WO2020224026A1 - 一种深度睡眠的动态监测方法 - Google Patents

一种深度睡眠的动态监测方法 Download PDF

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WO2020224026A1
WO2020224026A1 PCT/CN2019/090643 CN2019090643W WO2020224026A1 WO 2020224026 A1 WO2020224026 A1 WO 2020224026A1 CN 2019090643 W CN2019090643 W CN 2019090643W WO 2020224026 A1 WO2020224026 A1 WO 2020224026A1
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deep sleep
dynamic monitoring
value
point
variables
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王作第
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深圳六合六医疗器械有限公司
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4815—Sleep quality
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms

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  • This application relates to the measurement field of deep sleep, and more specifically to a dynamic monitoring method of deep sleep.
  • Sleep is necessary for everyone's physiology, and of course animals are also the same, and humans are also animals. Sleep can make the human body get adequate rest, adjust the body's functions, make the brain cells get enough oxygen and nourishment, and the brain nerves can get long-term relaxation.
  • the United Nations Health Organization regards "good sleep" as the top ten health standards. In view of the above problems, our company decided to study human sleep. A good deep sleep is very important for the human body. Poor sleep will seriously affect the next day's work and even affect other indicators of the body. Dynamic monitoring of sleep can enable people to manage their health scientifically. Provide users with convenient, real-time, safe and accurate services.
  • accelerometers are used in the market for statistics, which is relatively simple.
  • the general principle is that people do not move too fast during deep sleep. Therefore, there is an urgent need for a method that can make up for the deficiencies in the market and use data statistics. Calculate deep sleep.
  • the purpose of this application is to overcome the defects in the prior art and provide a dynamic monitoring method for deep sleep.
  • a dynamic monitoring method for deep sleep including the following steps:
  • Its further technical solution is: calculating the pulse with a relatively uniform probability distribution as the standard value of the variable; and calculating the stroke volume, heart rate, blood flow velocity, blood vessel radius, heartbeat interval, heartbeat interval, and vascular value based on the extracted variable standard value Peripheral resistance and the extreme point of each pulse.
  • calculation variables include the following variables calculated in sequence:
  • the comparison result uses the linear normalization method to calculate the relationship between the distribution and the slope of the variable, puts all the variable data on the coordinate system, and judges the magnitude of the slope. If the slope is greater than 1, then judge Not in deep sleep; if the slope is less than 1, it is considered that deep sleep has started.
  • the interval of deep sleep is specified as 2-2.5h, it is judged whether the duration of deep sleep falls within the interval, if it is less than this interval, it is reminded that the amount of sleep is poor; if it is greater than this interval, it is reminded that the quality of sleep is good.
  • the further technical solution is: using the value on a specific segment of integration, according to the sampling frequency, each point is 0.02s to calculate the velocity of the numerical point, and the velocity of the numerical point is inversely proportional to the blood flow velocity.
  • the blood vessel radius is a proportional coefficient based on the maximum value and the minimum value of continuous data in a period of time.
  • the velocity is obtained by observing the velocity change of a numerical point within a period of time, and after linear normalization according to multiple velocities, through the change of the slope;
  • the peripheral resistance is the value of the descending gorge and the extreme point.
  • the stroke amplitude of each heart beat is the maximum value of the selected value within a period of time.
  • the plane tangent point is a set of points Pm that can equally divide the area of both sides;
