WO2019033787A1 - 一种睡眠管理方法、系统及终端设备 - Google Patents

一种睡眠管理方法、系统及终端设备 Download PDF

Info

Publication number
WO2019033787A1
WO2019033787A1 PCT/CN2018/084634 CN2018084634W WO2019033787A1 WO 2019033787 A1 WO2019033787 A1 WO 2019033787A1 CN 2018084634 W CN2018084634 W CN 2018084634W WO 2019033787 A1 WO2019033787 A1 WO 2019033787A1
Authority
WO
WIPO (PCT)
Prior art keywords
sleep
user
physiological data
credibility
stage
Prior art date
Application number
PCT/CN2018/084634
Other languages
English (en)
French (fr)
Inventor
贺超
宋雨
李育高
Original Assignee
深圳创达云睿智能科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳创达云睿智能科技有限公司 filed Critical 深圳创达云睿智能科技有限公司
Publication of WO2019033787A1 publication Critical patent/WO2019033787A1/zh

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons

Definitions

  • the embodiment of the present application belongs to the technical field of sleep monitoring, and in particular, to a sleep management method, system, and terminal device.
  • the embodiment of the present application provides a sleep management method, system, and terminal device, which can solve the problem that the high-precision real-time staging result cannot be provided in the sleep staging process in the prior art, and the appropriate staging result cannot be properly intervened. problem.
  • a first aspect of the embodiments of the present application provides a sleep management method, where the sleep management method includes:
  • Sleep intervention is performed according to the current sleep stage of the user.
  • a second aspect of the embodiments of the present application provides a sleep management system, where the sleep management system includes:
  • a data acquisition unit configured to acquire more than one type of physiological data of the user when the user is in a sleep state, and extract feature information of each of the physiological data
  • a preliminary determining unit configured to initially determine, according to the feature information, a sleep staging stage corresponding to each of the physiological data
  • a credibility order obtaining unit configured to acquire a credibility sequence of each of the physiological data when the sleep staging stages corresponding to each of the physiological data are inconsistent;
  • a sleep staging stage determining unit configured to determine, according to the credibility order, a sleep staging stage in which the user is currently located
  • a sleep intervention unit is configured to perform sleep intervention according to a current sleep stage of the user.
  • a third aspect of the embodiments of the present application provides a terminal device including a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program The steps of the method of any of the sleep management methods are implemented.
  • a fourth aspect of the embodiments of the present application provides a computer readable storage medium storing a computer program, when the computer program is executed by a processor, implementing any one of the sleep management methods The steps of the method.
  • the physiological data of the user in the sleep state is acquired, the feature information of each physiological data is extracted, and the sleep stage of the user is currently according to the feature information of each physiological data.
  • obtaining the credibility order of the detected physiological data when the judgment results are inconsistent judging the sleep state of the user according to the credibility order of the physiological data, and then providing according to the sleep staging stage of the user Appropriate interventions.
  • the user can directly interpret the sleep stage of the user according to the physiological data detected by the user in real time, the judgment result is accurate, the detection cost is low, and the intervention is given according to the user's sleep stage, which helps to improve the user. Sleep quality.
  • FIG. 1 is a schematic flowchart of an implementation process of a sleep management method according to Embodiment 1 of the present application;
  • FIG. 2 is a schematic diagram of a sleep management system according to Embodiment 2 of the present application.
  • FIG. 3 is a schematic diagram of a terminal device according to Embodiment 3 of the present application.
  • FIG. 1 is a schematic flowchart of a sleep management method according to an embodiment of the present application, which is described in detail as follows:
  • Step S11 When the user is in a sleep state, acquire more than one type of physiological data of the user, and extract feature information of each of the physiological data;
  • a plurality of physiological data of the user are collected in real time when the user is in a sleep state, wherein the physiological data includes but is not limited to EEG data.
  • At least one raw data such as eye movement data, body position data, body motion data, heart rate data, blood oxygen data, respiratory rate, snoring loudness, body temperature, and the like.
  • the collected physiological data is extracted in units of preset time intervals, and feature information of various physiological data in each preset time interval is extracted.
  • the preset time interval starts to count when the user is in a sleep state, for example, five minutes from when the user is in a sleep state is a preset time interval. Extracting characteristic information of various physiological data of the user within five minutes, and then acquiring physiological data within 5-10 minutes (next preset time interval) of the user's sleep state as the user sleeps, and acquiring the 5-10 Characteristic information of various physiological data of the user within minutes.
  • the preset time interval may be set by the user, such as setting the preset time interval to 1 minute or 10 minutes, etc., which is not limited herein.
  • Extracting characteristic information of the physiological data includes extracting energy proportion of each band of brain waves in the brain electrical data to determine feature information of the brain electrical data in a current time interval; and calculating the heart rate data according to the heart rate algorithm and the blood oxygen algorithm
  • the blood oxygen data calculates the heart rate value and the blood oxygen value in the current preset time interval; further, the heart rate value or the fluctuation characteristic of the blood oxygen value may be extracted as the heart rate data and the blood oxygen data in the current preset time interval.
  • Characteristic information similarly, the corresponding feature information is extracted according to the characteristics of other physiological data of the user.
  • Step S12 preliminary determining, according to the feature information, a sleep staging stage corresponding to each of the physiological data
  • the sleep process of an adult is divided into a Wake period according to the AASM criterion---awake period, N1 phase---non-rapid eye movement phase 1, N2 phase---non-rapid eye movement phase 2, N3 phase --- non-rapid eye movement phase 3 and R phase --- rapid eye movement period.
  • the preliminary determination of the sleep staging phase currently occupied by the user is performed according to the preset model and the extracted feature information of various physiological data.
  • the preset model When the preset model is established, various physiological data in each sleep process of the same user within a preset time period are collected, and the collected physiological data is analyzed and collated, and the clustering algorithm is used (including but not limited to the k-means algorithm, C-means algorithm, dynamic clustering, recurrent neural network) establishes the mapping relationship between the feature information of each physiological data and the corresponding sleep staging stage to form the preset model.
  • the clustering algorithm including but not limited to the k-means algorithm, C-means algorithm, dynamic clustering, recurrent neural network
  • each physiological data of the same user can establish a corresponding preset model.
  • the N1 period in which the current user is in the sleep process is determined; according to the heart rate value and blood
  • the oxygen value and the corresponding preset model determine that the user is currently in the N1 phase of the sleep process.
  • Step S13 acquiring a credibility sequence of each of the physiological data when the sleep staging stages corresponding to each of the physiological data are inconsistent;
  • the sleep stage of the current user is most likely to be determined, and then the credibility of the physiological data of the user at the stage of the sleep stage is obtained.
  • the EEG data it is initially determined that the user is in the N3 phase of sleep, and the user is initially judged to be in the N1 phase of the sleep process according to the heart rate value, and judges that the user is also in the sleep process N3 according to the blood oxygen data and the eye movement data.
  • the credibility of the EEG data, the blood oxygen data, and the eye movement data in the N3 phase is obtained.
  • the reliability of each physiological data is different in different stages of sleep staging, and the order of credibility is determined according to the existing research results. For example, in the Wake period, the credibility of the body motion data is greater than the credibility of other physiological data.
  • the credibility of the N3 EEG data is greater than the credibility of other physiological data.
  • the sleep staging phase currently in which the user is currently located is directly determined according to the preliminary judgment result; for example, preliminary judgments according to brain electrical data, heart rate data, blood oxygen data, and the like are determined.
  • the user is determined to be in the N2 phase of the sleep process according to the preliminary judgment result.
  • the obtaining the credibility order of each of the physiological data comprises:
  • the order of credibility of each of the physiological data is determined according to the accuracy.
  • the accuracy of each physiological data detected under the current conditions may also be Will be affected, for example, the detection conditions are appropriate and the accuracy of the physiological data obtained when the instrument is in good condition will be higher, and the accuracy of the physiological data obtained when the instrument state or other conditions change will also change, therefore, in determining each
  • the credibility of the physiological data is first, the preliminary judgment result of the plurality of physiological data is firstly determined to determine the most likely stage of the sleep stage of the user, and the accuracy rate of each physiological data corresponding to the sleep staging stage is obtained, and then the accuracy rate is The order of credibility of each physiological data obtained by the study is combined with the order of reliability of the currently detected physiological data.
  • Step S14 determining, according to the credibility order, a sleep staging stage in which the user is currently located;
  • the sleep staging phase currently occupied by the user is determined according to the credibility order of the monitored physiological data. Specifically, firstly, according to the preliminary judgment result of each physiological data, the sleep staging stage that the user is most likely to be currently determined is determined, and then the credibility order of the physiological data in the sleep staging stage most likely to be in the user is obtained, and the presupposition is preset according to the credibility.
  • the physiological data within the range determines the stage of sleep staging that the user is in.
  • the determining, according to the credibility order, the sleep staging phase currently in which the user is located includes:
  • the accuracy of the detection result of the physiological data with the highest credibility under the current condition is obtained, and if the accuracy under the current condition is greater than the preset
  • the value determines the stage of sleep staging of the current user based on the most reliable physiological data. For example, if the current EEG data has the highest reliability, the accuracy of the EEG data under the current detection condition is obtained. If the accuracy rate is greater than the preset value, and the EEG data initially determines that the current user is in the N3 phase of sleep, then Determine the user is currently in the N3 phase of sleep. If the accuracy of the test data obtained under the current detection condition is less than the preset value, the physiological data with the most credibility is in the second or third (or other credibility order) physiological data. The judgment result determines the stage of sleep staging that the user is currently in.
  • Step S15 performing sleep intervention according to the current sleep stage of the user.
  • the user is given appropriate intervention after determining the sleep stage of the user, such as playing the music that helps the user to sleep when the user needs to sleep; when the user needs to wake up, the user is awakened by an appropriate method. Wait.
  • the performing sleep intervention according to the current sleep stage of the user includes:
  • the preset intervention is triggered when the user is allowed to intervene in the current sleep staging phase.
