Sleep quality analysis system based on human body HRV measurement

Abstract

The invention discloses a sleep quality analysis system based on human body HRV measurement, and relates to the technical field of sleep quality, a preliminary analysis module can quickly judge the sleep quality fluctuation condition of a user through calculation of a heart rate variability factor HRV, when the HRV value exceeds a preset threshold value, the system automatically sends a sleep quality fluctuation command to a cloud platform, and the sleep quality fluctuation command is sent to the cloud platform. Therefore, the change of the sleep state is timely responded. Besides, the neural activity module analyzes the balance state of parasympathetic nerves and sympathetic nerves and constructs a neural activity proportion Jhb, the activity deviation of the nervous system of the user in the sleep state can be determined, and a reliable basis is provided for deep analysis of the sleep state. The sleep state comprehensive analysis module is further combined with the heart rate recovery ratio Xhb and the neural activity ratio Jhb, a sleep quality evaluation index Sgzs is calculated through the trained sleep quality prediction model, and quantitative evaluation of the sleep quality of the user is further achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of sleep quality, and in particular to a sleep quality analysis system based on human HRV measurement. Background Art

[0002] As people's demand for personalized health management increases, sleep monitoring has gradually become one of the important research directions of modern health technology. As a physiological parameter, human HRV (heart rate variability) can reflect the activity of the cardiac autonomic nervous system, especially the balance between the sympathetic and parasympathetic nerves. By analyzing HRV, we can gain a deep understanding of the health status and neural activity of the human body during sleep, and thus provide a reliable basis for evaluating sleep quality.

[0003] There are still some deficiencies in the current analysis of the impact of physiological parameters such as human HRV on sleep quality. Most existing sleep monitoring systems rely on simple sleep duration and the distribution of sleep stages. They lack the evaluation of the deep relationship between autonomic nervous system activity and sleep quality, lack real-time monitoring and feedback of HRV fluctuations and neural activity status, and easily ignore subtle changes in sleep quality, resulting in inaccurate and individuated evaluation results. In addition, many systems only focus on HRV changes, while ignoring the importance of parameters such as heart rate recovery and sleep status, making it difficult to provide sufficient feedback and compensation measures for the user's overall sleep health status. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a sleep quality analysis system based on human HRV measurement, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a sleep quality analysis system based on human HRV measurement, including a data monitoring module, a preliminary analysis module, a neural activity module, a sleep state comprehensive analysis module and a feedback module;

[0006] The data monitoring module is used to monitor the health status of the user according to a plurality of groups of monitoring instruments to obtain relevant physiological data, relevant electrical activity data and relevant personalized data of the user during sleep;

[0007] The preliminary analysis module is used to generate a heart rate variability factor HRV based on relevant physiological data, and if the heart rate variability factor HRV exceeds a preset threshold, a sleep quality fluctuation command is issued to the cloud platform;

[0008] The neural activity module is used to analyze the balance between the parasympathetic nerves and the sympathetic nerves according to the relevant electrical activity data after receiving the sleep quality fluctuation command, so as to construct the neural activity ratio Jhb, and determine the neural activity bias of the current user in the sleep state based on the neural activity ratio Jhb;

[0009] The sleep state comprehensive analysis module is used to construct a heart rate recovery ratio Xhb according to relevant personalized data, and combine it with the neural activity ratio Jhb and the trained sleep quality prediction model to obtain the sleep quality assessment index Sgzs;

[0010] The feedback module is used to pre-set an evaluation threshold Q, and compare and analyze the sleep quality evaluation index Sgzs with the evaluation threshold Q to comprehensively estimate the quality status of the current user's sleep, and take corresponding compensation measures according to the quality status.

[0011] Preferably, the data monitoring module includes a deployment unit, a first collection unit, a second collection unit and a third collection unit;

[0012] The deployment unit is used to deploy several groups of monitoring instruments around the user before the user falls asleep, wherein the several groups of monitoring instruments include a health bracelet, an electrocardiogram sensor, a respiratory monitoring sensor, an accelerometer, a camera and a gyroscope;

[0013] The first acquisition unit is used to monitor and record relevant physiological data of the user in a sleeping state in real time, wherein the relevant physiological data includes the RR interval Rjj and the breathing difference Hxc in each monitoring period;

[0014] The second acquisition unit is used to monitor and record relevant electrical activity data of the user in a sleeping state in real time, wherein the relevant electrical activity data includes the power of the heart rate variability signal in a high frequency range and the power of the heart rate variability signal in a low frequency range;

[0015] The third acquisition unit is used to monitor and record relevant personalized data of the user in the sleep state in real time, wherein the relevant personalized data includes the maximum heart rate value Xz in the sleep state max , the heart rate value Xz(t) when entering the recovery state after the maximum heart rate value, and the resting heart rate value Xz in the sleep state rest , the number of times the body posture changes during sleep Hzsc, the total sleep duration Zssc and the number of times you wake up during sleep Jxcs.

