Method and device for identifying rapid eye movement in sleep period, and storage medium

Abstract

The present invention relates to the technical field of sleep staging, and in particular to a method and device for identifying a rapid eye movement in a sleep period, and a storage medium, aiming at solving the problem of existing sleep staging methods being unable to accurately identify the rapid eye movement. To achieve this objective, the method of the present invention comprises: dividing acquired user heart rate data of a sleep period to be monitored into user heart rate data of a plurality of segmented intervals, obtaining sleep heart rate data of each segmented interval on the basis of currently stored average baseline values, identifying a rapid eye movement proportion of each segmented interval on the basis of the obtained sleep heart rate data, and obtaining the rapid eye movements in the whole sleep period to be monitored, wherein initial values of the average baseline values are obtained on the basis of medical-grade sleep characteristic data. The solution provided by the present invention can effectively improve the accuracy of identifying rapid eye movements in sleep staging.

Description

Method, device and storage medium for identifying rapid eye movement period during sleep

[0001] This application claims priority to Chinese patent application No. 202311288282.5 filed on October 7, 2023, entitled “A method, device and storage medium for identifying rapid eye movement periods during sleep”. The entire contents of the above Chinese patent application are incorporated into this application by reference. Technical Field

[0002] The present invention relates to the technical field of sleep staging, and specifically provides a method, device and storage medium for identifying rapid eye movement (REM) sleep stages. Background Art

[0003] With the development of the economy and the improvement of people's living standards, people are paying more and more attention to their health status. Among them, sleep monitoring is a very important scenario in the field of health monitoring. In the current existing technology, common sleep monitoring devices include smart bracelets and smart watches, which can monitor daily sleep quality and effectively provide feedback on key information such as the physiological signals of the human body during sleep. Sleep staging is to divide the sleep process into different stages according to the different changes in the physiological signals of the human body during sleep. For example, the sleep period is divided into deep sleep, light sleep and rapid eye movement (REM). Accurate sleep staging results can help us understand the user's health and stress status, and also help professionals develop intervention measures to improve sleep.

[0004] In the existing technology, the body movement and heart rate signals fed back by the sleep monitoring device can usually be used to relatively accurately determine the light sleep and deep sleep stages in the sleep stage classification, but it cannot accurately identify the REM stage. Moreover, since there are individual differences among users of the sleep monitoring device, the sleep stage detection results of the existing technology also have the problem of large differences and inaccuracies.

[0005] Accordingly, the art needs a solution that can accurately identify the rapid eye movement period during sleep to solve the above problems.

[0006] Summary of the Invention

[0007] In order to overcome the above-mentioned defects, the present invention is proposed to provide a method, device and storage medium for identifying rapid eye movement (REM) periods during sleep, which solve or at least partially solve the technical problem of being unable to accurately identify REM periods.

[0008] In a first aspect, the present invention provides a method for identifying rapid eye movement sleep, comprising:

[0009] Acquire the user's heart rate data during the sleep period to be measured, and divide the user's heart rate data during the sleep period to be measured into user heart rate data of a plurality of segmented intervals;

[0010] Obtaining a currently stored average baseline value, and obtaining sleep heart rate data for each segmented interval based on the average baseline value and the user's heart rate data corresponding to each segmented interval;

[0011] The sleep heart rate data of each segmented interval is analyzed to obtain the proportion of rapid eye movement in each segmented interval;

[0012] The rapid eye movement period in the sleep period to be tested is identified based on the rapid eye movement period ratio of each segmented interval.

[0013] Preferably, the user heart rate data of the sleep period to be measured is specifically the user heart rate data of the preset sleep duration; the user heart rate data of the sleep period to be measured is divided into multiple segmented intervals, specifically: the segmented interval length and the number of segmented intervals are determined according to the preset sleep duration, and the user heart rate data is divided into multiple segmented intervals according to the segmented interval length and the number of segmented intervals.

[0014] The above-mentioned method of obtaining the sleeping heart rate data of each segmented interval based on the average baseline value and the user heart rate data corresponding to each segmented interval is specifically as follows: the user heart rate data corresponding to each segmented interval is smoothed, and the difference between the smoothed heart rate data and the average baseline value is calculated to obtain the sleeping heart rate data of each segmented interval.