  • the difference threshold point is the maximum point and the minimum point of each heart beat;
  • the parameter T is the cardiac cycle
  • Ps is the maximum value
  • Pd is the minimum value
  • the line gap is the line connecting the extreme points of every two beats
  • the heartbeat interval is the minimum interval of every two beats.
  • the beneficial effect of the application compared with the prior art is that a dynamic monitoring method of deep sleep in this application compares the calculated variable with the standard value of the variable stored in the server, compares the results, and analyzes the duration of deep sleep. Assess and send out a message reminder that it uses data statistics to calculate deep sleep, which is a good way to make up for the lack of statistics using acceleration sensors on the market.
  • the dynamic monitoring of sleep can enable people to scientifically manage their health and provide users Provide convenient, real-time, safe and accurate services.
  • Figure 1 is a flow chart of a dynamic monitoring method for deep sleep according to this application.
  • Figure 2 is a pulse curve diagram of a dynamic monitoring method for deep sleep in this application.
  • a dynamic monitoring method for deep sleep includes the following steps:
  • the pulse with a relatively uniform probability distribution is calculated as the standard value of the variable; and the stroke volume, heart rate, blood flow velocity, blood vessel radius, heartbeat gap are calculated according to the extracted standard value of the variable , Heartbeat interval, peripheral resistance of blood vessels and extreme points of each pulse. And store the data calculated from the standard values of these variables to the server as a template for reference comparison.
  • calculating variables includes sequentially calculating the following variables:
  • the comparison result uses the linear normalization method to calculate the relationship between the distribution and the slope of the variable, put all the variable data on the coordinate system, and judge the magnitude of the slope, if the slope is greater than 1, it is determined that it is not in deep sleep; if the slope is less than 1, it is determined that deep sleep has started. That is to put all the data on the coordinate system, this straight line makes all the values distributed around a straight line, calculate and analyze the slope change of this straight line to distinguish the change of the variable. For example, if the slope is greater than 1, and the value increases, then it is not in deep sleep, and the slope is less than 1, indicating that the value is decreasing.
  • the algorithm system will recursively calculate the relationship between the distribution and slope of these variables).
  • each person’s data will be different. For example, some people have a heart rate of 50 beats per minute during deep sleep, and some people’s heart rate is 52 beats per minute. We use our own and our own data. Compare.
  • the interval of deep sleep is specified as 2-2.5h, and it is judged whether the duration of deep sleep falls into the interval, if it is less than this interval, it is reminded that the amount of sleep is poor; if it is greater than this interval, it is reminded that the quality of sleep is good.
  • the numerical value on a specific segment is integrated, and the numerical point velocity is calculated according to the sampling frequency, 0.02s per point, and the velocity of the numerical point is inversely proportional to the blood flow velocity.
  • the blood vessel radius is a proportional coefficient based on the maximum value and the minimum value of continuous data in a period of time.
  • the velocity is obtained by observing the velocity change of a numerical point within a period of time, linearly normalized according to multiple velocities, and obtained through the change of the slope;
  • the peripheral resistance is the value and the extreme value of the xiazhongxia The ratio of points;
  • the pulse amplitude of each heart beat is the maximum value of the selected value within a period of time.
  • the parameter T is the cardiac cycle
  • Ps is the maximum value
  • Pd is the minimum value
  • the line gap is the line connecting the extreme points of every two beats; the heartbeat interval is the minimal interval of every two beats.