[0016] Preferably, the preliminary analysis module includes an HRV analysis unit and a preliminary judgment unit;

[0017] The HRV analysis unit extracts the RR intervals Rjj in each monitoring period from the relevant physiological data, and uses a statistical algorithm to obtain the heart rate variability factor HRV. The heart rate variability factor HRV is specifically obtained in the following manner:

[0018]

[0019] Where N is the number of RR intervals, i = 1, 2, 3, ..., N, Rjj i Represented as the i-th RR interval, Rjj avg Expressed as the mean RR interval.

[0020] Preferably, the preliminary judgment unit is used to pre-set a threshold value, and compare the heart rate variability factor HRV with the set threshold value to preliminarily judge the heart rate fluctuation difference of the current user in the sleep state, and the specific judgment content is as follows:

[0021] If the heart rate variability factor HRV exceeds the set threshold, it will be preliminarily determined that the current user's heart rate fluctuation in the sleeping state is in a normal state;

[0022] If the heart rate variability factor HRV does not exceed the set threshold, it will be preliminarily determined that the heart rate fluctuation of the current user in the sleeping state is not in a normal state, and a sleep quality fluctuation command will be issued to the cloud platform.

[0023] Preferably, the neural activity module includes a frequency domain analysis unit and a neural bias selection unit;

[0024] The frequency domain analysis unit is used to analyze the balance state between the parasympathetic nerves and the sympathetic nerves according to the relevant electrical activity data, so as to obtain the power of the heart rate variability signal in the high frequency range and the power of the heart rate variability signal in the low frequency range, and record them as HF power P1 and LF power P2 respectively. The specific acquisition method is as follows:

[0025]

[0026] Wherein, Ppz(f) represents the power spectrum density; the integration range of HF power P1 is the high frequency range, i.e., 0.15 Hz-0.4 Hz; the integration range of LF power P2 is the low frequency range, i.e., 0.04 Hz-0.15 Hz; and f represents the frequency.

[0027] Preferably, the neural bias selection unit is used to calculate the neural activity ratio Jhb according to the HF power P1 and the LF power P2, and the neural activity ratio Jhb is obtained by the following formula:

[0028]

[0029] Based on the value of the neural activity ratio Jhb, the neural activity bias of the current user in the sleep state is judged. The specific judgment content is as follows:

[0030] If the nerve activity ratio Jhb>1, it is judged that the parasympathetic nerve activity is dominant in the current user's sleep state;

[0031] If the nerve activity ratio Jhb<1, it is judged that the sympathetic nerve activity of the current user is dominant in the sleep state;

[0032] If the nerve activity ratio Jhb=1, it is determined that the sympathetic nerve activity and the parasympathetic nerve activity of the current user in the sleep state are in a relatively stable state.

[0033] Preferably, the sleep state comprehensive analysis module includes a recovery ability unit, a change unit and a comprehensive analysis unit;

[0034] The recovery capacity unit is used to construct a heart rate recovery ratio Xhb according to relevant personalized data, which is specifically obtained by the following formula:

[0035]

[0036] In the formula, Xz max Expressed as the maximum heart rate value in the sleeping state, Xz rest It is represented as the resting heart rate value in the sleeping state, and Xz(t) is represented as the heart rate value when entering the recovery state t after the maximum heart rate value.

[0037] Preferably, the change unit is used to obtain the body position change rate Tbz and the sleep fragmentation rate Spz respectively according to the relevant personalized data, which are specifically obtained by the following formula:

[0038]

[0039] Where Hzsc represents the number of times the body posture changes during sleep, Jxcs represents the number of times the body wakes up during sleep, and Zssc represents the total sleep duration.

[0040] Preferably, the comprehensive analysis unit is used to construct a sleep quality prediction model using a convolutional neural network, and input the neural activity ratio Jhb, the body position change rate Tbz, the sleep fragmentation rate Spz and the heart rate recovery ratio Xhb into the sleep quality prediction model, and after linear normalization processing, the sleep quality assessment index Sgzs is obtained by fitting, which is specifically obtained by the following method:

[0041]

[0042] Wherein, Hxc represents the respiratory difference, a1, a2, a3, a4 and a5 represent the weight values ​​of the heart rate recovery ratio Xhb, the neural activity ratio Jhb, the sleep fragmentation rate Spz, the body position change rate Tbz and the respiratory difference Hxc, respectively, and V represents the correction constant.