[0015] In one technical solution of the above method, the currently stored average baseline value is an updated average baseline value;

[0016] The method also includes a step of updating the currently stored average baseline value, specifically as follows: based on the rapid eye movement proportion of each segmented interval obtained by the identification and a preset value range, adjusting the average baseline value corresponding to each segmented interval in a preset manner, and determining a new average baseline value based on the adjusted average baseline values ​​of each segmented interval, and using the new average baseline value to update the currently stored average baseline value.

[0017] The method of adjusting the average baseline value corresponding to each segmented interval according to a preset method based on the REM ratio of each segmented interval obtained by the identification and the preset value range is specifically as follows: for each segmented interval, if the REM ratio of the segmented interval exceeds the preset value range, the sleep heart rate data and the average baseline value of the segmented interval are re-determined according to a preset formula until the REM ratio of the segmented interval obtained based on the re-determined sleep heart rate data meets the preset value range, thereby obtaining the adjusted average baseline value corresponding to the segmented interval.

[0018] In one technical solution of the above method, the currently stored average baseline value is an initial value of the average baseline value obtained in combination with medical-grade sleep characteristic data;

[0019] The initial value of the average baseline value obtained by combining the medical-grade sleep characteristic data specifically includes:

[0020] Obtaining medical-grade sleep characteristic data and sample user heart rate data of equal data length, obtaining an average heart rate based on the sample user heart rate data, obtaining sample sleep heart rate data based on the average heart rate and the sample user heart rate data, dividing the sample sleep heart rate data and the medical-grade sleep characteristic data into the same number of segmented intervals, and obtaining sample sleep heart rate data and medical-grade sleep characteristic data corresponding to each segmented interval;

[0021] For each segmented interval, the rapid eye movement period ratio obtained based on the medical-grade sleep characteristic data is recorded as a first ratio value; the rapid eye movement period ratio obtained based on the sample sleep heart rate data is recorded as a second ratio value;

[0022] Based on the first proportion value and the second proportion value, the heart rate baseline value corresponding to each segment interval is adjusted in a preset manner, and the initial value of the average baseline value is determined according to the adjusted heart rate baseline value of each segment interval.

[0023] The heart rate baseline value corresponding to each segmented interval is adjusted in a preset manner based on the first proportion value and the second proportion value. Specifically, for each segmented interval, if the difference between the second proportion value and the first proportion value exceeds a preset value, the sleep heart rate data and the heart rate baseline value of the segmented interval are re-determined according to a preset formula until the difference between the second proportion value corresponding to the segmented interval obtained based on the re-determined sleep heart rate data and the first proportion value does not exceed the preset value, thereby obtaining the adjusted heart rate baseline value for the segmented interval.

[0024] Preferably, the above method further comprises the step of performing data continuity processing on the identified rapid eye movement period in the sleep period to be measured, specifically as follows:

[0025] If the non-rapid eye movement period between two adjacent rapid eye movement periods in the sleep period to be tested is less than a preset time length, the non-rapid eye movement period is re-identified as a rapid eye movement period.

[0026] In a second aspect, the present invention provides a control device comprising a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the method described in any one of the technical solutions of the method for identifying rapid eye movement periods during sleep.

[0027] In a third aspect, the present invention provides a computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the method described in any one of the technical solutions of the method for identifying rapid eye movement periods during sleep.

[0028] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0029] In implementing the technical solution of the present invention, the user heart rate data obtained during the sleep period to be measured is first divided into user heart rate data of multiple segmented intervals. The sleep heart rate data of each segmented interval can be obtained based on the stored average baseline value. The proportion of rapid eye movement periods in each segmented interval is identified based on the obtained sleep heart rate data, and then the rapid eye movement periods in the entire sleep period to be measured are obtained. The initial value of the average baseline value is obtained in combination with medical-grade sleep characteristic data. The rapid eye movement periods identified by the solution of the present invention can be closer to the sleep staging detection results of medical-grade monitoring equipment. Therefore, the solution of the present invention can effectively improve the accuracy of identifying rapid eye movement periods in sleep staging. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The disclosure of the present invention will be more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Furthermore, similar numbers in the drawings represent similar components, wherein:

[0031] FIG1 is a flow chart of a method for obtaining an initial value of an average baseline value by combining medical-grade sleep characteristic data according to one embodiment of the present invention;

[0032] FIG2 is a flow chart showing the main steps of a method for identifying rapid eye movement sleep phases according to an embodiment of the present invention;

[0033] FIG3 is a schematic diagram of a main structural block diagram of a rapid eye movement recognition device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] Some embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0035] In the description of the present invention, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, and the like. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "one" and "the" may also include the plural forms.