  • the sleep staging phase After determining the sleep staging phase that the user is currently in, it is possible to determine whether to allow sleep intervention for the user according to the preset condition of the user, and give appropriate intervention if the user allows. For example, if it is determined that the user is in the awake period (Wake period), according to the user preset condition, the user is allowed to perform sleep intervention, and some audio stimulation for the sleep aid is started, and environmental factors such as lights, curtains, temperature, etc. are adjusted, and The feedback results of physiological data such as brain waves are analyzed. Physiological information such as brain waves, if feedback to the user is drowsy, continues to play, and gradually reduces the level of audio stimulation. When the user enters the light sleep period (N1 period) for a while, the audio stimulation is stopped.
  • N3 period deep sleep period
  • audio stimulation that contributes to deep sleep extension is started, which is mainly based on low frequency brain waves Synchronous oscillation principle, when the user brain wave enters the low frequency specific frequency state, the instantaneous audio content corresponding to the brain wave rhythm is dynamically matched, and the real-time brain wave of the user is dynamically matched, and the stimulus is weakened when the feedback result is not ideal, and Stops automatically when the user naturally or accidentally leaves the deep sleep period.
  • Intelligent wake-up during the light sleep period Before or after the user presets the wake-up time, or before reaching the user's preset sleep target, if it is determined that the user is in a light sleep state, the user is awakened using a gentle external stimulus, including but not limited to Slowly accelerating equipment vibration, or progressive music, lighting, curtain opening, mattress movement, etc.
  • a gentle external stimulus including but not limited to Slowly accelerating equipment vibration, or progressive music, lighting, curtain opening, mattress movement, etc.
  • the method includes:
  • the sleep staging phase of the user's entire sleep process and the time period information of each sleep staging phase are displayed.
  • the sleep staging phase After determining the sleep staging phase that the user is currently in, recording the result of the current judgment and the time the user stays in the sleep staging phase, while continuing to detect the physiological data of the user's subsequent sleep in real time, and obtaining the entire user.
  • the sleep segmentation results of the entire sleep process are comprehensively analyzed and the segmentation of the user's entire sleep process is displayed in the form of a sleep report.
  • the sleep process has periodicity, generally one cycle sleep stage sequence is: Wake phase, N1 phase, N2 phase, N3 phase, and REM phase.
  • the results of the sleep staging phase should be met for the total staging results.
  • the comprehensive analysis of the entire sleep process if the user is in the sleep period of several preset time intervals before the current preset time interval, the user determines that the sleep stage is N2, and the current preset time interval is N1. Then, the judgment result in the current time interval is adjusted to the N2 phase or the N3 phase according to the result of the judgment of the next preset time interval of the current preset time interval.
  • Comprehensive analysis of the test results of the whole process can also avoid certain feature failures. For example, if the user has a jump in the Wake period and the REM period during a long deep sleep period (N3 period), the comprehensive analysis can be used to eliminate the Reasonable conclusion.
  • the physiological data of the user in the sleep state is acquired, the feature information of each physiological data is extracted, and the sleep stage of the user is currently according to the feature information of each physiological data.
  • Performing preliminary judgments obtaining the credibility order of the detected physiological data when the judgment results are inconsistent, judging the sleep state of the user according to the credibility order of the physiological data, and then providing according to the sleep staging stage of the user Appropriate interventions.
  • the user can determine the current stage of sleep staging according to the physiological data detected by the user in real time, the judgment result is accurate, the detection cost is low, and the intervention is given according to the user's sleep stage, which helps to improve the user. Sleep quality.
  • FIG. 2 is a structural block diagram of the sleep management system provided by the embodiment of the present application. For the convenience of description, only parts related to the embodiment of the present application are shown.
  • the sleep management system includes: a data acquisition unit 21, a preliminary determination unit 22, a credibility order acquisition unit 23, a sleep staging phase determination unit 24, and a sleep intervention unit 25, wherein:
  • the data obtaining unit 21 is configured to acquire more than one type of physiological data of the user when the user is in a sleep state, and extract feature information of each of the physiological data;
  • a plurality of physiological data of the user are collected in real time when the user is in a sleep state, wherein the physiological data includes but is not limited to EEG data.
  • At least one raw data such as eye movement data, body position data, body motion data, heart rate data, blood oxygen data, respiratory rate, snoring loudness, body temperature, and the like.
  • the collected physiological data is extracted in units of preset time intervals, and feature information of various physiological data in each preset time interval is extracted.
  • the preset time interval starts to count when the user is in a sleep state, for example, five minutes from when the user is in a sleep state is a preset time interval. Extracting characteristic information of various physiological data of the user within five minutes, and then acquiring physiological data within 5-10 minutes (next preset time interval) of the user's sleep state as the user sleeps, and acquiring the 5-10 Characteristic information of various physiological data of the user within minutes.
  • the preset time interval may be set by the user, such as setting the preset time interval to 1 minute or 10 minutes, etc., which is not limited herein.
  • Extracting characteristic information of the physiological data includes extracting energy proportion of each band of brain waves in the brain electrical data to determine feature information of the brain electrical data in a current time interval; and calculating the heart rate data according to the heart rate algorithm and the blood oxygen algorithm
  • the blood oxygen data calculates the heart rate value and the blood oxygen value in the current preset time interval; further, the heart rate value or the fluctuation characteristic of the blood oxygen value may be extracted as the heart rate data and the blood oxygen data in the current preset time interval.
  • Characteristic information similarly, the corresponding feature information is extracted according to the characteristics of other physiological data of the user.
  • the preliminary determining unit 22 is configured to initially determine, according to the feature information, a sleep staging stage corresponding to each of the physiological data;
  • the sleep process of an adult is divided into a Wake period according to the AASM criterion---awake period, N1 phase---non-rapid eye movement phase 1, N2 phase---non-rapid eye movement phase 2, N3 phase --- non-rapid eye movement phase 3 and R phase --- rapid eye movement period.
  • the preliminary determination of the sleep staging phase currently occupied by the user is performed according to the preset model and the extracted feature information of various physiological data.
  • the preset model When the preset model is established, various physiological data in each sleep process of the same user within a preset time period are collected, and the collected physiological data is analyzed and collated, and the clustering algorithm is used (including but not limited to the k-means algorithm, C-means algorithm, dynamic clustering, recurrent neural network) establishes the mapping relationship between the feature information of each physiological data and the corresponding sleep staging stage to form the preset model.
  • the clustering algorithm including but not limited to the k-means algorithm, C-means algorithm, dynamic clustering, recurrent neural network
  • each physiological data of the same user can establish a corresponding preset model.
  • the N1 period in which the current user is in the sleep process is determined; according to the heart rate value and blood
  • the oxygen value and the corresponding preset model determine that the user is currently in the N1 phase of the sleep process.
  • the credibility order obtaining unit 23 is configured to obtain a credibility sequence of each of the physiological data when the sleep staging stages corresponding to each of the physiological data are inconsistent;
  • the sleep stage of the current user is most likely to be determined, and then the credibility of the physiological data of the user at the stage of the sleep stage is obtained.
  • the EEG data it is initially determined that the user is in the N3 phase of sleep, and the user is initially judged to be in the N1 phase of the sleep process according to the heart rate value, and judges that the user is also in the sleep process N3 according to the blood oxygen data and the eye movement data.
  • the credibility of the EEG data, the blood oxygen data, and the eye movement data in the N3 phase is obtained.
  • the reliability of each physiological data is different in different stages of sleep staging, and the order of credibility is determined according to the existing research results. For example, in the Wake period, the credibility of the body motion data is greater than the credibility of other physiological data.
  • the credibility of the N3 EEG data is greater than the credibility of other physiological data.
  • the sleep staging phase currently in which the user is currently located is directly determined according to the preliminary judgment result; for example, preliminary judgments according to brain electrical data, heart rate data, blood oxygen data, and the like are determined.
  • the user is determined to be in the N2 phase of the sleep process according to the preliminary judgment result.
  • the credibility order obtaining unit 23 includes:
  • An accuracy acquisition module configured to obtain an accuracy rate of each of the physiological data in the corresponding sleep staging stage
  • a sequence determining module configured to determine a credibility sequence of each of the physiological data according to the accuracy rate.
  • the accuracy of each physiological data detected under the current conditions may also be Will be affected, for example, the detection conditions are appropriate and the accuracy of the physiological data obtained when the instrument is in good condition will be higher, and the accuracy of the physiological data obtained when the instrument state or other conditions change will also change, therefore, in determining each
  • the credibility of the physiological data is first, the preliminary judgment result of the plurality of physiological data is firstly determined to determine the most likely stage of the sleep stage of the user, and the accuracy rate of each physiological data corresponding to the sleep staging stage is obtained, and then the accuracy rate is The order of credibility of each physiological data obtained by the study is combined with the order of reliability of the currently detected physiological data.
  • the sleep staging stage determining unit 24 is configured to determine, according to the credibility order, a sleep staging stage in which the user is currently located;
  • the sleep staging phase currently occupied by the user is determined according to the credibility order of the monitored physiological data. Specifically, firstly, according to the preliminary judgment result of each physiological data, the sleep staging stage that the user is most likely to be currently determined is determined, and then the credibility order of the physiological data in the sleep staging stage most likely to be in the user is obtained, and the presupposition is preset according to the credibility.
  • the physiological data within the range determines the stage of sleep staging that the user is in.
  • the sleep staging stage determining unit 24 includes:
  • a first determining module configured to determine, according to a sleep staging phase corresponding to the physiological data with the highest credibility, a sleep staging phase of the user when the accuracy of the most credible physiological data is greater than a preset value ;
  • the second determining module is configured to determine, according to the sleep staging phase corresponding to the physiological data in the preset sorting, the sleep staging phase currently in which the user is located when the accuracy of the most reliable physiological data is not greater than a preset value.
  • the accuracy of the detection result of the physiological data with the highest credibility under the current condition is obtained, and if the accuracy under the current condition is greater than the preset