[0043] Preferably, the feedback module includes a comparison unit and a compensation unit;

[0044] The comparison unit is used to pre-set an evaluation threshold Q, and compare and analyze the evaluation threshold Q with the sleep quality evaluation index Sgzs to comprehensively estimate the quality of the current user's sleep. The specific contents are as follows:

[0045] If the sleep quality evaluation index Sgzs exceeds the evaluation threshold Q, it is estimated that the current user's sleep quality is in a normal state;

[0046] If the sleep quality evaluation index Sgzs does not exceed the evaluation threshold Q, it is estimated that the current user's sleep quality is not in a normal state;

[0047] The compensation unit is used to take corresponding compensation measures according to the corresponding sleep quality obtained in the comparison unit, and the specific contents are as follows:

[0048] If the sleep state is normal, the user will be advised to maintain the current sleep environment and be prompted to do some light relaxation activities before going to bed, including meditation, deep breathing exercises and stretching exercises.

[0049] If it is not in a normal state, it will help users regulate sympathetic and parasympathetic nervous activities, and guide users to meditate, progressive muscle relaxation and deep breathing exercises, while reminding users to adjust their work and rest time and maintain a regular sleep schedule.

[0050] The present invention provides a sleep quality analysis system based on human HRV measurement, which has the following beneficial effects:

[0051] (1) The data monitoring module can obtain the user's physiological data, electrical activity data and personalized data through a variety of monitoring instruments to fully capture physiological changes during sleep; the preliminary analysis module can quickly determine the user's sleep quality fluctuations by calculating the heart rate variability factor HRV. When the HRV value exceeds the preset threshold, the system automatically sends a sleep quality fluctuation command to the cloud platform, thereby responding to changes in sleep status in a timely manner. In addition, the neural activity module analyzes the balance between the parasympathetic and sympathetic nerves and constructs the neural activity ratio Jhb, which can clarify the activity bias of the user's nervous system during sleep and provide a reliable basis for in-depth analysis of sleep status. The comprehensive analysis module of sleep status further combines the heart rate recovery ratio Xhb and the neural activity ratio Jhb, and calculates the sleep quality evaluation index Sgzs through the trained sleep quality prediction model, further realizing the quantitative evaluation of the user's sleep quality. Finally, the feedback module can quickly determine whether the user's sleep state is normal by comparing Sgzs with the preset evaluation threshold Q, and provide personalized compensation measures when the sleep quality is poor, such as adjusting the sleep environment and providing relaxation training, to help users improve their sleep quality. Through this system, the accuracy and personalization of sleep monitoring can be effectively improved, and sleep quality problems can be discovered and fed back in a timely manner, thereby helping users better manage and improve their own sleep health status, which has significant practical application value.

[0052] (2) Through the setting of the preliminary analysis module, the user's heart rate fluctuation during sleep can be quickly and accurately judged. The HRV analysis unit can extract the RR interval of each monitoring period from the relevant physiological data, and calculate the heart rate variability factor HRV using a statistical algorithm. This HRV factor not only fully reflects the user's heart rate fluctuation through an accurate calculation formula based on the number of RR intervals and their mean, but also provides a direct evaluation of the activity of the autonomic nervous system, especially the balance between the sympathetic and parasympathetic nerves.

[0053] (3) The neural bias selection unit calculates the neural activity ratio Jhb based on the HF power and the LF power, so as to further analyze the neural activity bias of the user in the sleep state. Specifically, if the neural activity ratio Jhb is greater than 1, it indicates that the parasympathetic nerve activity is dominant, which may mean that the user is in a better state of relaxation and has a higher sleep quality; if the neural activity ratio Jhb is less than 1, the sympathetic nerve activity is dominant, which may indicate that the user is in a state of tension or stress, which may affect the sleep quality; if the neural activity ratio Jhb is equal to 1, it indicates that the sympathetic and parasympathetic nerve activities are relatively balanced, and the user's neural state is in a relatively stable state. This analysis capability enables the system to understand the state of the user's nervous system during sleep in detail, which helps to timely discover and adjust factors that may affect sleep quality, thereby providing users with personalized sleep optimization suggestions. Through precise neural activity monitoring and analysis, the present invention can more effectively support sleep health management and improve the user's sleep quality and overall quality of life.