[0036] Here we first explain some terms involved in the present invention.

[0037] NREM: Non-rapid eye movement sleep, NREM is divided into deep sleep and light sleep.

[0038] REM: Rapid eye movement, which is more likely to occur in the late stage of sleep. The characteristics of REM are: twitching, convulsions, rapid movements, body numbness, rapid eye movements, etc.; from NREM to REM: the heart rate rises slowly (reaches the highest within 6 to 10 minutes) and remains high for a long time (greater than the heart rate during light sleep).

[0039] PSG: Polysomnography, mainly used in medical clinical practice to collect electrophysiological signals such as electrocardiogram, electroencephalogram, and electrooculogram for sleep staging research.

[0040] The current theory of sleep structure stages divides human sleep into REM (concentrated in the last few hours of sleep, more likely to occur in the late stages of sleep) and NREM. NREM is further divided into deep sleep and light sleep. Medically, the sleep stage structure is usually divided into stages 1, 2, 3, and 4, with the closer to stage 4, the closer to deep sleep, and the closer to stage 1, the closer to light sleep. After a person falls asleep, they first enter stage 1 sleep, followed by stages 2, 3, and 4, and then from deep sleep to light sleep and back again. After returning to stage 2 sleep, they usually enter rapid eye movement sleep, and then enter another sleep cycle, from light to deep and then from deep to light, interspersed with rapid eye movement sleep, and so on. There are about 4-5 cycles in a night, each cycle is about 90 minutes, with the first cycle being longer and the subsequent cycles gradually becoming shorter.

[0041] In one application scenario of the present invention, the identification of REM sleep stages uses a retrospective sleep staging method, that is, staging is performed after obtaining complete sleep monitoring data. The sleep staging identification based on historical data is more accurate, and the staging results can be used to display a sleep report to the user.

[0042] An embodiment of the present invention provides a method for identifying rapid eye movement sleep. This method first requires obtaining an initial average baseline value based on medical-grade sleep characteristic data and storing this initial value in a heart rate monitoring device. When a user first uses the heart rate monitoring device, the average baseline value obtained will be the pre-stored initial value. For example, the heart rate monitoring device may be a smartwatch.

[0043] Referring to FIG. 1 , FIG. 1 is a main step flow diagram of obtaining an initial value of an average baseline value by combining medical-grade sleep characteristic data provided by an embodiment of the present invention, as shown in FIG. 1 , mainly including the following steps S101 to S105 .

[0044] Step S101: Acquire medical-grade sleep characteristic data and sample user heart rate data of equal length;

[0045] In this embodiment, medical-grade sleep characteristic data refers to sleep characteristic data obtained by electrophysiological signals collected by medical-grade acquisition equipment such as polysomnography (PSG), and sample user heart rate data refers to historical user heart rate data obtained by monitoring using heart rate monitoring devices such as smart bracelets and watches.

[0046] For example, if the sleep data of the sleep duration is 7 hours and 30 minutes, and assuming that each hour corresponds to 200 lengths, the data length of the sleep data obtained is equivalent to 1500.

[0047] Step S102: obtaining an average heart rate according to the sample user's heart rate data, and obtaining sample sleeping heart rate data based on the average heart rate and the sample user's heart rate data;

[0048] In one embodiment, the sample heart rate data is first smoothed to obtain smoothed heart rate data, an average heart rate of the smoothed heart rate data is obtained, and the sample sleeping heart rate data is obtained based on the difference between the smoothed heart rate data and the average heart rate. The average heart rate can be calculated by averaging multiple smoothed heart rate data obtained.

[0049] For example: HRinSlp=HR1result1S-MeanHR; where HRinSlp represents sample sleeping heart rate data, HR1result1S represents smoothed heart rate data, and MeanHR represents the average heart rate.

[0050] In this embodiment, a heart rate baseline may be obtained based on the sample sleep heart rate data. There is no unique method for obtaining the heart rate baseline. For example, the heart rate baseline may be obtained by the following calculation method:

[0051] HRLevel=(sum(SortHR(1:round(LenHRinSlpPo / 2),1)) / (round(LenHRinSlpPo / 2)*3)); where HRLevel represents the heart rate baseline, LenHRinSlpPo represents the length of heart rate data greater than zero in the sleeping heart rate data, and SortHR represents the data after the values ​​greater than zero in the sleeping heart rate data are sorted in descending order.

