the target user in a plurality of preset time periods in the sleeping process;
the energy determining module is used for determining a physiological energy sequence of the target user in the sleeping process according to the physiological data sequence, wherein the physiological energy sequence comprises physiological energy of the target user in the preset time periods, the physiological energy comprises heartbeat energy, and the heartbeat energy is obtained by substituting heart rate, heartbeat count and heartbeat intensity in the physiological data as parameters into a heartbeat energy calculation formula;
according to the physiological data sequence, determining effective acquisition time lengths of the data acquisition module in a plurality of preset time periods;
the sleep stage module is used for carrying out sleep stage on the sleep process of the target user according to the physiological energy sequence, and comprises the following steps: dividing the physiological energy by the effective acquisition duration of the target user in a corresponding preset period of time for each physiological energy in the physiological energy sequence to obtain the effective average physiological energy of the target user in the corresponding preset period of time; according to the effective average physiological energy of the target user in the preset time periods, carrying out sleep stage on the sleep process of the target user, wherein the sleep stage is to carry out denoising treatment on the effective average physiological energy in the preset time periods by using a preset average smoothing algorithm to obtain denoised average physiological energy data, and further to cluster the denoised average physiological energy data by using a preset clustering algorithm to obtain a preset number of class clusters, carrying out average calculation on the average physiological energy data in each class cluster, determining a corresponding physiological energy data average value, and determining the sleep stage of the sleep process of the target user based on the physiological energy data average value corresponding to each class cluster so as to realize sleep stage on the sleep process.
8. An electronic device, comprising: a processor and a memory, the processor being configured to execute a sleep staging program stored in the memory to implement the sleep staging method of any one of claims 1-6.
9. A storage medium storing one or more programs executable by one or more processors to implement the sleep staging method of any one of claims 1-6.
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