[0054] (4) The calculation result of the heart rate recovery ratio can reveal the user's heart rate recovery after sleep, providing a deep insight into sleep quality and helping to evaluate the impact of sleep on heart health; the change unit calculates the body position change rate and sleep fragmentation rate through further analysis of personalized data; the calculation of the body position change rate involves the number of changes in body posture during sleep, and the sleep fragmentation rate reflects the number of awakenings during sleep and the total sleep duration. These indicators can reveal the frequency of body position adjustments during sleep and the continuity of sleep, and thus reflect the stability and depth of sleep. A higher body position change rate and sleep fragmentation rate may indicate poor sleep quality, insufficient sleep or disturbance. The comprehensive analysis unit integrates these data to provide a multi-faceted sleep quality assessment that can reveal the user's sleep recovery ability and changes during sleep. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The present invention is a block diagram of a sleep quality analysis system based on human HRV measurement. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Example 1

[0058] See also Figure 1, the present invention provides a sleep quality analysis system based on human HRV measurement, including a data monitoring module, a preliminary analysis module, a neural activity module, a sleep state comprehensive analysis module and a feedback module;

[0059] The data monitoring module is used to monitor the health status of the user according to a plurality of groups of monitoring instruments to obtain relevant physiological data, relevant electrical activity data and relevant personalized data of the user during sleep;

[0060] The preliminary analysis module is used to generate a heart rate variability factor HRV based on relevant physiological data, and if the heart rate variability factor HRV exceeds a preset threshold, a sleep quality fluctuation command is issued to the cloud platform;

[0061] The neural activity module is used to analyze the balance between the parasympathetic nerves and the sympathetic nerves according to the relevant electrical activity data after receiving the sleep quality fluctuation command, so as to construct the neural activity ratio Jhb, and determine the neural activity bias of the current user in the sleep state based on the neural activity ratio Jhb;

[0062] The sleep state comprehensive analysis module is used to construct a heart rate recovery ratio Xhb according to relevant personalized data, and combine it with the neural activity ratio Jhb and the trained sleep quality prediction model to obtain the sleep quality assessment index Sgzs;

[0063] The feedback module is used to pre-set an evaluation threshold Q, and compare and analyze the sleep quality evaluation index Sgzs with the evaluation threshold Q to comprehensively estimate the quality status of the current user's sleep, and take corresponding compensation measures according to the quality status.

[0064] During the operation of this system, the system can realize accurate monitoring, evaluation and feedback of the user's sleep quality through the collaborative work of multiple modules. First, the data monitoring module of the system can obtain the user's physiological data, electrical activity data and personalized data during sleep through multiple monitoring instruments, ensuring the comprehensiveness and multi-dimensionality of data collection, and providing a reliable basis for subsequent analysis. The preliminary analysis module effectively captures the quality fluctuations during sleep by generating HRV factors in real time and issuing sleep quality fluctuation commands when HRV exceeds the preset threshold, and has the ability to respond in time. Secondly, the neural activity module constructs the neural activity ratio Jhb by analyzing the balance between the parasympathetic and sympathetic nerves, and accurately judges the user's neural activity bias. This analysis can carefully reflect the activity of the user's autonomic nervous system in the sleep state, thus laying the foundation for personalized evaluation. The comprehensive analysis module of sleep state further combines personalized data to construct the heart rate recovery ratio Xhb, and generates the sleep quality evaluation index Sgzs by fitting the trained sleep quality prediction model, further realizing a comprehensive quantitative evaluation of the user's sleep quality. Finally, the feedback module compares the sleep quality assessment index Sgzs with the pre-set assessment threshold Q, and can accurately determine the current user's sleep quality status and take corresponding compensation measures. This not only allows personalized adjustments and improvements based on actual sleep conditions, but also provides timely feedback to users to help them improve their sleep quality. This multi-level systematic approach based on physiological data, neural activity and personalized analysis has maximized the accuracy and effectiveness of sleep quality assessment, providing an innovative solution for sleep health management.

[0065] Example 2

[0066] Please refer to Figure 1 , specifically: the data monitoring module includes a deployment unit, a first collection unit, a second collection unit and a third collection unit;

[0067] The deployment unit is used to deploy several groups of monitoring instruments around the user before the user falls asleep, wherein the several groups of monitoring instruments include a health bracelet, an electrocardiogram sensor, a respiratory monitoring sensor, an accelerometer, a camera and a gyroscope;

[0068] The first acquisition unit is used to monitor and record relevant physiological data of the user in a sleeping state in real time, wherein the relevant physiological data includes the RR interval Rjj and the breathing difference Hxc in each monitoring period;

[0069] The second acquisition unit is used to monitor and record relevant electrical activity data of the user in a sleeping state in real time, wherein the relevant electrical activity data includes the power of the heart rate variability signal in a high frequency range and the power of the heart rate variability signal in a low frequency range;

[0070] The third acquisition unit is used to monitor and record relevant personalized data of the user in the sleep state in real time, wherein the relevant personalized data includes the maximum heart rate value Xz in the sleep state max , the heart rate value Xz(t) when entering the recovery state after the maximum heart rate value, and the resting heart rate value Xz in the sleep state rest , the number of times the body posture changes during sleep Hzsc, the total sleep duration Zssc and the number of times you wake up during sleep Jxcs.