[0052] Step S103: Dividing the sample sleep heart rate data and the medical-grade sleep characteristic data into the same number of segmented intervals to obtain sample sleep heart rate data and medical-grade sleep characteristic data corresponding to each segmented interval;

[0053] In this embodiment, the medical-grade sleep characteristic data can be divided into 4-5 segmented intervals based on the user's sleep duration and the changing patterns of the medical-grade sleep staging chart. The basis for dividing the corresponding sample sleep heart rate data is not limited, and for example, the sample sleep heart rate data can be divided according to equal length.

[0054] For example, the medical-grade sleep characteristic data is divided into 5 segmented intervals. Similarly, the sample sleep heart rate data is also divided into 5 segmented intervals. The first segmented interval of the medical-grade sleep characteristic data corresponds to the first segmented interval of the sample sleep heart rate data, and the second segmented interval of the medical-grade sleep characteristic data corresponds to the second segmented interval of the sample sleep heart rate data, and so on, to obtain sample sleep heart rate data and medical-grade sleep characteristic data that correspond one to one to each segmented interval.

[0055] Step S104: For each segmented interval, the rapid eye movement period ratio obtained based on the medical-grade sleep characteristic data is recorded as a first ratio value; and the rapid eye movement period ratio obtained based on the sample sleep heart rate data is recorded as a second ratio value;

[0056] In this embodiment, the method for obtaining the REM period proportion based on the medical-grade sleep characteristic data and the REM period proportion based on the sample sleep heart rate data can be directly obtained by using the existing sleep staging method based on sleep characteristic data, and is not limited here. For example, the REM period proportion of a sleep period can be directly obtained based on the sleep characteristic data and an existing sleep staging model.

[0057] For example, the rapid eye movement proportions obtained based on the medical-grade sleep characteristic data are 12%, 24%, 19%, 22% and 30% respectively, that is, the first proportion corresponding to the first segmented interval of the medical-grade sleep characteristic data is 12%, the first proportion corresponding to the second segmented interval is 24%, the first proportion corresponding to the third segmented interval is 19%, the first proportion corresponding to the fourth segmented interval is 22%, and the first proportion corresponding to the fifth segmented interval is 30%.

[0058] Step S105: Based on the first proportion value and the second proportion value, the heart rate baseline value corresponding to each segment interval is adjusted according to a preset method, and the initial value of the average baseline value is determined according to the adjusted heart rate baseline value of each segment interval.

[0059] In one embodiment, for each segmented interval, if the difference between the second ratio and the first ratio exceeds a preset value, the sleep heart rate data and the heart rate baseline value for that segmented interval are re-determined according to a preset formula until the difference between the second ratio corresponding to that segmented interval obtained based on the re-determined sleep heart rate data and the first ratio does not exceed the preset value, thereby obtaining an adjusted heart rate baseline value for that segmented interval. The preset value is greater than or equal to 0.

[0060] For example, the first proportion corresponding to the first segmented interval of the medical-grade sleep characteristic data is 12%, and the second proportion corresponding to the first segmented interval of the sample sleep heart rate data is 5%, that is, the second proportion is less than the first proportion. According to the preset formula: sleep heart rate data = HR1result1S-(MeanHR-HRLevel / 2*countMinus), and heart rate baseline value = MeanHR-HRLevel / 2*countMinus, the sleep heart rate data and heart rate baseline value are re-determined; wherein HR1result1S represents the smoothed heart rate data, MeanHR represents the average heart rate, HRLevel represents the heart rate baseline, and countMinus represents the count value of the number of adjustments. The count value is increased by one each time the count value is adjusted until the second proportion corresponding to the segmented interval obtained according to the re-determined sleep heart rate data is closest to the first proportion value, that is, the degree of proximity is determined according to the preset value, and the heart rate baseline value calculated using the formula during this adjustment process is determined as the heart rate baseline value of the adjusted first segmented interval. For example, the heart rate baseline value of the adjusted first segmented interval is equal to 59.2223.