  • the value determines the stage of sleep staging of the current user based on the most reliable physiological data. For example, if the current EEG data has the highest reliability, the accuracy of the EEG data under the current detection condition is obtained. If the accuracy rate is greater than the preset value, and the EEG data initially determines that the current user is in the N3 phase of sleep, then Determine the user is currently in the N3 phase of sleep. If the accuracy of the test data obtained under the current detection condition is less than the preset value, the physiological data with the most credibility is in the second or third (or other credibility order) physiological data. The judgment result determines the stage of sleep staging that the user is currently in.
  • the sleep intervention unit 25 is configured to perform sleep intervention according to the current sleep stage of the user.
  • the user is given appropriate intervention after determining the sleep stage of the user, such as playing the music that helps the user to sleep when the user needs to sleep; when the user needs to wake up, the user is awakened by an appropriate method. Wait.
  • the sleep intervention unit 25 comprises:
  • a condition obtaining module configured to acquire a preset condition, and determine, according to the preset condition, whether to allow intervention of the current sleep staging phase of the user;
  • the intervention module is configured to trigger a preset intervention when the intervention of the current sleep stage of the user is allowed.
  • the sleep staging phase After determining the sleep staging phase that the user is currently in, it is possible to determine whether to allow sleep intervention for the user according to the preset condition of the user, and give appropriate intervention if the user allows. For example, if it is determined that the user is in the awake period (Wake period), according to the user preset condition, the user is allowed to perform sleep intervention, and some audio stimulation for the sleep aid is started, and environmental factors such as lights, curtains, temperature, etc. are adjusted, and The feedback results of physiological data such as brain waves are analyzed. Physiological information such as brain waves, if feedback to the user is drowsy, continues to play, and gradually reduces the level of audio stimulation. When the user enters the light sleep period (N1 period) for a while, the audio stimulation is stopped.
  • N3 period deep sleep period
  • audio stimulation that contributes to deep sleep extension is started, which is mainly based on low frequency brain waves Synchronous oscillation principle, when the user brain wave enters the low frequency specific frequency state, the instantaneous audio content corresponding to the brain wave rhythm is dynamically matched, and the real-time brain wave of the user is dynamically matched, and the stimulus is weakened when the feedback result is not ideal, and Stops automatically when the user naturally or accidentally leaves the deep sleep period.
  • Intelligent wake-up during the light sleep period Before or after the user presets the wake-up time, or before reaching the user's preset sleep target, if it is determined that the user is in a light sleep state, the user is awakened using a gentle external stimulus, including but not limited to Slowly accelerating equipment vibration, or progressive music, lighting, curtain opening, mattress movement, etc.
  • a gentle external stimulus including but not limited to Slowly accelerating equipment vibration, or progressive music, lighting, curtain opening, mattress movement, etc.
  • the sleep management system further includes:
  • a recording unit configured to record a sleep staging phase currently in which the user is located and time period information corresponding to the sleep staging phase
  • a display unit configured to display, during the entire sleep process of the user, a sleep staging phase of the entire sleep process of the user and time period information of each sleep staging phase.
  • the sleep staging phase After determining the sleep staging phase that the user is currently in, recording the result of the current judgment and the time the user stays in the sleep staging phase, while continuing to detect the physiological data of the user's subsequent sleep in real time, and obtaining the entire user.
  • the sleep segmentation results of the entire sleep process are comprehensively analyzed and the segmentation of the user's entire sleep process is displayed in the form of a sleep report.
  • the sleep process has periodicity, generally one cycle sleep stage sequence is: Wake phase, N1 phase, N2 phase, N3 phase, and REM phase.
  • the results of the sleep staging phase should be met for the total staging results.
  • the comprehensive analysis of the entire sleep process if the user is in the sleep period of several preset time intervals before the current preset time interval, the user determines that the sleep stage is N2, and the current preset time interval is N1. Then, the judgment result in the current time interval is adjusted to the N2 phase or the N3 phase according to the result of the judgment of the next preset time interval of the current preset time interval.
  • Comprehensive analysis of the test results of the whole process can also avoid certain feature failures. For example, if the user has a jump in the Wake period and the REM period during a long deep sleep period (N3 period), the comprehensive analysis can be used to eliminate the Reasonable conclusion.
  • the physiological data of the user in the sleep state is acquired, the feature information of each physiological data is extracted, and the sleep stage of the user is currently according to the feature information of each physiological data.
  • Performing preliminary judgments obtaining the credibility order of the detected physiological data when the judgment results are inconsistent, judging the sleep state of the user according to the credibility order of the physiological data, and then providing according to the sleep staging stage of the user Appropriate interventions.
  • the user can determine the current stage of sleep staging according to the physiological data detected by the user in real time, the judgment result is accurate, the detection cost is low, and the intervention is given according to the user's sleep stage, which helps to improve the user. Sleep quality.
  • FIG. 3 is a schematic diagram of a terminal device according to an embodiment of the present application.
  • the terminal device 3 of this embodiment includes a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and operable on the processor 30.
  • the processor 30 executes the computer program 32, the steps in the foregoing various sleep management method embodiments are implemented, such as steps S11 to S15 shown in FIG.
  • the processor 30 executes the computer program 32, the functions of the modules/units in the above-described respective device embodiments, such as the functions of the units 21 to 25 shown in FIG. 2, are implemented.
  • the computer program 32 can be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to complete This application.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing a particular function, the instruction segments being used to describe the execution of the computer program 32 in the terminal device 3.
  • the computer program 32 can be divided into a data acquisition unit, a preliminary determination unit, a credibility order acquisition unit, a sleep staging phase determination unit, and a sleep intervention unit, wherein:
  • a data acquisition unit configured to acquire more than one type of physiological data of the user when the user is in a sleep state, and extract feature information of each of the physiological data
  • a preliminary determining unit configured to initially determine, according to the feature information, a sleep staging stage corresponding to each of the physiological data
  • a credibility order obtaining unit configured to acquire a credibility sequence of each of the physiological data when the sleep staging stages corresponding to each of the physiological data are inconsistent;
  • a sleep staging stage determining unit configured to determine, according to the credibility order, a sleep staging stage in which the user is currently located
  • a sleep intervention unit is configured to perform sleep intervention according to a current sleep stage of the user.
  • the credibility order obtaining unit includes:
  • An accuracy acquisition module configured to obtain an accuracy rate of each of the physiological data in the corresponding sleep staging stage
  • a sequence determining module configured to determine a credibility sequence of each of the physiological data according to the accuracy rate.
  • the sleep staging stage determining unit includes:
  • a first determining module configured to determine, according to a sleep staging phase corresponding to the physiological data with the highest credibility, a sleep staging phase of the user when the accuracy of the most credible physiological data is greater than a preset value ;
  • the second determining module is configured to determine, according to the sleep staging phase corresponding to the physiological data in the preset sorting, the sleep staging phase currently in which the user is located when the accuracy of the most reliable physiological data is not greater than a preset value.
  • the sleep management system further includes:
  • a recording unit configured to record a sleep staging phase currently in which the user is located and time period information corresponding to the sleep staging phase
  • a display unit configured to display, during the entire sleep process of the user, a sleep staging phase of the entire sleep process of the user and time period information of each sleep staging phase.
  • the sleep intervention unit includes:
  • a condition obtaining module configured to acquire a preset condition, and determine, according to the preset condition, whether to allow intervention of the current sleep staging phase of the user;
  • the intervention module is configured to trigger a preset intervention when the intervention of the current sleep stage of the user is allowed.
  • the terminal device 3 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the terminal device may include, but is not limited to, the processor 30 and the memory 31. It will be understood by those skilled in the art that FIG. 3 is only an example of the terminal device 3, does not constitute a limitation of the terminal device 3, may include more or less components than those illustrated, or combine some components, or different components.
  • the terminal device may further include an input/output device, a network access device, a bus, and the like.
  • the so-called processor 30 can be a central processing unit (Central Processing Unit, CPU), can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3.
  • the memory 31 may also be an external storage device of the terminal device 3, for example, a plug-in hard disk equipped on the terminal device 3, a smart memory card (SMC), and a secure digital (SD). Card, flash card (Flash Card) and so on.
  • the memory 31 may also include both an internal storage unit of the terminal device 3 and an external storage device.
  • the memory 31 is used to store the computer program and other programs and data required by the terminal device.
  • the memory 31 can also be used to temporarily store data that has been output or is about to be output.
  • each functional unit and module in the foregoing system may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be implemented by hardware.
  • Formal implementation can also be implemented in the form of software functional units.
  • the specific names of the respective functional units and modules are only for the purpose of facilitating mutual differentiation, and are not intended to limit the scope of protection of the present application.
  • the disclosed device/terminal device and method may be implemented in other manners.
  • the device/terminal device embodiments described above are merely illustrative.
  • the division of the modules or units is only a logical function division.
  • there may be another division manner for example, multiple units.
  • components may be combined or integrated into another system, or some features may be omitted or not performed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated modules/units if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the processes in the foregoing embodiments, and may also be completed by a computer program to instruct related hardware.
  • the computer program may be stored in a computer readable storage medium. The steps of the various method embodiments described above may be implemented when the program is executed by the processor. .
  • the computer program comprises computer program code, which may be in the form of source code, object code form, executable file or some intermediate form.
  • the computer readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard drive, a magnetic disk, an optical disk, a computer memory, a read only memory (ROM, Read-Only) Memory), random access memory (RAM, Random) Access Memory), electrical carrier signals, telecommunications signals, and software distribution media.
  • ROM Read Only memory
  • RAM Random Access Memory
  • electrical carrier signals telecommunications signals
  • telecommunications signals and software distribution media. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer readable media It does not include electrical carrier signals and telecommunication signals.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