[0071] The above-mentioned RR interval Rjj is mainly obtained through an electrocardiogram (ECG) sensor. The ECG sensor can accurately measure the time interval between each heart beat, that is, the RR interval. The sensor can be an electrode attached to the skin or a high-precision sensor embedded in a wearable device;

[0072] The respiratory difference Hxc is mainly measured by a respiratory monitoring sensor or a chest strap (such as a strap with a strain gauge on the chest). The respiratory sensor records the breathing rate and depth, and then calculates the respiratory difference. It can also be obtained through the internal sensors of advanced sleep monitoring equipment.

[0073] Heart rate values ​​are usually obtained by a photoplethysmography (PPG) sensor, which measures heart rate by detecting tiny optical changes in blood vessels under the skin. This sensor is commonly found in health bracelets and smart watches.

[0074] The number of times the body posture changes during sleep is usually detected by accelerometers and gyroscopes. The accelerometer monitors the changes in body acceleration, and the gyroscope detects the rotation of the posture. The combination of the two can determine the changes in body posture. These sensors are commonly found in smart watches, health bracelets and specialized sleep monitoring devices.

[0075] The number of awakenings during sleep is monitored and acquired through a camera;

[0076] Power spectral density is mainly obtained by the built-in analysis function of electrocardiogram (ECG) monitoring instruments or sleep monitoring equipment. Power spectral density is a part of frequency domain analysis.

[0077] In this embodiment, the deployment unit can pre-deploy several groups of monitoring instruments (such as health bracelets) before the user falls asleep, further ensuring sufficient coverage and stability of the monitoring environment. The first acquisition unit can collect and record the user's physiological data in real time, including the RR interval Rjj and the breathing difference, ensuring continuous monitoring of the heart and respiratory system activities, and providing detailed time series data for subsequent sleep quality analysis. The second acquisition unit can accurately analyze the activity state of the user's sympathetic and parasympathetic nerves by collecting the power of the heart rate variability signal in the high-frequency and low-frequency ranges, which is of great significance for evaluating the balance of the user's autonomic nervous system and the depth of sleep. The third acquisition unit focuses on the user's personalized data monitoring, including key parameters such as the maximum heart rate value, heart rate recovery value, and resting heart rate value, as well as behavioral data such as the number of changes in body posture during sleep, total sleep time, and the number of wake-ups, providing a comprehensive observation and record of the user's overall sleep process. Through these multi-dimensional data collection, the present invention can more accurately evaluate the user's sleep quality, and provide customized feedback and improvement suggestions by analyzing the correlation between these data. This system can not only monitor physiological and electrical activity data, but also combine the user's personalized characteristics, further improving the accuracy and reliability of sleep quality assessment, and helping users optimize their sleep health.

[0078] Example 3

[0079] Please refer to Figure 1 ,Specifically: the preliminary analysis module includes an HRV analysis unit and a preliminary judgment unit;

[0080] The HRV analysis unit extracts the RR intervals Rjj in each monitoring period from the relevant physiological data, and uses a statistical algorithm to obtain the heart rate variability factor HRV. The heart rate variability factor HRV is specifically obtained in the following manner:

[0081]

[0082] Where N is the number of RR intervals, i = 1, 2, 3, ..., N, Rjj i Represented as the i-th RR interval, Rjj avg Expressed as the average RR interval; further reflects the overall heart rate fluctuation;

[0083] The preliminary judgment unit is used to preset a threshold value and compare the heart rate variability factor HRV with the set threshold value to preliminarily judge the heart rate fluctuation difference of the current user in the sleep state. The specific judgment content is as follows:

[0084] If the heart rate variability factor HRV exceeds the set threshold, it will be preliminarily determined that the current user's heart rate fluctuation in the sleeping state is in a normal state;

[0085] If the heart rate variability factor HRV does not exceed the set threshold, it will be preliminarily determined that the heart rate fluctuation of the current user in the sleeping state is not in a normal state, and a sleep quality fluctuation command will be issued to the cloud platform.