[0061] For example, the first proportion corresponding to the second segmented interval of the medical-grade sleep characteristic data is 24%, and the second proportion of the second segmented interval of the sample sleep heart rate data is 35%, that is, the second proportion is greater than the first proportion. According to the preset formula: sleep heart rate data = HR1result1S-(MeanHR+HRLevel / 2*countMinus), and heart rate baseline value = MeanHR+HRLevel / 2*countMinus, the sleep heart rate data and the heart rate baseline value are re-determined until the second proportion corresponding to the segmented interval obtained according to the re-determined sleep heart rate data is closest to the first proportion, that is, the degree of proximity is determined according to the preset value, and the heart rate baseline value calculated by using the formula during this adjustment process is determined as the heart rate baseline value of the adjusted second segmented interval. For example, the heart rate baseline value of the adjusted second segmented interval is equal to 57.2223.

[0062] Similar to the above example, the heart rate baseline values ​​corresponding to the third, fourth, and fifth segmented intervals can be determined successively, and the initial value of the average baseline value can be determined based on the acquired heart rate baseline values ​​for the five segmented intervals. For example, the average of the acquired heart rate baseline values ​​for the five segmented intervals can be determined as the initial value, or a numerical sequence consisting of the acquired heart rate baseline values ​​for the five segmented intervals can be determined as the initial value.

[0063] Furthermore, it is also possible to obtain sample sleep heart rate data and medical-grade sleep characteristic data for multiple consecutive days, and obtain initial values ​​of multiple average baseline values ​​based on the above steps S101-step S105, and determine the final initial value to be stored based on the multiple initial values ​​obtained, for example, by taking the average of the multiple initial values ​​obtained to obtain the final initial value to be stored.

[0064] 2, which is a flow chart illustrating the main steps of a method for identifying rapid eye movement (REM) sleep according to an embodiment of the present invention. As shown in FIG2, the method for identifying REM sleep in the embodiment of the present invention mainly includes the following steps S201 to S204.

[0065] Step S201: obtaining the user's heart rate data during the sleep period to be measured, and dividing the user's heart rate data during the sleep period to be measured into user heart rate data of a plurality of segmented intervals;

[0066] In this embodiment, the user's heart rate data during the sleep period to be measured is specifically the user's heart rate data for a preset sleep duration. The segmented interval length and the number of segmented intervals can be determined based on the preset sleep duration, and the user's heart rate data is divided into multiple segmented intervals based on the segmented interval lengths and the number of segmented intervals.

[0067] In actual applications, when the sleep time is greater than or equal to 7 hours, it is usually divided into 1.5-hour segments. For example, if the sleep time is 7 hours, the number of segmented intervals corresponding to rounding is 5; when the sleep time is less than 7 hours, it is divided into 1.2-hour segments. For example, if the sleep time is 5 hours, the number of segmented intervals corresponding to rounding is 4.

[0068] It is understandable that the sleep duration can also be converted into data length for division. For example, if the preset sleep duration is 8 hours and the data length corresponds to 200 per hour, the total length of the user heart rate data obtained is 1600.

[0069] Step S202: obtaining a currently stored average baseline value, and obtaining sleep heart rate data for each segmented interval based on the average baseline value and the user's heart rate data corresponding to each segmented interval;

[0070] It is understandable that when a user uses the heart rate monitoring device for the first time, the currently stored average baseline value obtained is the initial value of the average baseline value determined according to the method shown in FIG1 .

[0071] Specifically, in this embodiment, the currently stored average baseline value may be a single value or a sequence of values. Obtaining the sleep heart rate data for each segmented interval based on the average baseline value and the user's heart rate data corresponding to each segmented interval specifically includes smoothing the user's heart rate data corresponding to each segmented interval, and performing a difference calculation between the smoothed heart rate data and the average baseline value to obtain the sleep heart rate data for each segmented interval.

[0072] It is understandable that if the average baseline value is a numerical value, a difference calculation is performed based on the smoothed heart rate data corresponding to each segmented interval and the numerical value to determine the sleeping heart rate data of each segmented interval.

[0073] If the average baseline value is a numerical sequence, the currently stored numerical sequence corresponding thereto can be obtained according to the number of segmented intervals. For example, if the number of segmented intervals is specifically 4, the currently stored average baseline value that can be obtained is [A1, A2, A3, A4]; if the number of segmented intervals is specifically 5, the currently stored average baseline value that can be obtained is [A1, A2, A3, A4, A5]. For each segmented interval, according to the correspondence between the segmented interval and the numerical sequence, the difference between the smoothed heart rate data corresponding to each segmented interval and the corresponding average baseline value is calculated to determine the sleep heart rate data of each segmented interval. For example, the smoothed heart rate data corresponding to the first segmented interval corresponds to the average baseline value A1, and so on.