一种睡眠管理方法、系统及终端设备，包括：用户处于睡眠状态时，获取所述用户的多于一种的生理数据，并提取每种所述生理数据的特征信息（S11）；根据所述特征信息初步判断每种所述生理数据对应的睡眠分期阶段（S12）；在每种所述生理数据对应的睡眠分期阶段不一致时，获取每种所述生理数据的可信度顺序（S13）；根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段，并根据所述用户当前的睡眠分期阶段进行睡眠干预（S14）。该方法、系统及终端设备可根据实时检测到的用户睡眠时的各项生理数据判定用户当前所处的睡眠分期阶段，判断结果精准，检测成本低，根据用户的睡眠分期阶段给予干预措施，有助于提高用户睡眠质量。

Description

一种睡眠管理方法、系统及终端设备 技术领域
本申请实施例属于睡眠监测技术领域，尤其涉及一种睡眠管理方法、系统及终端设备。
 背景技术
众所周知，睡眠质量的好坏直接影响着人们的身体健康。随着人们生活节奏的不断加快，各种睡眠问题不断出现，人们也越来越关注自己的睡眠过程中每个睡眠时期的睡眠质量。2007年美国睡眠协会（AASM）颁布了基于多导睡眠仪（Polysomnography,PSG）的脑电睡眠分期准则（AASM准则），已成为医学界对睡眠问题即睡眠分期进行分析的重要依据。
随着技术的进步和市场需求的旺盛，睡眠监测设备不断出现，但现有的睡眠监测设备大多不能保证监测信号质量，在对睡眠分期时也并未完全遵循上述AASM准则。医院中通过PSG对睡眠进行监测并分期时，需要医生进行手动分析以得出报告，因此，即使两位顶级的睡眠相关专家对同一份睡眠监测数据进行睡眠分期，所得的睡眠分期结果也可能存在较大的差别，并且PSG设备价格昂贵、安装复杂、使用门槛极高，目前还无法实现大规模普及，同时现有的睡眠阶段检测设备也不能根据用户所处的睡眠阶段给予适当的干预措施。
 技术问题
有鉴于此，本申请实施例提供了一种睡眠管理方法、系统及终端设备，以解决现有技术中睡眠分期过程中无法提供高精度的实时分期结果也不能根据所得的分期结果进行适当干预的问题。
 技术解决方案
本申请实施例的第一方面提供了一种睡眠管理方法，所述睡眠管理方法包括：
用户处于睡眠状态时，获取所述用户的多于一种的生理数据，并提取每种所述生理数据的特征信息；
根据所述特征信息初步判断每种所述生理数据对应的睡眠分期阶段；
在每种所述生理数据对应的睡眠分期阶段不一致时，获取每种所述生理数据的可信度顺序；
根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段；
根据所述用户当前的睡眠分期阶段进行睡眠干预。
本申请实施例的第二方面提供了一种睡眠管理系统，所述睡眠管理系统包括：
数据获取单元，用于在用户处于睡眠状态时，获取所述用户的多于一种的生理数据，并提取每种所述生理数据的特征信息；
初步判断单元，用于根据所述特征信息初步判断每种所述生理数据对应的睡眠分期阶段；
可信度顺序获取单元，用于在每种所述生理数据对应的睡眠分期阶段不一致时，获取每种所述生理数据的可信度顺序；
睡眠分期阶段判定单元，用于根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段；
睡眠干预单元，用于根据所述用户当前的睡眠分期阶段进行睡眠干预。
本申请实施例的第三方面提供了一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如所述睡眠管理方法任一项所述方法的步骤。
本申请实施例的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如所述睡眠管理方法任一项所述方法的步骤。
 有益效果
本申请提供的实施例中用户处于睡眠状态时，获取用户处于睡眠中状态的生理数据，提取每项生理数据的特征信息，并根据每项生理数据的特征信息对用户当前所处的睡眠分期阶段进行初步判断，在各项判断结果不一致时，获取检测到的生理数据的可信度顺序，根据生理数据的可信度顺序判断用户所处的睡眠状态，然后根据用户所处的睡眠分期阶段提供适当的干预措施。该过程中可根据实时检测到的用户睡眠时的各项生理数据判读用户当前所处的睡眠分期阶段，判断结果精准，检测成本低，根据用户的睡眠分期阶段给予干预措施，有助于提高用户睡眠质量。
 附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例一提供的一种睡眠管理方法的实现流程示意图；
图2是本申请实施例二提供的一种睡眠管理系统的示意图；
图3是本申请实施例三提供的终端设备的示意图。
 本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
为了说明本申请所述的技术方案，下面通过具体实施例来进行说明。
实施例1
图1示出了本申请实施例提供的一种睡眠管理方法实现流程示意图，详述如下：
步骤S11，用户处于睡眠状态时，获取所述用户的多于一种的生理数据，并提取每种所述生理数据的特征信息；
本申请提供的实施例中为了检测用户睡眠时所处的睡眠分期阶段，在用户处于睡眠状态时，实时采集所述用户的多种生理数据，其中，所述生理数据包含但不限于脑电数据、眼动数据、体位数据、体动数据、心率数据、血氧数据、呼吸频率、鼾声响度、体温等的至少一种原始数据。
对采集到的各种生理数据以预设时间间隔为单位，提取每个预设时间间隔内各种生理数据的特征信息。其中，所述预设时间间隔以所述用户处于睡眠状态开始计时，例如，从用户处于睡眠状态开始的五分钟为一个预设时间间隔。提取五分钟内用户各种生理数据的特征信息，然后随着用户睡眠过程的进行获取用户睡眠状态开始5-10分钟（下一个预设时间间隔）内的生理数据，并获取所述5-10分钟内用户各种生理数据的特征信息。当然，所述预设时间间隔可由用户进行设定，如设置预设时间间隔为1分钟或10分钟等，在此不做限定。提取所述生理数据的特征信息包括提取脑电数据中脑电波各个波段的能量占比以确定脑电数据在当前的时间间隔内的特征信息；通过心率算法、血氧算法根据所述心率数据和血氧数据计算出当前的预设时间间隔内心率值和血氧值；进一步地，可提取所述心率值或血氧值的波动特征作为当前的预设时间间隔内心率数据和血氧数据的特征信息；同理根据用户其他各项生理数据的特点分别提取其对应的特征信息。
步骤S12，根据所述特征信息初步判断每种所述生理数据对应的睡眠分期阶段；
本申请提供的实施例中根据AASM准则将成年人的睡眠过程分为Wake期---清醒期、N1期---非快速眼动1期、N2期---非快速眼动2期、N3期---非快速眼动3期以及R期---快速眼动期。在提取到用户预设时间间隔内各种生理数据的特征信息后，根据预设模型以及提取的各种生理数据的特征信息对用户当前所处的睡眠分期阶段进行初步判断。在建立所述预设模型时，采集同一用户预设时间内每次睡眠过程中的各种生理数据，对采集的生理数据进行分析整理，利用聚类算法(包括但不限于k-means算法，C-means算法，动态聚类，递归神经网络)建立每种生理数据的特征信息与对应睡眠分期阶段的映射关系，形成所述预设模型。可选地，同一用户每种生理数据可以建立一个对应的预设模型。进行初步判断时，例如根据当前的预设时间间隔内所述用户的脑电数据的特征信息及脑电数据对应的预设模型判断出当前用户处于睡眠过程中的N1期；根据心率值和血氧值及与之对应的预设模型均判断出用户当前处于睡眠过程中的N1期。
步骤S13，在每种所述生理数据对应的睡眠分期阶段不一致时，获取每种所述生理数据的可信度顺序；
本申请提供的实施例中，根据预设模型以及用户生理数据的特征信息对用户当前所处的睡眠状态进行初步判断后，若通过每项检测到的生理数据判断出的用户所处睡眠状态不在同一时期，则结合所有生理数据的初步判断结果，确定出当前用户最可能处于的睡眠分期阶段，然后获取用户在该睡眠分期阶段时各项生理数据的可信度。例如根据脑电数据初步判断出用户处于睡眠的N3期，而根据心率值初步判断出所述用户处于睡眠过程的N1期，根据血氧数据和眼动数据判断出用户同样处于睡眠过程的N3，则判定用户当前最可能处于N3期，然后获取脑电数据、血氧数据和眼动数据在N3期的可信度。每项生理数据在不同睡眠分期阶段的可信度不同，其的可信度顺序根据已有研究结果确定，例如，在Wake期，体动数据可信度大于其他项生理数据的可信度，而在N3期脑电数据的可信度大于其他项生理数据的可信度。
可选地，若每种所述生理数据初步判断的睡眠分期阶段一致，则直接根据初步判断结果确定用户当前所处的睡眠分期阶段；例如根据脑电数据、心率数据、血氧数据等初步判断后均得出用户当前处于睡眠分期阶段的N2期，则根据初步判断结果确定所述用户当前处于睡眠过程的N2期。
优选地，所述获取每种所述生理数据的可信度顺序，包括：
获取每种所述生理数据在所述对应的睡眠分期阶段中的准确率；
根据所述准确率确定所述每种生理数据的可信度顺序。
具体地，在获取用户睡眠过程中各种生理数据时，由于受检测时用户所处的环境条件、用户自身状态等因素的影响，当前条件下所检测到的每项生理数据的准确率可能也会受到影响，例如，检测条件适宜且检测仪器状态好时所得生理数据的准确率会高些，而仪器状态或其他条件改变时所得生理数据的准确率也会随之改变，因此，在确定每项生理数据的可信度时，首先根据多项生理数据的初步判断结果判定用户最可能处于的睡眠分期阶段，并获取对应睡眠分期阶段每项生理数据的准确率，然后将所述准确率与研究所得出的每项生理数据的可信度顺序结合确定当前检测到的生理数据的可信度顺序。
步骤S14，根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段；
本申请提供的实施例中若每项生理数据的初步判断结果不一致，则根据所监测到的生理数据的可信度顺序确定用户当前所处的睡眠分期阶段。具体地，首先根据每项生理数据的初步判断结果确定用户当前最可能处于的睡眠分期阶段，然后获取用户最可能处于的睡眠分期阶段中生理数据的可信度顺序，根据可信度在预设范围内的生理数据确定用户所处的睡眠分期阶段。
优选地，所述根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段，包括：