[0086] In this embodiment, the beneficial effect of the present invention is that through the design of the preliminary analysis module, the real-time analysis and preliminary judgment of the heart rate fluctuation of the user in the sleep state can be realized. Specifically, the HRV analysis unit can extract the RR interval Rjj of each period from the monitored physiological data, and calculate the heart rate variability factor HRV using a statistical algorithm. The HRV factor can reflect the fluctuation of the overall heart rate, which provides an important basis for analyzing the autonomic nervous system activity of the user in the sleep state. Further, the preliminary judgment unit compares the calculated HRV factor with the preset threshold by setting a threshold, and issues a sleep quality fluctuation command according to the comparison result, providing a timely feedback mechanism for subsequent analysis and intervention. Through this mechanism, the system can not only quickly respond to the heart rate fluctuation of the user during sleep, but also judge potential problems in the early stage, and initiate further sleep quality analysis and improvement plans. This preliminary judgment function based on HRV analysis provides users with real-time and accurate sleep monitoring and early warning, helping users to better manage sleep health.

[0087] Example 4

[0088] Please refer to Figure 1 ,Specifically: the neural activity module includes a frequency domain analysis unit and a neural bias selection unit;

[0089] The frequency domain analysis unit is used to analyze the balance state between the parasympathetic nerves and the sympathetic nerves according to the relevant electrical activity data, so as to obtain the power of the heart rate variability signal in the high frequency range and the power of the heart rate variability signal in the low frequency range, and record them as HF power P1 and LF power P2 respectively. The specific acquisition method is as follows:

[0090]

[0091] Wherein, Ppz(f) represents the power spectrum density; the integration range of HF power P1 is the high frequency range, i.e., 0.15 Hz-0.4 Hz; the integration range of LF power P2 is the low frequency range, i.e., 0.04 Hz-0.15 Hz; and f represents the frequency.

[0092] The neural bias selection unit is used to calculate the neural activity ratio Jhb according to the HF power P1 and the LF power P2. The neural activity ratio Jhb is obtained by the following formula:

[0093]

[0094] Based on the value of the neural activity ratio Jhb, the neural activity bias of the current user in the sleep state is judged. The specific judgment content is as follows:

[0095] If the nerve activity ratio Jhb>1, it is judged that the parasympathetic nerve activity is dominant in the current user's sleep state;

[0096] If the nerve activity ratio Jhb<1, it is judged that the sympathetic nerve activity of the current user is dominant in the sleep state;

[0097] If the nerve activity ratio Jhb=1, it is determined that the sympathetic nerve activity and the parasympathetic nerve activity of the current user in the sleep state are in a relatively stable state.

[0098] In this embodiment, through the design of the neural activity module, the user's autonomic nervous system activity in the sleep state can be effectively analyzed, and the balance between the sympathetic and parasympathetic nerves can be determined. Through the frequency domain analysis unit, the system can perform detailed frequency domain analysis on the heart rate variability signal in the high frequency range and the low frequency range, and calculate the HF power and LF power respectively. This separation calculation method of high and low frequency power enables the activities of the sympathetic and parasympathetic nerves in the autonomic nervous system to be accurately distinguished, providing detailed neural activity data. In addition, the neural bias selection unit quantifies the ratio as a bias indicator of nervous system activity through the neural activity ratio Jhb formula. According to the numerical value of the neural activity ratio Jhb, the system can automatically determine the dominant direction of the user's neural activity in the sleep state. When the neural activity ratio Jhb>1, it means that the parasympathetic nerve activity is dominant, which means that the user is in a deep state of relaxation, which is conducive to deep sleep; and when Jhb<1, the sympathetic nerve is dominant, which means that the user may be in a shallow sleep or even a potential stress state. In addition, when Jhb=1, it indicates that the sympathetic and parasympathetic nerves are in a balanced state, which is a relatively stable state of nerve activity. Through this mechanism, the present invention can not only monitor the user's nerve activity state in real time, but also provide a basis for subsequent sleep quality assessment and provide reliable guidance for further sleep intervention and optimization.

[0099] Example 5

[0100] Please refer to Figure 1 ,Specifically: the sleep state comprehensive analysis module includes a recovery ability unit, a change unit and a comprehensive analysis unit;

[0101] The recovery capacity unit is used to construct a heart rate recovery ratio Xhb according to relevant personalized data, which is specifically obtained by the following formula:

[0102]

[0103] In the formula, Xz max Expressed as the maximum heart rate value in the sleeping state, Xz rest It is represented as the resting heart rate value in the sleeping state, and Xz(t) is represented as the heart rate value when entering the recovery state t after the maximum heart rate value. This formula is used to quantify the degree of heart rate recovery after exercise.

