Monitoring system for physiological parameters during sleep

Abstract

The invention relates to the technical field of sleep monitoring, and discloses a sleep period physiological parameter monitoring system which comprises a data monitoring unit, a data compensation module, a sleep monitoring module and a monitoring awakening unit. The data monitoring unit is used for monitoring the skin conductivity, the muscle activity performance, the blood oxygen content, the electroencephalogram activity, the electrocardio activity and the blood pressure activity of a monitored person in real time and feeding back the monitored data to the monitoring awakening unit and the control unit; and the control unit is used for controlling the range of data compensation and monitoring the start and stop of the wake-up unit. According to the invention, the physiological data of the monitored person is monitored, and awakening is carried out according to the condition that the set alarm time interval coincides with the optimal awakening time of the monitored person instead of completely adopting the set alarm when awakening is carried out after each time of monitoring; the wake-up design can ensure that the brain of the monitored person is stable, so that the monitored person can be tested for a long time.

Description

Technical Field

[0001] The present invention relates to the technical field of sleep monitoring, and in particular to a monitoring system for physiological parameters during sleep. Background Art

[0002] The monitoring system of physiological parameters during sleep is used to track and record various physiological indicators of people during sleep in real time. These systems are usually used in clinical diagnosis, research, sleep quality assessment, sleep disorder diagnosis, etc. The monitoring system of physiological parameters during sleep can comprehensively record and analyze multiple physiological indicators, help detect sleep quality, identify potential sleep disorders, and play an important role in medical research, clinical diagnosis and health management. It is critical to choose the right monitoring system according to the application scenario and needs.

[0003] The existing patent discloses a sleep monitoring method and a sleep monitoring system (CN116098578A). The sleep monitoring method is applied to a monitoring system for monitoring the vital signs of a patient, including: obtaining a monitoring signal of the monitored object; displaying at least one physiological parameter information of the monitored object based on the monitoring signal; extracting one or more sleep-related characteristic information of one or more monitoring signals. In the technology disclosed in the patent, after the monitored person is tested, the set alarm is often used to wake up the person. If the monitored person is awakened by the alarm for a long time, it is very easy for the monitored person to develop a physiological emergency phenomenon, which is not conducive to long-term physiological monitoring of the monitored person. Summary of the invention

[0004] The main technical problem solved by the present invention is to provide a monitoring system for physiological parameters during sleep, which solves the problems in the above-mentioned background technology.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, a system for monitoring physiological parameters during sleep includes a data monitoring unit, a control unit, a data compensation module, a sleep monitoring module, and a monitoring and awakening unit; The data monitoring unit is used to monitor the skin conductivity, muscle activity performance, blood oxygen content, brain electrical activity, heart electrical activity and blood pressure activity of the monitored person in real time, and feed the monitored data back to the monitoring wake-up unit and the control unit; The control unit is used to control the range of data compensation and monitor the start and stop of the wake-up unit; The sleep monitoring module is used to receive feedback data from the data monitoring unit and the data compensation module, and analyze whether the monitored person is in a non-rapid eye movement sleep state and whether the monitored person is in a state of muscle relaxation and stable body movement according to the feedback data, so as to determine whether the monitored person is in the best wake-up time; The monitoring and awakening unit specifically includes a time setting module, a wake-up judgment module and an alarm module, wherein the monitoring and awakening unit is used to determine whether to wake up the monitored person according to the set time according to the analysis of the sleep monitoring module.

[0006] Furthermore, the data monitoring unit specifically includes a body monitoring module and a heart and brain monitoring module; The body monitoring module monitors the activity of the autonomic nervous system during sleep through skin conductivity, evaluates the degree of muscle tension and whether muscle activity occurs during sleep through muscle activity monitoring, and detects the oxygen level in the blood through blood oxygen content; The heart and brain monitoring module monitors whether the patient is in a non-rapid eye movement sleep state through EEG activity, monitors changes in heart rate in a non-rapid eye movement sleep state through EKG activity, and monitors blood pressure fluctuations during sleep in a non-rapid eye movement sleep state through blood pressure activity.