在可信度最大的生理数据的准确率大于预设值时，根据所述可信度最大的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段；
在可信度最大的生理数据的准确率不大于预设值时，根据预设排序内的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段；
具体地，在根据生理数据的可信度判断用户当前所处的睡眠分期阶段时，获取可信度最大的生理数据在当前条件下检测结果的准确率，若当前条件下的准确率大于预设值，则根据可信度最大的生理数据确定当前用户所处的睡眠分期阶段。例如当前脑电数据的可信度最大，则获取脑电数据在当前检测条件下的准确率，若其准确率大于预设值，而脑电数据初步判断出当前用户处于睡眠的N3期，则判定用户当前处于睡眠过程中的N3期。若可信度最大的生理数据在当前检测条件下得出的检测数据的准确率小于预设值，则根据可信度处于第二或第三（或其他可信度顺序）的生理数据的初步判断结果判断用户当前所处的睡眠分期阶段。
步骤S15，根据所述用户当前的睡眠分期阶段进行睡眠干预。
本申请提供的实施例中，在确定用户当前所处的睡眠分期阶段后给予用户适当的干预，如需要加深用户睡眠时播放有助用户睡眠的音乐；需要唤醒用户时，采用适当方法将用户唤醒等。
优选地，所述根据所述用户当前的睡眠分期阶段进行睡眠干预，包括：
获取预设条件，根据所述预设条件判断是否允许对所述用户当前的睡眠分期阶段进行干预；
在允许对所述用户当前的睡眠分期阶段进行干预时，触发预设的干预事项。
具体地，在判断出用户当前所处的睡眠分期阶段之后，可以根据用户的预设条件判断是否允许对用户进行睡眠干预，并在用户允许的情况下给予适当的干预。例如若判断出用户处于清醒期（Wake期），根据用户预设条件判断出用户允许进行睡眠干预的情况下，开始进行一些助眠的音频刺激，并调节灯光、窗帘、温度等环境因素，并对脑波等生理数据的反馈结果进行分析。脑波等生理信息如果反馈用户有睡意，则持续播放，并逐渐降低音频刺激程度。当用户进入浅睡期（N1期）一段时间，停止进行音频刺激。
深睡期的延长与巩固：若判断出用户处于深睡期（N3期），且允许进行干预的前提下，开始进行有助于深睡延长的音频刺激，所述音频刺激主要基于低频脑波的同步震荡原理，在用户脑波进入低频特定频率状态下，进行瞬时的、与脑波节律有对应关系的音频内容，并动态匹配用户的实时脑波，在反馈结果不理想时减弱刺激，并在用户自然或意外脱离深睡期时自动停止。
浅睡期（N1期）的智能唤醒：在接近用户预设起床时间，或达到用户预设睡眠目标前后，若判断出用户处于浅睡状态，则使用温柔的外界刺激进行唤醒，包括但不限于慢慢加快的设备振动，或者渐入式的音乐，灯光，窗帘开启、床垫的运动等。
优选地，在所述根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段之后，包括：
记录所述用户当前所处的睡眠分期阶段以及与所述睡眠分期阶段对应的时间段信息；
在所述用户整个睡眠过程结束后，显示所述用户整个睡眠过程的睡眠分期阶段及每个睡眠分期阶段的时间段信息。
具体地，在判断出用户当前所处的睡眠分期阶段后，记录当前的判断的结果以及用户在该睡眠分期阶段停留的时间，同时继续实时检测用户后续睡眠中的生理数据，并得出用户整个睡眠过程的各个阶段以及各个阶段经历的时间。在用户整个睡眠过程结束后，对整个睡眠过程的睡眠分段结果进行综合分析并以睡眠报告的形式显示用户整个睡眠过程的分段情况。可选地，由于睡眠过程具有周期性，一般一个周期睡眠分期阶段顺序为：Wake期、N1期、N2期、N3期和REM期。对于总的分期结果应该满足睡眠分期阶段顺序。因此，在对整个睡眠过程进行综合分析时，若当前预设时间间隔之前的若干个预设时间间隔内判断出用户所处睡眠分期阶段为N2，而当前预设时间间隔内判断结果为N1期，则根据当前预设时间间隔的下一个预设时间间隔的判断的结果将当前时间间隔内的判断结果调整为N2期或N3期。对整个过程的检测结果进行综合分析还能避免一定的特征失效，如用户在长时间深睡期（N3期）之中，出现Wake期、REM期的跳变，则可以通过综合分析来剔除不合理的结论。
本申请提供的实施例中用户处于睡眠状态时，获取用户处于睡眠中状态的生理数据，提取每项生理数据的特征信息，并根据每项生理数据的特征信息对用户当前所处的睡眠分期阶段进行初步判断，在各项判断结果不一致时，获取检测到的生理数据的可信度顺序，根据生理数据的可信度顺序判断用户所处的睡眠状态，然后根据用户所处的睡眠分期阶段提供适当的干预措施。该过程中可根据实时检测到的用户睡眠时的各项生理数据判定用户当前所处的睡眠分期阶段，判断结果精准，检测成本低，根据用户的睡眠分期阶段给予干预措施，有助于提高用户睡眠质量。
实施例2
对应于上文实施例所述的睡眠管理方法，图2示出了本申请实施例提供的睡眠管理系统的结构框图，为了便于说明，仅示出了与本申请实施例相关的部分。
参照图2，该睡眠管理系统包括：数据获取单元21，初步判断单元22，可信度顺序获取单元23，睡眠分期阶段判定单元24，睡眠干预单元25，其中：
数据获取单元21，用于在用户处于睡眠状态时，获取所述用户的多于一种的生理数据，并提取每种所述生理数据的特征信息；
本申请提供的实施例中为了检测用户睡眠时所处的睡眠分期阶段，在用户处于睡眠状态时，实时采集所述用户的多种生理数据，其中，所述生理数据包含但不限于脑电数据、眼动数据、体位数据、体动数据、心率数据、血氧数据、呼吸频率、鼾声响度、体温等的至少一种原始数据。
对采集到的各种生理数据以预设时间间隔为单位，提取每个预设时间间隔内各种生理数据的特征信息。其中，所述预设时间间隔以所述用户处于睡眠状态开始计时，例如，从用户处于睡眠状态开始的五分钟为一个预设时间间隔。提取五分钟内用户各种生理数据的特征信息，然后随着用户睡眠过程的进行获取用户睡眠状态开始5-10分钟（下一个预设时间间隔）内的生理数据，并获取所述5-10分钟内用户各种生理数据的特征信息。当然，所述预设时间间隔可由用户进行设定，如设置预设时间间隔为1分钟或10分钟等，在此不做限定。提取所述生理数据的特征信息包括提取脑电数据中脑电波各个波段的能量占比以确定脑电数据在当前的时间间隔内的特征信息；通过心率算法、血氧算法根据所述心率数据和血氧数据计算出当前的预设时间间隔内心率值和血氧值；进一步地，可提取所述心率值或血氧值的波动特征作为当前的预设时间间隔内心率数据和血氧数据的特征信息；同理根据用户其他各项生理数据的特点分别提取其对应的特征信息。
初步判断单元22，用于根据所述特征信息初步判断每种所述生理数据对应的睡眠分期阶段；
本申请提供的实施例中根据AASM准则将成年人的睡眠过程分为Wake期---清醒期、N1期---非快速眼动1期、N2期---非快速眼动2期、N3期---非快速眼动3期以及R期---快速眼动期。在提取到用户预设时间间隔内各种生理数据的特征信息后，根据预设模型以及提取的各种生理数据的特征信息对用户当前所处的睡眠分期阶段进行初步判断。在建立所述预设模型时，采集同一用户预设时间内每次睡眠过程中的各种生理数据，对采集的生理数据进行分析整理，利用聚类算法(包括但不限于k-means算法，C-means算法，动态聚类，递归神经网络)建立每种生理数据的特征信息与对应睡眠分期阶段的映射关系，形成所述预设模型。可选地，同一用户每种生理数据可以建立一个对应的预设模型。进行初步判断时，例如根据当前的预设时间间隔内所述用户的脑电数据的特征信息及脑电数据对应的预设模型判断出当前用户处于睡眠过程中的N1期；根据心率值和血氧值及与之对应的预设模型均判断出用户当前处于睡眠过程中的N1期。
可信度顺序获取单元23，用于在每种所述生理数据对应的睡眠分期阶段不一致时，获取每种所述生理数据的可信度顺序；
本申请提供的实施例中，根据预设模型以及用户生理数据的特征信息对用户当前所处的睡眠状态进行初步判断后，若通过每项检测到的生理数据判断出的用户所处睡眠状态不在同一时期，则结合所有生理数据的初步判断结果，确定出当前用户最可能处于的睡眠分期阶段，然后获取用户在该睡眠分期阶段时各项生理数据的可信度。例如根据脑电数据初步判断出用户处于睡眠的N3期，而根据心率值初步判断出所述用户处于睡眠过程的N1期，根据血氧数据和眼动数据判断出用户同样处于睡眠过程的N3，则判定用户当前最可能处于N3期，然后获取脑电数据、血氧数据和眼动数据在N3期的可信度。每项生理数据在不同睡眠分期阶段的可信度不同，其的可信度顺序根据已有研究结果确定，例如，在Wake期，体动数据可信度大于其他项生理数据的可信度，而在N3期脑电数据的可信度大于其他项生理数据的可信度。
可选地，若每种所述生理数据初步判断的睡眠分期阶段一致，则直接根据初步判断结果确定用户当前所处的睡眠分期阶段；例如根据脑电数据、心率数据、血氧数据等初步判断后均得出用户当前处于睡眠分期阶段的N2期，则根据初步判断结果确定所述用户当前处于睡眠过程的N2期。
优选地，所述可信度顺序获取单元23，包括：
准确率获取模块，用于获取每种所述生理数据在所述对应的睡眠分期阶段中的准确率；
顺序确定模块，用于根据所述准确率确定所述每种生理数据的可信度顺序。
具体地，在获取用户睡眠过程中各种生理数据时，由于受检测时用户所处的环境条件、用户自身状态等因素的影响，当前条件下所检测到的每项生理数据的准确率可能也会受到影响，例如，检测条件适宜且检测仪器状态好时所得生理数据的准确率会高些，而仪器状态或其他条件改变时所得生理数据的准确率也会随之改变，因此，在确定每项生理数据的可信度时，首先根据多项生理数据的初步判断结果判定用户最可能处于的睡眠分期阶段，并获取对应睡眠分期阶段每项生理数据的准确率，然后将所述准确率与研究所得出的每项生理数据的可信度顺序结合确定当前检测到的生理数据的可信度顺序。
睡眠分期阶段判定单元24，用于根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段；
本申请提供的实施例中若每项生理数据的初步判断结果不一致，则根据所监测到的生理数据的可信度顺序确定用户当前所处的睡眠分期阶段。具体地，首先根据每项生理数据的初步判断结果确定用户当前最可能处于的睡眠分期阶段，然后获取用户最可能处于的睡眠分期阶段中生理数据的可信度顺序，根据可信度在预设范围内的生理数据确定用户所处的睡眠分期阶段。