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  • Life Sciences & Earth Sciences (AREA)
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  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
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  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)

Abstract

本申请提出了一种深度睡眠的动态监测方法，包括以下步骤：在服务器先存储一些变量标准值；采集数据，坐标上建立模型，计算变量；对计算的变量和服务器存储的变量标准值进行比较；对比对结果进行分析，进行整体分析；对深度睡眠的持续时间进行评估，并发出消息提醒。一种深度睡眠的动态监测方法通过对计算的变量和服务器存储的变量标准值进行比较，对比对结果进行分析，对深度睡眠的持续时间进行评估，并发出消息提醒，其采用数据统计的方法去计算深度睡眠，很好的弥补了市面上采用加速度传感器统计的不足，对睡眠的动态监测可以使人们科学的去管理自己的健康，为用户提供便捷、实时、安全、精准的服务。

Description

一种深度睡眠的动态监测方法
本申请是以申请号为201910386385.2，申请日为2019年5月9日的中国专利申请为基础，并主张其优先权，该申请的全部内容在此作为整体引入本申请中。
技术领域
本申请涉及深度睡眠的测定领域，更具体地说是指一种深度睡眠的动态监测方法。
背景技术
睡眠是每个人生理所必须的，当然动物亦如此，人亦是动物。睡眠能够使人体得到充分的休息，调整身体机能，使大脑细胞得到足够的氧气与养料，大脑神经得到长久的放松。我们的1/3的时间都在睡觉，随着社会的发展，电灯出现在这个世界，人们的工作时间大幅度的变长，睡眠的时间相对都减少了。联合国卫生组织把“睡眠良好”作为十大健康标准。针对以上的问题我们公司决定研究人体睡眠，一个良好的深度睡眠对人体来说十分的重要，睡眠不好会严重影响第二天的工作，甚至会影响身体的其他指标发生变化。对睡眠的动态监测可以使人们科学的去管理自己的健康。为用户提供便捷、实时、安全、精准的服务。
现在市面上都是采用加速度传感器去统计，比较单一，一般原理都是人为在深度睡眠的时候，不会动的太快，因此急需一种能弥补了市面上的不足，采用数据统计的方法来计算深度睡眠。
申请内容
本申请的目的在于克服现有技术存在的缺陷，提供一种深度睡眠的动态监测方法。
为实现上述目的，本申请采用以下技术方案：
一种深度睡眠的动态监测方法，包括以下步骤：
在服务器先存储一些变量标准值；
采集数据，坐标上建立模型，计算变量；
对计算的变量和服务器存储的变量标准值进行比较；
对比对结果进行分析，进行整体分析；
对深度睡眠的持续时间进行评估，并发出消息提醒。
其进一步技术方案为：计算概率分布比较均匀的脉搏作为变量标准值；并依据所提取的变量标准值计算出心搏出量、心率、血流速度、血管半径、心跳间隙、心跳间隔、血管的外周阻力和每次脉搏的极值点。
其进一步技术方案为：所述计算变量包括依次计算如下变量：
血流速度、血管半径、速率、外周阻力、心脏每次搏动的搏幅、平面切面点、平面切面点、差分阈值点、心搏出量、线条间隙和心跳间隔。
其进一步技术方案为：所述比对结果采用线性归化的方法计算变量的分布和斜率的关系，将所有的变量数据都放到坐标系上，判断斜率的大小，如斜率大于1，则判定不在深度睡眠；如斜率小于1，则认定深度睡眠开始。
其进一步技术方案为：所述深度睡眠的区间规定为2-2.5h，判断深度睡眠的持续时间是否落入所述区间，若小于这个区间提醒睡眠量差；若大于这个区间提醒睡眠质量良好。
其进一步技术方案为：采用积分特定段上的数值，根据采样频率，每个点0.02s，计算出数值点速度，数值点的速度反比即为血流速度。
其进一步技术方案为：所述血管半径为根据一段时间内连续数据的极大值和极小值的的比例系数。
其进一步技术方案为：所述速率是根据观察一段时间内数值点速度变化，根据多个速度线性归化之后，通过斜率的变化所得；所述外周阻力是降中峡的值与极值点的比值；所述心脏每次搏动的搏幅为选取一段时间内数值的极大值。
其进一步技术方案为：所述平面切面点为一组数据可以等分两边面积的点Pm；所述差分阈值点为心脏每一次搏动极大值点和极小值点；
所述心搏出量sv＝(0.283/(k*k))(Ps-Pd)*T