[0007] Furthermore, the data compensation module optimizes the compensation of the instantaneous data collected by the sleep monitoring module according to the compensation data of the set monitoring range size and the weight of each component data in the range data, so:

[0008] In the formula, It indicates the data finally received by the sleep monitoring module after compensation by the data compensation module. represents the data measured for the i-th time within the range of data compensation, represents the weight of the i-th measurement data within the range of data compensation, represents the data sample range used as compensation, It represents the sum of data within the data sample range, and has:

[0009] In the formula, It represents the standard deviation of the i-th measurement within the range of data compensation.

[0010] Furthermore, the time setting module is used to receive instructions from the control unit to set the wake-up time for the monitored person; The wake-up judgment module is used to judge whether the best wake-up time is within 40 minutes before the set time. If the best wake-up time is within 40 minutes before the set time, the monitored person is woken up; if the best wake-up time is not within 40 minutes before the set time, the monitored person is woken up according to the set time; The alarm module is used to emit a sound to wake up the monitored person.

[0011] Furthermore, the sleep monitoring module determines the probability coefficient of the monitored person being in non-rapid eye movement sleep state according to the proportion of brain waves with a frequency of 0.5-4 Hz per unit time, the size of the heart rate after stabilization, and the drop in blood pressure, then:

[0012] In the formula, Indicates the probability coefficient that the monitored person is in non-rapid eye movement sleep state, It indicates the proportion of brain waves with a frequency of 0.5-4 Hz per unit time. Indicates the size of the heart rate after it becomes stable. Indicates the degree of decrease in blood pressure.

[0013] Furthermore, the sleep monitoring module determines the probability coefficient of the monitored person being in a state of muscle relaxation and stable body movement according to the resistance of the unit skin after conduction, the proportion of 0.7-3.7 Hz electromyogram in muscle activity monitoring per unit time, and the blood oxygen saturation per unit time, then:

[0014] In the formula, It indicates the probability coefficient that the monitored person is in a state of muscle relaxation and stable body movement. Indicates the resistance of the unit skin after conduction. It represents the proportion of 0.7-3.7 Hz electromyogram in muscle activity monitoring per unit time. Indicates the blood oxygen saturation per unit time.

[0015] Furthermore, the sleep monitoring module determines the probability coefficient of the monitored person being in the optimal wake-up time according to the probability coefficient of the monitored person being in the non-rapid eye movement sleep state and the probability coefficient of the monitored person being in the muscle relaxation and body movement stable state, then:

[0016] In the formula, Indicates the probability coefficient that the monitored person is currently at the best wake-up time, Indicates the probability coefficient that the monitored person is in non-rapid eye movement sleep state, It indicates the probability coefficient that the monitored person is in a state of muscle relaxation and stable body movement.

[0017] Furthermore, when When , it means that the monitored person is currently in the best wake-up time, and the monitored person can be woken up after the monitoring and wake-up unit confirms that it is in the wake-up time; when When , it means that the current monitored person is not in the best wake-up time, and the monitored person cannot be woken up even if the monitoring and wake-up unit confirms that it is in the wake-up time.

[0018] Beneficial effects: 1. The present invention monitors the physiological data of the monitored person, and wakes up the person after each monitoring not entirely by the set alarm, but by waking up the person when the set alarm time interval coincides with the best wake-up time of the monitored person. This wake-up design can ensure the brain stability of the monitored person, which is conducive to long-term testing of the monitored person.

[0019] 2. The present invention determines whether the monitored person is currently in the best wake-up time through the probability coefficient of the monitored person being in the non-rapid eye movement sleep state and the probability coefficient of the monitored person being in the muscle relaxation and stable body movement state. This wake-up design can reduce brain stimulation to the monitored person.

[0020] 3. The present invention compensates and optimizes the original sample data by setting a controllable sample compensation range for the original data and by using the weights of all data within the sample compensation range and their corresponding data. This optimization design can ensure the characteristics of the original data while also ensuring the stability of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the present invention; Figure 2 It is a monitoring schematic diagram of the present invention; Figure 3 a represents the sampling graph without compensation, b represents the sample data histogram without compensation, c represents the sampling graph within the compensation range of N, and d represents the sample data histogram after compensation; Figure 4 It is a schematic diagram of the model of D, h and c in the present invention. DETAILED DESCRIPTION