优选地，所述睡眠分期阶段判定单元24，包括：
第一判定模块，用于在可信度最大的生理数据的准确率大于预设值时，根据所述可信度最大的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段；
第二判定模块，用于在可信度最大的生理数据的准确率不大于预设值时，根据预设排序内的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段。
具体地，在根据生理数据的可信度判断用户当前所处的睡眠分期阶段时，获取可信度最大的生理数据在当前条件下检测结果的准确率，若当前条件下的准确率大于预设值，则根据可信度最大的生理数据确定当前用户所处的睡眠分期阶段。例如当前脑电数据的可信度最大，则获取脑电数据在当前检测条件下的准确率，若其准确率大于预设值，而脑电数据初步判断出当前用户处于睡眠的N3期，则判定用户当前处于睡眠过程中的N3期。若可信度最大的生理数据在当前检测条件下得出的检测数据的准确率小于预设值，则根据可信度处于第二或第三（或其他可信度顺序）的生理数据的初步判断结果判断用户当前所处的睡眠分期阶段。
睡眠干预单元25，用于根据所述用户当前的睡眠分期阶段进行睡眠干预。
本申请提供的实施例中，在确定用户当前所处的睡眠分期阶段后给予用户适当的干预，如需要加深用户睡眠时播放有助用户睡眠的音乐；需要唤醒用户时，采用适当方法将用户唤醒等。
优选地，所述睡眠干预单元25，包括：
条件获取模块，用于获取预设条件，根据所述预设条件判断是否允许对所述用户当前的睡眠分期阶段进行干预；
干预模块，用于在允许对所述用户当前的睡眠分期阶段进行干预时，触发预设的干预事项。
具体地，在判断出用户当前所处的睡眠分期阶段之后，可以根据用户的预设条件判断是否允许对用户进行睡眠干预，并在用户允许的情况下给予适当的干预。例如若判断出用户处于清醒期（Wake期），根据用户预设条件判断出用户允许进行睡眠干预的情况下，开始进行一些助眠的音频刺激，并调节灯光、窗帘、温度等环境因素，并对脑波等生理数据的反馈结果进行分析。脑波等生理信息如果反馈用户有睡意，则持续播放，并逐渐降低音频刺激程度。当用户进入浅睡期（N1期）一段时间，停止进行音频刺激。
深睡期的延长与巩固：若判断出用户处于深睡期（N3期），且允许进行干预的前提下，开始进行有助于深睡延长的音频刺激，所述音频刺激主要基于低频脑波的同步震荡原理，在用户脑波进入低频特定频率状态下，进行瞬时的、与脑波节律有对应关系的音频内容，并动态匹配用户的实时脑波，在反馈结果不理想时减弱刺激，并在用户自然或意外脱离深睡期时自动停止。
浅睡期（N1期）的智能唤醒：在接近用户预设起床时间，或达到用户预设睡眠目标前后，若判断出用户处于浅睡状态，则使用温柔的外界刺激进行唤醒，包括但不限于慢慢加快的设备振动，或者渐入式的音乐，灯光，窗帘开启、床垫的运动等。
优选地，所述睡眠管理系统还包括：
记录单元，用于记录所述用户当前所处的睡眠分期阶段以及与所述睡眠分期阶段对应的时间段信息；
显示单元，用于在所述用户整个睡眠过程结束后，显示所述用户整个睡眠过程的睡眠分期阶段及每个睡眠分期阶段的时间段信息。
具体地，在判断出用户当前所处的睡眠分期阶段后，记录当前的判断的结果以及用户在该睡眠分期阶段停留的时间，同时继续实时检测用户后续睡眠中的生理数据，并得出用户整个睡眠过程的各个阶段以及各个阶段经历的时间。在用户整个睡眠过程结束后，对整个睡眠过程的睡眠分段结果进行综合分析并以睡眠报告的形式显示用户整个睡眠过程的分段情况。可选地，由于睡眠过程具有周期性，一般一个周期睡眠分期阶段顺序为：Wake期、N1期、N2期、N3期和REM期。对于总的分期结果应该满足睡眠分期阶段顺序。因此，在对整个睡眠过程进行综合分析时，若当前预设时间间隔之前的若干个预设时间间隔内判断出用户所处睡眠分期阶段为N2，而当前预设时间间隔内判断结果为N1期，则根据当前预设时间间隔的下一个预设时间间隔的判断的结果将当前时间间隔内的判断结果调整为N2期或N3期。对整个过程的检测结果进行综合分析还能避免一定的特征失效，如用户在长时间深睡期（N3期）之中，出现Wake期、REM期的跳变，则可以通过综合分析来剔除不合理的结论。
本申请提供的实施例中用户处于睡眠状态时，获取用户处于睡眠中状态的生理数据，提取每项生理数据的特征信息，并根据每项生理数据的特征信息对用户当前所处的睡眠分期阶段进行初步判断，在各项判断结果不一致时，获取检测到的生理数据的可信度顺序，根据生理数据的可信度顺序判断用户所处的睡眠状态，然后根据用户所处的睡眠分期阶段提供适当的干预措施。该过程中可根据实时检测到的用户睡眠时的各项生理数据判定用户当前所处的睡眠分期阶段，判断结果精准，检测成本低，根据用户的睡眠分期阶段给予干预措施，有助于提高用户睡眠质量。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
实施例3
图3是本申请一实施例提供的一种终端设备的示意图。如图3所示，该实施例的终端设备3包括：处理器30、存储器31以及存储在所述存储器31中并可在所述处理器30上运行的计算机程序32。所述处理器30执行所述计算机程序32时实现上述各个睡眠管理方法实施例中的步骤，例如图1所示的步骤S11至S15。或者，所述处理器30执行所述计算机程序32时实现上述各装置实施例中各模块/单元的功能，例如图2所示单元21至25的功能。
示例性的，所述计算机程序32可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器31中，并由所述处理器30执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序32在所述终端设备3中的执行过程。例如，所述计算机程序32可以被分割成数据获取单元、初步判断单元、可信度顺序获取单元、睡眠分期阶段判定单元、睡眠干预单元，其中：
数据获取单元，用于在用户处于睡眠状态时，获取所述用户的多于一种的生理数据，并提取每种所述生理数据的特征信息；
初步判断单元，用于根据所述特征信息初步判断每种所述生理数据对应的睡眠分期阶段；
可信度顺序获取单元，用于在每种所述生理数据对应的睡眠分期阶段不一致时，获取每种所述生理数据的可信度顺序；
睡眠分期阶段判定单元，用于根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段；
睡眠干预单元，用于根据所述用户当前的睡眠分期阶段进行睡眠干预。
进一步地，所述可信度顺序获取单元，包括：
准确率获取模块，用于获取每种所述生理数据在所述对应的睡眠分期阶段中的准确率；
顺序确定模块，用于根据所述准确率确定所述每种生理数据的可信度顺序。
进一步地，所述睡眠分期阶段判断单元，包括：
第一判定模块，用于在可信度最大的生理数据的准确率大于预设值时，根据所述可信度最大的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段；
第二判定模块，用于在可信度最大的生理数据的准确率不大于预设值时，根据预设排序内的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段。
进一步地，所述睡眠管理系统还包括：
记录单元，用于记录所述用户当前所处的睡眠分期阶段以及与所述睡眠分期阶段对应的时间段信息；
显示单元，用于在所述用户整个睡眠过程结束后，显示所述用户整个睡眠过程的睡眠分期阶段及每个睡眠分期阶段的时间段信息。
进一步地，所述睡眠干预单元，包括：
条件获取模块，用于获取预设条件，根据所述预设条件判断是否允许对所述用户当前的睡眠分期阶段进行干预；
干预模块，用于在允许对所述用户当前的睡眠分期阶段进行干预时，触发预设的干预事项。
所述终端设备3可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述终端设备可包括，但不仅限于，处理器30、存储器31。本领域技术人员可以理解，图3仅仅是终端设备3的示例，并不构成对终端设备3的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述终端设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器30可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器 (Digital Signal Processor，DSP)、专用集成电路 (Application Specific Integrated Circuit，ASIC)、现成可编程门阵列 (Field-Programmable Gate Array，FPGA) 或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器31可以是所述终端设备3的内部存储单元，例如终端设备3的硬盘或内存。所述存储器31也可以是所述终端设备3的外部存储设备，例如所述终端设备3上配备的插接式硬盘，智能存储卡（Smart Media Card, SMC），安全数字（Secure Digital, SD）卡，闪存卡（Flash Card）等。进一步地，所述存储器31还可以既包括所述终端设备3的内部存储单元也包括外部存储设备。所述存储器31用于存储所述计算机程序以及所述终端设备所需的其他程序和数据。所述存储器31还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器（ROM，Read-Only Memory）、随机存取存储器（RAM，Random Access Memory）、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括是电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (12)