[0104] The change unit is used to obtain the body position change rate Tbz and the sleep fragmentation rate Spz according to the relevant personalized data, which are specifically obtained by the following formula:

[0105]

[0106] Where Hzsc represents the number of times the body posture changes during sleep, Jxcs represents the number of times the body wakes up during sleep, and Zssc represents the total sleep duration.

[0107] In this embodiment, the recovery ability unit evaluates the user's recovery ability in the sleep state by calculating the heart rate recovery ratio Xhb. The calculation formula of this ratio takes into account the maximum heart rate value, the resting heart rate value, and the heart rate value when entering the recovery state after the maximum heart rate value, thereby providing a comprehensive understanding of the user's heart rate recovery ability. By evaluating the recovery ability, the system can identify whether the user has effectively recovered during sleep, thereby judging their sleep quality and health status. Secondly, the change unit further analyzes the sleep quality by obtaining the body position change rate Tbz and the sleep fragmentation rate Spz. The calculation of the body position change rate Tbz takes into account the number of changes in body posture during sleep and the total sleep time, reflecting the user's activity level during sleep. The sleep fragmentation rate Spz evaluates the continuity and stability of the user's sleep by calculating the ratio of the number of wake-ups during sleep to the total sleep time. The comprehensive analysis of these indicators helps to understand the user's sleep fragmentation and the impact of body position changes on sleep quality. By comprehensively analyzing these data, the system can provide a more accurate sleep quality assessment, and can identify and adjust factors that may affect sleep quality, thereby providing users with personalized improvement suggestions and optimizing their sleep health management. This multi-dimensional data analysis capability enables the present invention to more effectively support the scientific assessment and improvement of sleep quality, and improve the overall health level and quality of life of users.

[0108] Example 6

[0109] Please refer to Figure 1Specifically: the comprehensive analysis unit is used to construct a sleep quality prediction model using a convolutional neural network, and input the neural activity ratio Jhb, the body position change rate Tbz, the sleep fragmentation rate Spz and the heart rate recovery ratio Xhb into the sleep quality prediction model. After linear normalization, the sleep quality evaluation index Sgzs is obtained by fitting, which is specifically obtained by the following method:

[0110]

[0111] Wherein, Hxc is represented by the respiratory difference, a1, a2, a3, a4 and a5 are respectively represented by the heart rate recovery ratio Xhb, the neural activity ratio Jhb, the sleep fragmentation rate Spz, the body position change rate Tbz and the weight value of the respiratory difference Hxc, wherein, 0<a1≤1, 0<a2≤1, 0<a3≤1, 0<a4≤1, 0<a5≤1, and a1+a2+a3+a4+a5=1, and V is represented by the correction constant.

[0112] The breathing difference Hxc refers to the difference between the maximum breathing depth and the minimum breathing depth of the user during sleep.

[0113] The feedback module includes a comparison unit and a compensation unit;

[0114] The comparison unit is used to pre-set an evaluation threshold Q, and compare and analyze the evaluation threshold Q with the sleep quality evaluation index Sgzs to comprehensively estimate the quality of the current user's sleep. The specific contents are as follows:

[0115] If the sleep quality evaluation index Sgzs exceeds the evaluation threshold Q, it is estimated that the current user's sleep quality is in a normal state;

[0116] If the sleep quality evaluation index Sgzs does not exceed the evaluation threshold Q, it is estimated that the current user's sleep quality is not in a normal state;

[0117] The compensation unit is used to take corresponding compensation measures according to the corresponding sleep quality obtained in the comparison unit, and the specific contents are as follows:

[0118] If the sleep quality is normal, some mild maintenance compensation measures will be adopted to help users maintain good sleep quality and prevent the decline of sleep quality. Users will be advised to maintain the current sleeping environment, such as light, noise, temperature and humidity, to ensure continuous good sleep. At the same time, users will be prompted to do mild relaxation activities before going to bed, including meditation, deep breathing exercises and stretching exercises, to promote better physical relaxation and prevent potential decline in sleep quality caused by daily fatigue.

[0119] If it is not in a normal state, some more active intervention measures will be taken to help users improve their current sleep quality and solve problems that affect sleep. At this time, users will be helped to adjust to reduce sympathetic nerve activity and increase parasympathetic nerve activity, and reduce stress by guiding users to meditate, progressive muscle relaxation and deep breathing exercises to promote a better state of physical relaxation and prevent potential decline in sleep quality due to daily fatigue. At the same time, users are reminded to adjust their work and rest time, maintain a regular sleep schedule, avoid staying up late or irregular sleep time, and promote the stability of the biological clock.