[0022] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Example 1 like Figure 1- As shown in Figure 4, according to one aspect of the present invention, a system for monitoring physiological parameters during sleep is provided, including a data monitoring unit, a control unit, a data compensation module, a sleep monitoring module and a monitoring wake-up unit; the data monitoring unit is used to monitor the skin conductivity, muscle activity performance, blood oxygen content, and EEG activity, ECG activity and blood pressure activity of the monitored person in real time, and feed back the monitored data to the monitoring wake-up unit and the control unit; the control unit is used to control the range of data compensation and the start and stop of the monitoring wake-up unit; the sleep monitoring module is used to receive feedback data from the data monitoring unit and the data compensation module, and analyze whether the monitored person is in a non-rapid eye movement sleep state and whether the monitored person is in a state of muscle relaxation and stable body movement based on the feedback data, so as to determine whether the monitored person is in an optimal wake-up time; the monitoring wake-up unit specifically includes a time setting module, a wake-up judgment module and an alarm module, wherein the monitoring wake-up unit is used to determine whether to wake up the monitored person according to the set time based on the analysis of the sleep monitoring module. By monitoring the physiological data of the monitored person, and waking up after each monitoring not entirely by the set alarm, but by waking up when the set alarm time interval coincides with the monitored person's optimal wake-up time, this wake-up design can ensure the monitored person's brain stability, which is conducive to long-term testing of the monitored person.

[0024] In this embodiment, the data monitoring unit specifically includes a body monitoring module and a heart-brain monitoring module; the body monitoring module monitors the activity of the autonomic nervous system during sleep through skin conductivity, evaluates the degree of muscle tension and whether muscle activity occurs during sleep through muscle activity monitoring, and detects the oxygen level in the blood through blood oxygen content; the heart-brain monitoring module monitors whether the person is in a non-rapid eye movement sleep state through EEG activity, monitors the changes in heart rate during non-rapid eye movement sleep through EEG activity, and monitors the blood pressure fluctuations during sleep during non-rapid eye movement sleep through blood pressure activity. Whether the monitored person is currently in the best wake-up time is determined by the probability coefficient of the monitored person being in a non-rapid eye movement sleep state and the probability coefficient of the monitored person being in a state of muscle relaxation and stable body movement. This wake-up design can reduce brain stimulation to the monitored person.

[0025] Example 2 like Figure 3 As shown in c and d, the data compensation module optimizes the instantaneous data collected by the sleep monitoring module according to the compensation data of the set monitoring range size and the weight of each component data in the range data, so:

[0026] In the formula, It indicates the data finally received by the sleep monitoring module after compensation by the data compensation module. represents the data measured for the i-th time within the range of data compensation, represents the weight of the i-th measurement data within the range of data compensation, represents the data sample range used as compensation, It represents the sum of data within the data sample range, and has:

[0027] In the formula, represents the standard deviation of the ith measurement within the range of data compensation. Figure 3 a is to directly sample the original data) to set a controllable sample compensation range (such as Figure 3 c is to set the compensation range of N to sample the original data), and the original sample data is compensated and optimized through the weights of all data within the sample compensation range and their corresponding data ( Figure 3 d is Figure 3 c optimized output data), this optimization design can ensure the characteristics of the original data while also ensuring the stability of the data.

[0028] Example 3 The time setting module is used to receive instructions from the control unit to set the wake-up time for the monitored person; the wake-up judgment module is used to judge whether the best wake-up time is within 40 minutes before the set time. If the best wake-up time is within 40 minutes before the set time, the monitored person is woken up; if the best wake-up time is not within 40 minutes before the set time, the person is woken up according to the set time; the alarm module is used to make a sound to wake up the monitored person.

[0029] The sleep monitoring module determines the probability coefficient of the monitored person being in non-rapid eye movement sleep based on the proportion of brain waves with a frequency of 0.5-4 Hz per unit time, the size of the heart rate after stabilization, and the drop in blood pressure. Then:

[0030] In the formula, Indicates the probability coefficient that the monitored person is in non-rapid eye movement sleep state, It indicates the proportion of brain waves with a frequency of 0.5-4 Hz per unit time. Indicates the size of the heart rate after it becomes stable. Indicates the degree of decrease in blood pressure.

[0031] Among them, the physiological data of the monitored person X is monitored. The proportion of brain waves with a frequency of 0.5-4 Hz in a unit time is taken (Percentage = duration of brain wave frequency between 0.5-4 Hz within 180 seconds ÷ 180 seconds).