k＝(Ps-Pm)/(Ps-Pd)。
其中，参数T是心动周期，Ps是极大值，Pd是极小值。
其进一步技术方案为：所述线条间隙为每两次搏动的极值点的连线；所述心跳间隔为每两次搏动的极小值间隔。
申请与现有技术相比的有益效果是：本申请一种深度睡眠的动态监测方法通过对计算的变量和服务器存储的变量标准值进行比较，对比对结果进行分析，对深度睡眠的持续时间进行评估，并发出消息提醒，其采用数据统计的方法去计算深度睡眠，很好的弥补了市面上采用加速度传感器统计的不足，对睡眠的动态监测可以使人们科学的去管理自己的健康，为用户提供便捷、实时、安全、精准的服务。
下面结合附图和具体实施例对本申请作进一步描述。
附图说明
图1为本申请一种深度睡眠的动态监测方法的流程图；
图2为本申请一种深度睡眠的动态监测方法的脉搏曲线图。
具体实施方式
为了更充分理解本申请的技术内容，下面结合具体实施例对本申请的技术方案进一步介绍和说明，但不局限于此。
如图1和图2所示，一种深度睡眠的动态监测方法，包括以下步骤：
在服务器先存储一些变量标准值；
采集数据，坐标上建立模型，计算变量；
对计算的变量和服务器存储的变量标准值进行比较；
对比对结果进行分析，进行整体分析；
对深度睡眠的持续时间进行评估，并发出消息提醒。
通过采用数据统计的方法去计算深度睡眠，很好的弥补了市面上采用加速度传感器统计的不足，对睡眠的动态监测可以使人们科学的去管理自己的健康，为用户提供便捷、实时、安全、精准的服务。
具体地，如图1和图2所示，计算概率分布比较均匀的脉搏作为变量标准值；并依据所提取的变量标准值计算出心搏出量、心率、血流速度、血管半径、心跳间隙、心跳间隔、血管的外周阻力和每次脉搏的极值点。并将这些变量标准值计算得出的数据存储到服务器，作为参考对比的模板。
具体地，如图1和图2所示，计算变量包括依次计算如下变量：
血流速度、血管半径、速率、外周阻力、心脏每次搏动的搏幅、平面切面点、 平面切面点、差分阈值点、心搏出量、线条间隙和心跳间隔。
具体地，如图1和图2所示，比对结果采用线性归化的方法计算变量的分布和斜率的关系，将所有的变量数据都放到坐标系上，判断斜率的大小，如斜率大于1，则判定不在深度睡眠；如斜率小于1，则认定深度睡眠开始。即把所有的数据都放到坐标系上，这条直线使得所有的数值都会在一条直线的周围分布，计算分析这条直线的斜率变化来判别变量的发生的变化。比如说斜率大于1，值都增大，那么不在深度睡眠，斜率小于1，说明值在减小，当所有的变量波动不大渐趋于稳定时，我们认定深度睡眠开始，(因为每次采集数据都会保存在服务器上，算法系统会递归式的计算这些变量的分布和斜率的关系)。
具体地，因为个体差异，所以每一个人的数据都会不一样，比如说有的人在深度睡眠的时候心率就是50次/分，有的人就是52次/分，我们采取自己和自己的数据进行比对。
具体地，深度睡眠的区间规定为2-2.5h，判断深度睡眠的持续时间是否落入区间，若小于这个区间提醒睡眠量差；若大于这个区间提醒睡眠质量良好。
具体地，如图1和图2所示，采用积分特定段上的数值，根据采样频率，每个点0.02s，计算出数值点速度，数值点的速度反比即为血流速度。
具体地，如图1和图2所示，血管半径为根据一段时间内连续数据的极大值和极小值的比例系数。
具体地，如图1和图2所示，速率是根据观察一段时间内数值点速度变化，根据多个速度线性归化之后，通过斜率的变化所得；外周阻力是降中峡的值与极值点的比值；心脏每次搏动的搏幅为选取一段时间内数值的极大值。
具体地，如图1和图2所示，平面切面点为一组数据可以等分两边面积的点Pm；差分阈值点为心脏每一次搏动极大值点和极小值点；
心搏出量sv＝(0.283/(k*k))(Ps-Pd)*T
k＝(Ps-Pm)/(Ps-Pd)。
其中，参数T是心动周期，Ps是极大值，Pd是极小值。
具体地，如图1和图2所示，线条间隙为每两次搏动的极值点的连线；心跳间隔为每两次搏动的极小值间隔。
综上所述，一种深度睡眠的动态监测方法通过对计算的变量和服务器存储的变量标准值进行比较，对比对结果进行分析，对深度睡眠的持续时间进行评估，并发出消息提醒，其采用数据统计的方法去计算深度睡眠，很好的弥补了市面上采用加速度传感器统计的不足，对睡眠的动态监测可以使人们科学的去管理自己的健康，为用户提供便捷、实时、安全、精准的服务。
上述仅以实施例来进一步说明本申请的技术内容，以便于读者更容易理解，但不代表本申请的实施方式仅限于此，任何依本申请所做的技术延伸或再创造，均受本申请的保护。本申请的保护范围以权利要求书为准。
发明概述
技术问题
问题的解决方案
发明的有益效果