[0074] Step S203: analyzing the sleep heart rate data of each segmented interval to obtain the rapid eye movement (REM) ratio of each segmented interval;

[0075] Specifically, in this embodiment, the rapid eye movement (REM) period ratio is obtained by analyzing the sleep heart rate data and the heart rate change rate and the long-term fluctuation state of the heart rate.

[0076] In one embodiment, existing sleep staging methods based on heart rate data can be directly used to obtain REM sleep data, thereby determining the REM proportion. For example, for sleep heart rate data, the REM proportion can be determined based on the length of data that conforms to a preset heart rate variation pattern and the total length of sleep heart rate data. Another example is to directly use sleep heart rate data as input data and utilize existing sleep staging models to output the REM period, which is not limited here.

[0077] In an application scenario of this embodiment, since according to objective laws, the reasonable range of the proportion of REM during the entire sleep period is 20% to 25%, after obtaining the REM proportion of each segmented interval based on the above step S203, it also includes judging whether the REM proportion of each segmented interval conforms to the above reasonable range. If not, the average baseline value can be adaptively adjusted to regain a REM proportion that conforms to the above reasonable range.

[0078] Accordingly, after step S203, the above-mentioned step also includes the step of updating the currently stored average baseline value, specifically as follows: based on the REM proportion of each segmented interval obtained by the above-mentioned identification and the preset value range, the average baseline value corresponding to each segmented interval is adjusted according to a preset method, and a new average baseline value is determined based on the adjusted average baseline value of each segmented interval, and the currently stored average baseline value is updated with the new average baseline value. For example, the preset value range is 20% to 25%. It is understood that, accordingly, the currently stored average baseline value obtained in the above-mentioned step S202 can be an updated average baseline value.

[0079] In a specific embodiment, for each segmented interval, if the rapid eye movement ratio of the segmented interval exceeds a preset value range, the sleep heart rate data and average baseline value of the segmented interval are re-determined according to a preset formula until the rapid eye movement ratio of the segmented interval obtained based on the re-determined sleep heart rate data meets the preset value range, and the adjusted average baseline value corresponding to the segmented interval is obtained.

[0080] For example, the preset formula is as follows: Sleeping heart rate data = HR1result1S - (MeanHR±HRLevel / 2*countMinus); Average baseline value = MeanHR - HRLevel / 2*countMinus;

[0081] HR1result1S represents the smoothed heart rate data, i.e., the smoothed heart rate data obtained in the above step S102, MeanHR represents the average heart rate, HRLevel represents the heart rate baseline, countMinus represents the count value of the number of adjustments, and the count value is incremented each time the adjustment is made. For details on the acquisition and calculation of the various data involved in the formula, please refer to the description of the corresponding steps in Figure 1.

[0082] Specifically, if the REM period ratio is higher than the upper limit of the preset value range, the sleeping heart rate data is re-determined according to the sleeping heart rate data = HR1result1S - (MeanHR + HRLevel / 2 * countMinus) during the adjustment process;

[0083] If the REM period ratio is lower than the lower limit of the set value range, the sleeping heart rate data is re-determined according to the sleeping heart rate data = HR1result1S-(MeanHR-HRLevel / 2*countMinus) during the adjustment process.

[0084] For example, if the rapid eye movement period proportion of a certain segmented interval is 60%, which exceeds the upper limit of the preset range value of 25%, the rapid eye movement period proportion can be optimized to less than or equal to 25% by adjusting the average baseline value, and the average baseline value corresponding to the optimized rapid eye movement period proportion is used as the new average baseline value to update the currently stored average baseline value.

[0085] Step S204: identifying and obtaining the rapid eye movement period in the sleep period to be tested based on the rapid eye movement period ratio of each segmented interval.

[0086] Specifically, in this embodiment, based on the proportion of rapid eye movement periods in each segmented interval, the data length corresponding to the rapid eye movement period in each segmented interval can be determined, and according to the data length corresponding to the rapid eye movement period in each segmented interval, the distribution of rapid eye movement periods in the entire sleep period to be tested can be identified.

[0087] Furthermore, after the above step S204, the step of performing data serialization processing on the identified rapid eye movement period in the sleep period to be measured may be further included, specifically as follows:

[0088] If the non-rapid eye movement period between two adjacent rapid eye movement periods in the sleep period to be tested is less than a preset time length, the non-rapid eye movement period is re-identified as a rapid eye movement period.