[0032] After the heart rate stabilizes, (times / minute). The drop in blood pressure is (Decline = the drop in blood pressure at this moment ÷ the drop in blood pressure before sleep). Then we have:

[0033] From the above calculation, we can know that after the monitored person X has his physiological data monitored, the probability coefficient of the monitored person being in the non-rapid eye movement sleep state is , and compare multiple groups of data, there are: From the above calculation, we can know that as the monitored person is in non-rapid eye movement sleep state, the probability coefficient The value is getting bigger and bigger, and the number of eye movements of the monitored persons is getting less and less.

[0034] The sleep monitoring module determines the probability coefficient of the monitored person being in a state of muscle relaxation and stable body movement based on the resistance of the unit skin conduction, the proportion of 0.7-3.7 Hz electromyography in muscle activity monitoring per unit time, and the blood oxygen saturation per unit time. Then, we have:

[0035] In the formula, It indicates the probability coefficient that the monitored person is in a state of muscle relaxation and stable body movement. Indicates the resistance of the unit skin after conduction. It represents the proportion of 0.7-3.7 Hz electromyogram in muscle activity monitoring per unit time. Indicates the blood oxygen saturation per unit time.

[0036] The physiological data of the monitored person X is monitored. The resistance of the unit skin after conduction is (Kilohms, monitoring the skin of the monitored person at an interval of 10 cm).

[0037] The proportion of 0.7-3.7 Hz electromyogram in muscle activity monitoring per unit time (Percentage = duration of electromyography at 0.7-3.7 Hz within 180 seconds ÷ 180 seconds). Blood oxygen saturation per unit time , then we have:

[0038] From the above calculation, we can know that after the monitored person X has his physiological data monitored, the probability coefficient of the monitored person being in a state of muscle relaxation and stable body movement is , and compare multiple groups of data, there are: From the above table data, we can know that as the monitored person is in a state of muscle relaxation and stable body movement, the probability coefficient The value is getting bigger and bigger, and the number of limb activities and muscle movements of the monitored persons is getting less and less.

[0039] like Figure 4 As shown, the sleep monitoring module determines the probability coefficient of the monitored person being in the optimal wake-up time based on the probability coefficient of the monitored person being in the non-rapid eye movement sleep state and the probability coefficient of the monitored person being in the muscle relaxation and body movement stable state, then:

[0040] In the formula, Indicates the probability coefficient that the monitored person is currently at the best wake-up time, Indicates the probability coefficient that the monitored person is in non-rapid eye movement sleep state, It indicates the probability coefficient that the monitored person is in a state of muscle relaxation and stable body movement.

[0041] Among them, the physiological data of the monitored person X is monitored. The probability coefficient of the monitored person being in the non-rapid eye movement sleep state is , the probability coefficient of the monitored person being in a state of muscle relaxation and stable body movement is . Then we have:

[0042] From the above calculation, we can know that the probability coefficient of the monitored person being in the best wake-up time is , then the monitored person is currently at the best wake-up time, and the monitored person can be awakened after the monitoring and awakening unit confirms that the person is within the wake-up time. Comparing multiple sets of data can have: From the above calculation, we can know that when the sample data tends to infinity, the probability coefficient D will have a dividing line, which can determine whether to wake up the monitored person. The dividing line is 76%, so when When , it means that the monitored person is currently in the best wake-up time. After the monitoring and wake-up unit confirms that it is within the wake-up time, the monitored person can be woken up; when When , it means that the current monitored person is not in the best wake-up time, and the monitored person cannot be woken up even if the monitoring and wake-up unit confirms that it is in the wake-up time.

[0043] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A system for monitoring physiological parameters during sleep, characterized in that: It includes a data monitoring unit, a control unit, a data compensation module, a sleep monitoring module and a monitoring wake-up unit; The data monitoring unit is used to monitor the skin conductivity, muscle activity performance, blood oxygen content, brain electrical activity, heart electrical activity and blood pressure activity of the monitored person in real time, and feed the monitored data back to the monitoring wake-up unit and the control unit; The control unit is used to control the range of data compensation and monitor the start and stop of the wake-up unit; The sleep monitoring module is used to receive feedback data from the data monitoring unit and the data compensation module, and analyze whether the monitored person is in a non-rapid eye movement sleep state and whether the monitored person is in a state of muscle relaxation and stable body movement according to the feedback data, so as to determine whether the monitored person is in the best wake-up time; The monitoring and awakening unit specifically includes a time setting module, a wake-up judgment module and an alarm module, wherein the monitoring and awakening unit is used to determine whether to wake up the monitored person according to the set time according to the analysis of the sleep monitoring module.