  1. 一种睡眠管理方法，其特征在于，所述睡眠管理方法包括：
    用户处于睡眠状态时，获取所述用户的多于一种的生理数据，并提取每种所述生理数据的特征信息；
    根据所述特征信息初步判断每种所述生理数据对应的睡眠分期阶段；
    在每种所述生理数据对应的睡眠分期阶段不一致时，获取每种所述生理数据的可信度顺序；
    根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段；
    根据所述用户当前的睡眠分期阶段进行睡眠干预。
  2. 如权利要求1所述的睡眠管理方法，其特征在于，所述获取每种所述生理数据的可信度顺序，包括：
    获取每种所述生理数据在所述对应的睡眠分期阶段中的准确率；
    根据所述准确率确定所述每种生理数据的可信度顺序。
  3. 如权利要求2所述的睡眠管理方法，其特征在于，所述根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段，包括：
    在可信度最大的生理数据的准确率大于预设值时，根据所述可信度最大的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段；
    在可信度最大的生理数据的准确率不大于预设值时，根据预设排序内的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段。
  4. 如权利要求1所述的睡眠管理方法，其特征在于，在所述根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段之后，包括：
    记录所述用户当前所处的睡眠分期阶段以及与所述睡眠分期阶段对应的时间段信息；
    在所述用户整个睡眠过程结束后，显示所述用户整个睡眠过程的睡眠分期阶段及每个睡眠分期阶段的时间段信息。
  5. 如权利要求1所述的睡眠管理方法，其特征在于，所述根据所述用户当前的睡眠分期阶段进行睡眠干预，包括：
    获取预设条件，根据所述预设条件判断是否允许对所述用户当前的睡眠分期阶段进行干预；
    在允许对所述用户当前的睡眠分期阶段进行干预时，触发预设的干预事项。
  6. 一种睡眠管理系统，其特征在于，所述睡眠管理系统包括：
    数据获取单元，用于在用户处于睡眠状态时，获取所述用户的多于一种的生理数据，并提取每种所述生理数据的特征信息；
    初步判断单元，用于根据所述特征信息初步判断每种所述生理数据对应的睡眠分期阶段；
    可信度顺序获取单元，用于在每种所述生理数据对应的睡眠分期阶段不一致时，获取每种所述生理数据的可信度顺序；
    睡眠分期阶段判定单元，用于根据所述可信度顺序确定所述用户当前所处的睡眠分期阶段；
    睡眠干预单元，用于根据所述用户当前的睡眠分期阶段进行睡眠干预。
  7. 如权利要求6所述的睡眠管理系统，其特征在于，所述睡眠分期阶段判定单元，包括：
    第一判定模块，用于在可信度最大的生理数据的准确率大于预设值时，根据所述可信度最大的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段；
    第二判定模块，用于在可信度最大的生理数据的准确率不大于预设值时，根据预设排序内的生理数据对应的睡眠分期阶段确定所述用户当前所处的睡眠分期阶段。
  8. 如权利要求6所述的睡眠管理系统，其特征在于，所述睡眠干预单元，包括：
    条件获取模块，用于获取预设条件，根据所述预设条件判断是否允许对所述用户当前的睡眠分期阶段进行干预；
    干预模块，用于在允许对所述用户当前的睡眠分期阶段进行干预时，触发预设的干预事项。
  9. 如权利要求6所述的睡眠管理系统，其特征在于，所述可信度顺序获取单元，包括：
    准确率获取模块，用于获取每种所述生理数据在所述对应的睡眠分期阶段中的准确率；
    顺序确定模块，用于根据所述准确率确定所述每种生理数据的可信度顺序。
  10. 如权利要求6所述的睡眠管理系统，其特征在于，所述睡眠管理系统还包括：
    记录单元，用于记录所述用户当前所处的睡眠分期阶段以及与所述睡眠分期阶段对应的时间段信息；
    显示单元，用于在所述用户整个睡眠过程结束后，显示所述用户整个睡眠过程的睡眠分期阶段及每个睡眠分期阶段的时间段信息。
  11. 一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至5任一项所述方法的步骤。
  12. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至5任一项所述方法的步骤。
PCT/CN2018/084634 2017-08-15 2018-04-26 一种睡眠管理方法、系统及终端设备 WO2019033787A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710696734.1 2017-08-15
CN201710696734.1A CN107595245B (zh) 2017-08-15 2017-08-15 一种睡眠管理方法、系统及终端设备