[0089] For example, the distribution of REM periods identified in the sleep period to be measured is as follows: 2 minutes of REM followed by 4 minutes of light sleep, followed by 5 minutes of REM, with the preset duration being 10 minutes. Through data continuity processing, the 4 minutes of light sleep between the two REM periods can be re-identified and classified as REM, resulting in a continuous 11-minute REM period. This processing method can reduce REM fragmentation in the sleep period to be measured, achieving data continuity.

[0090] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0091] Furthermore, the present invention also provides a rapid eye movement recognition device.

[0092] Referring to Figure 3, Figure 3 is a block diagram of the main structure of a REM identification device according to one embodiment of the present invention. As shown in Figure 3, the REM identification device in this embodiment of the present invention primarily includes a first acquisition module 301, a second acquisition module 302, an analysis module 303, and an identification module 304. In some embodiments, one or more of the first acquisition module 301, the second acquisition module 302, the analysis module 303, and the identification module 304 can be combined into a single module.

[0093] The first acquisition module 301 is configured to acquire the user's heart rate data during the sleep period to be measured, and divide the user's heart rate data during the sleep period to be measured into user heart rate data of multiple segmented intervals;

[0094] The second acquisition module 302 is configured to acquire the currently stored average baseline value, and obtain the sleep heart rate data of each segmented interval based on the average baseline value and the user's heart rate data corresponding to each segmented interval;

[0095] Analysis module 303: for analyzing the sleep heart rate data of each segmented interval to obtain the rapid eye movement period ratio of each segmented interval;

[0096] The identification module 304 is configured to identify the rapid eye movement period in the sleep period to be tested based on the rapid eye movement period ratio of each segmented interval.

[0097] For a description of the specific implementation functions of the above modules, please refer to the description of the embodiment corresponding to FIG2 .

[0098] The above-mentioned rapid eye movement period identification device is used to execute the embodiment of the method for identifying rapid eye movement periods during sleep shown in Figure 2. The technical principles, technical problems solved and technical effects produced by the two are similar. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the rapid eye movement period identification device can refer to the contents described in the embodiment of the method for identifying rapid eye movement periods during sleep, and will not be repeated here.

[0099] Those skilled in the art will appreciate that all or part of the processes in the method for implementing the above-mentioned embodiment of the present invention may also be accomplished by instructing the relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, it may implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal, and software distribution medium capable of carrying the computer program code. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0100] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the method for identifying rapid eye movement periods during sleep in the above-mentioned method embodiment. The processor can be configured to execute the program in the storage device, which includes but is not limited to a program for executing the method for identifying rapid eye movement periods during sleep in the above-mentioned method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device can be a control device device formed by various electronic devices.

[0101] Furthermore, the present invention also provides a sleep monitoring device, including the above-mentioned control device, and the sleep monitoring device can be a product such as a smart bracelet, a smart watch, etc.

[0102] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the method of identifying the rapid eye movement period during sleep in the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned method of identifying the rapid eye movement period during sleep. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-transitory computer-readable storage medium.

[0103] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present invention, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0104] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules does not cause the technical solution to deviate from the principles of the present invention. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of the present invention.

[0105] The relevant user personal information that may be involved in the various embodiments of this application is strictly in accordance with the requirements of laws and regulations, follows the principles of legality, legitimacy and necessity, and is processed based on the reasonable purposes of the business scenarios to process the personal information that the user actively provides during the use of the product / service or is generated due to the use of the product / service, as well as the personal information obtained with the user's authorization.

[0106] The user personal information processed by this application will vary depending on the specific product / service scenario and should be based on the specific scenario in which the user uses the product / service. This may involve the user's sleep-related information. This application will treat the user's personal information and its processing with a high degree of diligence.

[0107] This application attaches great importance to the security of user personal information and has taken reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0108] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for identifying rapid eye movement sleep, characterized in that: The method comprises: Acquire the user's heart rate data during the sleep period to be measured, and divide the user's heart rate data during the sleep period to be measured into user's heart rate data of multiple segmented intervals; Obtaining a currently stored average baseline value, and obtaining the sleep heart rate data of each segmented interval according to the average baseline value and the user heart rate data corresponding to each segmented interval; The sleep heart rate data of each segmented interval is analyzed to obtain the proportion of rapid eye movement period in each segmented interval; The rapid eye movement period in the sleep period to be tested is obtained by identifying the rapid eye movement period in each segmented interval.