2. The system for monitoring physiological parameters during sleep according to claim 1, characterized in that: The data monitoring unit specifically includes a body monitoring module and a heart and brain monitoring module; The body monitoring module monitors the activity of the autonomic nervous system during sleep through skin conductivity, evaluates the degree of muscle tension and whether muscle activity occurs during sleep through muscle activity monitoring, and detects the oxygen level in the blood through blood oxygen content; The heart and brain monitoring module monitors whether the patient is in a non-rapid eye movement sleep state through EEG activity, monitors changes in heart rate in a non-rapid eye movement sleep state through EKG activity, and monitors blood pressure fluctuations during sleep in a non-rapid eye movement sleep state through blood pressure activity.

3. The system for monitoring physiological parameters during sleep according to claim 1, characterized in that: The data compensation module compensates and optimizes the instantaneous data collected by the sleep monitoring module according to the compensation data of the set monitoring range size and the weight of each component data in the range data, so: In the formula, It indicates the data finally received by the sleep monitoring module after compensation by the data compensation module. represents the data measured for the i-th time within the range of data compensation, represents the weight of the i-th measurement data within the range of data compensation, represents the data sample range used as compensation, It represents the sum of data within the data sample range, and has: In the formula, It represents the standard deviation of the i-th measurement within the range of data compensation.

4. The system for monitoring physiological parameters during sleep according to claim 1, characterized in that: The time setting module is used to receive instructions from the control unit to set the wake-up time for the monitored person; The wake-up judgment module is used to judge whether the best wake-up time is within 40 minutes before the set time. If the best wake-up time is within 40 minutes before the set time, the monitored person is woken up; if the best wake-up time is not within 40 minutes before the set time, the monitored person is woken up according to the set time; The alarm module is used to emit a sound to wake up the monitored person.

5. The system for monitoring physiological parameters during sleep according to claim 1, characterized in that: The sleep monitoring module determines the probability coefficient of the monitored person being in non-rapid eye movement sleep state according to the proportion of brain waves with a frequency of 0.5-4 Hz per unit time, the size of the heart rate after stabilization, and the drop in blood pressure. Then: In the formula, Indicates the probability coefficient that the monitored person is in non-rapid eye movement sleep state, It indicates the proportion of brain waves with a frequency of 0.5-4 Hz per unit time. Indicates the size of the heart rate after it becomes stable. Indicates the degree of decrease in blood pressure.

6. The system for monitoring physiological parameters during sleep according to claim 5, characterized in that: The sleep monitoring module determines the probability coefficient of the monitored person being in a state of muscle relaxation and stable body movement according to the resistance of the unit skin after conduction, the proportion of 0.7-3.7 Hz electromyogram in muscle activity monitoring per unit time, and the blood oxygen saturation per unit time, so: In the formula, It indicates the probability coefficient that the monitored person is in a state of muscle relaxation and stable body movement. Indicates the resistance of the unit skin after conduction. It represents the proportion of 0.7-3.7 Hz electromyogram in muscle activity monitoring per unit time. Indicates the blood oxygen saturation per unit time.

7. The system for monitoring physiological parameters during sleep according to claim 6, characterized in that: The sleep monitoring module determines the probability coefficient of the monitored person being in the optimal wake-up time according to the probability coefficient of the monitored person being in the non-rapid eye movement sleep state and the probability coefficient of the monitored person being in the muscle relaxation and body movement stable state, then: In the formula, Indicates the probability coefficient that the monitored person is currently at the best wake-up time, Indicates the probability coefficient that the monitored person is in non-rapid eye movement sleep state, It indicates the probability coefficient that the monitored person is in a state of muscle relaxation and stable body movement.

8. The system for monitoring physiological parameters during sleep according to claim 7, characterized in that: when When , it means that the monitored person is currently in the best wake-up time, and the monitored person can be woken up after the monitoring and wake-up unit confirms that it is in the wake-up time; when When , it means that the current monitored person is not in the best wake-up time, and the monitored person cannot be woken up even if the monitoring and wake-up unit confirms that it is in the wake-up time.

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