Publications (1)

Publication Number Publication Date
WO2019033787A1 true WO2019033787A1 (zh) 2019-02-21

Family

ID=61065040

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/084634 WO2019033787A1 (zh) 2017-08-15 2018-04-26 一种睡眠管理方法、系统及终端设备

Country Status (2)

Country Link
CN (1) CN107595245B (zh)
WO (1) WO2019033787A1 (zh)

Families Citing this family (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107595245B (zh) * 2017-08-15 2020-07-31 深圳创达云睿智能科技有限公司 一种睡眠管理方法、系统及终端设备
CN108671359A (zh) * 2018-03-26 2018-10-19 亘冠智能技术（杭州）有限公司 睡眠辅助方法
CN108652592A (zh) * 2018-05-31 2018-10-16 广东小天才科技有限公司 一种睡眠检测的方法、装置及终端设备
CN109192311A (zh) * 2018-08-17 2019-01-11 贵州优品睡眠健康产业有限公司 睡眠评估方法、装置、终端设备及存储介质
CN109222950B (zh) * 2018-10-19 2021-08-06 深圳和而泰数据资源与云技术有限公司 数据处理方法及装置
GB2579820B (en) * 2018-12-14 2023-07-12 Acurable Ltd Methods of and apparatus for measuring physiological parameters
CN109350826A (zh) * 2018-12-17 2019-02-19 珠海中科先进技术研究院有限公司 一种助眠及防呼吸暂停装置
CN109464130B (zh) * 2019-01-09 2021-11-09 浙江强脑科技有限公司 睡眠辅助方法、系统及可读存储介质
CN112006652B (zh) * 2019-05-29 2024-02-02 深圳市睿心由科技有限公司 睡眠状态检测方法和系统
CN112205964A (zh) * 2019-07-11 2021-01-12 京东方科技集团股份有限公司 睡眠干预设备和睡眠干预管理系统
CN111358448A (zh) * 2020-03-23 2020-07-03 珠海格力电器股份有限公司 一种睡眠调节方法及装置
CN111631682B (zh) * 2020-04-23 2023-06-20 深圳赛安特技术服务有限公司 基于去趋势分析的生理特征集成方法、装置和计算机设备
CN113746708B (zh) * 2020-05-28 2024-04-16 青岛海尔智能技术研发有限公司 电器配置方法、装置、智能家居系统和计算机设备
CN111726272B (zh) * 2020-05-29 2024-04-16 青岛海尔智能技术研发有限公司 睡眠过程中的干预设备的控制方法和智能家居系统
CN111880423B (zh) * 2020-07-21 2021-07-30 上海交通大学 晨间唤醒方法及系统
CN112190419A (zh) * 2020-09-11 2021-01-08 深圳数联天下智能科技有限公司 一种睡眠管理的方法及装置
CN112487235A (zh) * 2020-11-27 2021-03-12 珠海格力电器股份有限公司 音频资源的播放方法和装置、智能终端和存储介质
CN114967897A (zh) * 2021-02-19 2022-08-30 深圳市万普拉斯科技有限公司 一种功耗优化方法、装置及移动终端
CN113018635B (zh) * 2021-03-08 2023-07-14 恒大新能源汽车投资控股集团有限公司 车辆用户睡眠智能唤醒方法及装置
CN115670398A (zh) * 2021-07-27 2023-02-03 华为技术有限公司 生理参数检测方法及装置
CN114159036A (zh) * 2021-12-03 2022-03-11 中国人民解放军海军特色医学中心 一种改善深海环境下睡眠质量的睡垫及其控制方法
CN113974575B (zh) * 2021-12-16 2023-06-16 珠海格力电器股份有限公司 睡眠分期方法、装置、电子设备及存储介质
CN114469005B (zh) * 2022-02-17 2023-10-24 珠海格力电器股份有限公司 睡眠状态的监测方法及其装置、计算机可读存储介质
CN115956884B (zh) * 2023-02-14 2023-06-06 浙江强脑科技有限公司 一种睡眠状态和睡眠分期的监测方法、装置及终端设备
CN116999024B (zh) * 2023-05-26 2024-07-16 荣耀终端有限公司 生理参数检测方法、电子设备、存储介质及程序产品

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050209512A1 (en) * 2004-03-16 2005-09-22 Heruth Kenneth T Detecting sleep
US20070115133A1 (en) * 2005-11-17 2007-05-24 Siemens Vdo Automotive Method of evaluating the state of alertness of a vehicle driver
JP2010148575A (ja) * 2008-12-24 2010-07-08 Toyota Motor Corp 睡眠段階判定装置及び睡眠段階判定方法
CN102716539A (zh) * 2012-07-10 2012-10-10 邓云龙 一种睡眠介导的心理生理干预方法
CN104955385A (zh) * 2013-01-29 2015-09-30 皇家飞利浦有限公司 增大睡眠分阶的准确性的感官刺激
CN107595245A (zh) * 2017-08-15 2018-01-19 深圳创达云睿智能科技有限公司 一种睡眠管理方法、系统及终端设备

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105748070A (zh) * 2016-04-26 2016-07-13 深圳市思立普科技有限公司 一种提高睡眠质量的睡眠干预方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050209512A1 (en) * 2004-03-16 2005-09-22 Heruth Kenneth T Detecting sleep
US20070115133A1 (en) * 2005-11-17 2007-05-24 Siemens Vdo Automotive Method of evaluating the state of alertness of a vehicle driver
JP2010148575A (ja) * 2008-12-24 2010-07-08 Toyota Motor Corp 睡眠段階判定装置及び睡眠段階判定方法
CN102716539A (zh) * 2012-07-10 2012-10-10 邓云龙 一种睡眠介导的心理生理干预方法
CN104955385A (zh) * 2013-01-29 2015-09-30 皇家飞利浦有限公司 增大睡眠分阶的准确性的感官刺激
CN107595245A (zh) * 2017-08-15 2018-01-19 深圳创达云睿智能科技有限公司 一种睡眠管理方法、系统及终端设备

Also Published As

Publication number Publication date
CN107595245A (zh) 2018-01-19
CN107595245B (zh) 2020-07-31

Similar Documents

Publication Publication Date Title
WO2019033787A1 (zh) 一种睡眠管理方法、系统及终端设备
Wang et al. Detection of Sleep Apnea from Single‐Lead ECG Signal Using a Time Window Artificial Neural Network
CN104720748B (zh) 一种睡眠阶段确定方法和系统
CN110801221A (zh) 基于无监督特征学习的睡眠呼吸暂停片段检测方法及设备
US11406323B2 (en) Method and system for monitoring sleep quality
CN103006182B (zh) 家用睡眠呼吸暂停综合症的初步检测系统
CN104822316B (zh) 用于从脑电图确定睡眠/觉醒状态的概率以及睡眠和觉醒的质量的方法和软件
CN111657948B (zh) 一种睡眠呼吸状态的检测方法、装置及设备
Yue et al. Deep learning for diagnosis and classification of obstructive sleep apnea: A nasal airflow-based multi-resolution residual network
CN116636817B (zh) 一种麻醉深度评估方法、系统、装置和存储介质
CN106618560A (zh) 脑电波信号的处理方法和装置
CN109833031A (zh) 一种基于lstm利用多生理信号的自动睡眠分期方法
WO2021208656A1 (zh) 睡眠风险预测方法、装置和终端设备
WO2016168979A1 (zh) 一种生命体征分析方法与系统
US10373714B1 (en) Determination of bed-time duration using wearable sensors
CN109009004A (zh) 一种基于中医脉象分析的体质检测方法
Cao et al. Multi-task feature fusion network for Obstructive Sleep Apnea detection using single-lead ECG signal
US20180203009A1 (en) Device, system and method for managing treatment of an inflammatory autoimmune disease of a person
CN110179436A (zh) 一种鼾声取样方法及终端设备
Xie et al. EarSpiro: Earphone-based Spirometry for Lung Function Assessment
Fang et al. Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel‐Scale Features in Home Healthcare
CN116048250A (zh) 基于可穿戴设备的睡眠管理方法与装置
Li et al. Constructing an effective model for mental stress detection with small–scale analysis
CN113729732A (zh) 基于eeg信号的睡眠质量监测系统及方法
US20240197201A1 (en) Remote Monitoring of Respiration

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18846405

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205 DATED 10.09.2020)

122 Ep: pct application non-entry in european phase

Ref document number: 18846405

Country of ref document: EP

Kind code of ref document: A1