Claims (10)

  1. 一种深度睡眠的动态监测方法，其特征在于，包括以下步骤：
    在服务器先存储一些变量标准值；
    采集数据，坐标上建立模型，计算变量；
    对计算的变量和服务器存储的变量标准值进行比较；
    对比对结果进行分析，进行整体分析；
    对深度睡眠的持续时间进行评估，并发出消息提醒。
  2. 根据权利要求1所述的一种深度睡眠的动态监测方法，其特征在于，计算概率分布比较均匀的脉搏作为变量标准值；并依据所提取的变量标准值计算出心搏出量、心率、血流速度、血管半径、心跳间隙、心跳间隔、血管的外周阻力和每次脉搏的极值点。
  3. 根据权利要求1所述的一种深度睡眠的动态监测方法，其特征在于，所述计算变量包括依次计算如下变量：
    血流速度、血管半径、速率、外周阻力、心脏每次搏动的搏幅、平面切面点、平面切面点、差分阈值点、心搏出量、线条间隙和心跳间隔。
  4. 根据权利要求1所述的一种深度睡眠的动态监测方法，其特征在于，所述比对结果采用线性归化的方法计算变量的分布和斜率的关系，将所有的变量数据都放到坐标系上，判断斜率的大小，如斜率大于1，则判定不在深度睡眠；如斜率小于1，则认定深度睡眠开始。
  5. 根据权利要求1所述的一种深度睡眠的动态监测方法，其特征在于，所述深度睡眠的区间规定为2-2.5h，判断深度睡眠的持续时间是否落入所述区间，若小于这个区间提醒睡眠量差；若大于这个区间提醒睡眠质量良好。
  6. 根据权利要求3所述的一种深度睡眠的动态监测方法，其特征在于，采用积分特定段上的数值，根据采样频率，每个点0.02s，计算出数值点速度，数值点的速度反比即为血流速度。
  7. 根据权利要求3所述的一种深度睡眠的动态监测方法，其特征在于，所述血管半径为根据一段时间内连续数据的极大值和极小值的的比例系数。
  8. 根据权利要求3所述的一种深度睡眠的动态监测方法，其特征在于，所述速率是根据观察一段时间内数值点速度变化，根据多个速度线性归化之后，通过斜率的变化所得；所述外周阻力是降中峡的值与极值点的比值；所述心脏每次搏动的搏幅为选取一段时间内数值的极大值。
  9. 根据权利要求3所述的一种深度睡眠的动态监测方法，其特征在于，所述平面切面点为一组数据可以等分两边面积的点Pm；所述差分阈值点为心脏每一次搏动极大值点和极小值点；
    所述心搏出量sv＝(0.283/(k*k))(Ps-Pd)*T
    k＝(Ps-Pm)/(Ps-Pd)。
    其中，参数T是心动周期，Ps是极大值，Pd是极小值。
  10. 根据权利要求3所述的一种深度睡眠的动态监测方法，其特征在于，所述线条间隙为每两次搏动的极值点的连线；所述心跳间隔为每两次搏动的极小值间隔。
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