2. The method according to claim 1, characterized in that: The user's heart rate data during the sleep period to be measured is specifically the user's heart rate data of a preset sleep duration; The method of dividing the user heart rate data of the sleep period to be tested into multiple segmented intervals of user heart rate data is specifically: determining the segmented interval length and the number of segmented intervals according to the preset sleep duration, and dividing the user heart rate data into multiple segmented intervals of user heart rate data according to the segmented interval length and the number of segmented intervals.

3. The method according to claim 1, characterized in that: The step of obtaining the sleeping heart rate data of each segmented interval according to the average baseline value and the user heart rate data corresponding to each segmented interval is specifically as follows: smoothing the user heart rate data corresponding to each segmented interval, and performing a difference calculation between the smoothed heart rate data and the average baseline value to obtain the sleeping heart rate data of each segmented interval.

4. The method according to claim 1, characterized in that: The currently stored average baseline value is an updated average baseline value; The method further includes the step of updating the currently stored average baseline value, specifically as follows: based on the REM period proportion of each segmented interval obtained by the identification and the preset value range, adjusting the average baseline value corresponding to each segmented interval in a preset manner, and updating the average baseline value of each segmented interval according to the adjusted REM period proportion of each segmented interval according to the preset value range ... preset value range. The average baseline value of the segmented interval is used to determine a new average baseline value, and the currently stored average baseline value is updated with the new average baseline value.

5. The method according to claim 4, characterized in that: The method of adjusting the average baseline value corresponding to each segmented interval in a preset manner based on the REM ratio of each segmented interval obtained by the identification and the preset value range is specifically as follows: for each segmented interval, if the REM ratio of the segmented interval exceeds the preset value range, the sleep heart rate data and the average baseline value of the segmented interval are re-determined according to a preset formula until the REM ratio of the segmented interval obtained according to the re-determined sleep heart rate data meets the preset value range, thereby obtaining the adjusted average baseline value corresponding to the segmented interval.

6. The method according to claim 1, characterized in that: The currently stored average baseline value is an initial value of the average baseline value obtained in combination with the medical-grade sleep characteristic data; The initial value of the average baseline value obtained by combining the medical-grade sleep characteristic data specifically includes: Obtain medical-grade sleep characteristic data and sample user heart rate data of equal data length, obtain a heart rate average value according to the sample user heart rate data, obtain sample sleep heart rate data based on the heart rate average value and the sample user heart rate data, divide the sample sleep heart rate data and the medical-grade sleep characteristic data according to the same number of segmented intervals, and obtain sample sleep heart rate data and medical-grade sleep characteristic data corresponding to each segmented interval one by one; For each segmented interval, the rapid eye movement period ratio obtained based on the medical-grade sleep characteristic data is recorded as a first ratio value; the rapid eye movement period ratio obtained based on the sample sleep heart rate data is recorded as a second ratio value; Based on the first proportion value and the second proportion value, the heart rate baseline value corresponding to each segment interval is adjusted in a preset manner, and the initial value of the average baseline value is determined according to the adjusted heart rate baseline value of each segment interval.

7. The method according to claim 6, characterized in that: The method of adjusting the heart rate baseline value corresponding to each segmented interval in a preset manner based on the first proportion value and the second proportion value is specifically as follows: for each segmented interval, if the difference between the second proportion value and the first proportion value exceeds a preset value, the sleep heart rate data and the heart rate baseline value of the segmented interval are re-determined according to a preset formula until the difference between the second proportion value corresponding to the segmented interval obtained according to the re-determined sleep heart rate data and the first proportion value does not exceed the preset value, thereby obtaining the adjusted heart rate baseline value of the segmented interval.

8. The method according to claim 1, characterized in that: The method further includes: a step of performing data continuity processing on the identified rapid eye movement period in the sleep period to be tested, which is specifically as follows: If the non-rapid eye movement period between two adjacent rapid eye movement periods in the sleep period to be tested is less than a preset time length, the non-rapid eye movement period is re-identified as a rapid eye movement period.

9. A control device, comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by the processor to execute the method for identifying rapid eye movement periods in sleep according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the method for identifying rapid eye movement periods in sleep according to any one of claims 1 to 8.

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