[0120] In this embodiment, the comprehensive analysis unit uses a convolutional neural network (CNN) to construct an efficient sleep quality prediction model, taking the neural activity ratio, body position change rate, sleep fragmentation rate and heart rate recovery ratio as inputs, and obtains a sleep quality assessment index through linear normalization. This method can comprehensively consider multiple physiological indicators and provide a more accurate and comprehensive sleep quality assessment. This quantitative assessment method can better reflect the user's true sleep state and provide a scientific basis for subsequent feedback measures. The feedback module of the system includes a comparison unit and a compensation unit. The comparison unit compares the sleep quality assessment index with the assessment threshold by setting an assessment threshold Q, and can accurately judge the user's sleep quality state. For users with normal sleep quality, the system will recommend maintaining the current good sleeping environment and provide mild maintenance compensation measures, such as meditation, deep breathing exercises, etc. These suggestions help maintain the user's good sleep state and prevent further decline in sleep quality. For users whose sleep quality does not meet the standard, the system will take more active intervention measures. These measures include reducing sympathetic nerve activity, increasing parasympathetic nerve activity, guiding users to meditate, progressive muscle relaxation and deep breathing exercises. Through these measures, users' stress can be effectively reduced, and physical relaxation can be promoted, thereby improving sleep quality. In addition, the system will also recommend users to adjust their work and rest schedules, maintain regular sleep habits, and avoid staying up late or irregular sleep time, which is of great significance for improving the stability of the biological clock and overall sleep quality. Through accurate sleep quality assessment and personalized feedback measures, this system can not only improve users' sleep quality, but also help users develop healthy living habits, thereby improving the overall health level of users. The intervention measures and suggestions provided by the system can effectively relieve users' stress and anxiety, improve the sleeping environment, and ultimately achieve long-term maintenance of healthy sleep.

[0121] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sleep quality analysis system based on human HRV measurement, characterized in that: It includes data monitoring module, preliminary analysis module, neural activity module, sleep state comprehensive analysis module and feedback module; The data monitoring module is used to monitor the health status of the user according to a plurality of groups of monitoring instruments to obtain relevant physiological data, relevant electrical activity data and relevant personalized data of the user during sleep; The preliminary analysis module is used to generate a heart rate variability factor HRV based on relevant physiological data, and if the heart rate variability factor HRV exceeds a preset threshold, a sleep quality fluctuation command is issued to the cloud platform; The neural activity module is used to analyze the balance between the parasympathetic nerves and the sympathetic nerves according to the relevant electrical activity data after receiving the sleep quality fluctuation command, so as to construct the neural activity ratio Jhb, and determine the neural activity bias of the current user in the sleep state based on the neural activity ratio Jhb; The sleep state comprehensive analysis module is used to construct a heart rate recovery ratio Xhb according to relevant personalized data, and combine it with the neural activity ratio Jhb and the trained sleep quality prediction model to obtain the sleep quality assessment index Sgzs; The feedback module is used to pre-set an evaluation threshold Q, and compare and analyze the sleep quality evaluation index Sgzs with the evaluation threshold Q to comprehensively estimate the quality status of the current user's sleep, and take corresponding compensation measures according to the quality status.

2. A sleep quality analysis system based on human HRV measurement according to claim 1, characterized in that: The data monitoring module includes a deployment unit, a first collection unit, a second collection unit and a third collection unit; The deployment unit is used to deploy several groups of monitoring instruments around the user before the user falls asleep, wherein the several groups of monitoring instruments include a health bracelet, an electrocardiogram sensor, a respiratory monitoring sensor, an accelerometer, a camera and a gyroscope; The first acquisition unit is used to monitor and record relevant physiological data of the user in a sleeping state in real time, wherein the relevant physiological data includes the RR interval Rjj and the breathing difference Hxc in each monitoring period; The second acquisition unit is used to monitor and record relevant electrical activity data of the user in a sleeping state in real time, wherein the relevant electrical activity data includes the power of the heart rate variability signal in a high frequency range and the power of the heart rate variability signal in a low frequency range; The third acquisition unit is used to monitor and record relevant personalized data of the user in the sleep state in real time, wherein the relevant personalized data includes the maximum heart rate value Xz in the sleep state max , the heart rate value Xz(t) when entering the recovery state after the maximum heart rate value, and the resting heart rate value Xz in the sleep state rest , the number of times the body posture changes during sleep Hzsc, the total sleep duration Zssc and the number of times you wake up during sleep Jxcs.

3. A sleep quality analysis system based on human HRV measurement according to claim 2, characterized in that: The preliminary analysis module includes an HRV analysis unit and a preliminary judgment unit; The HRV analysis unit extracts the RR intervals Rjj in each monitoring period from the relevant physiological data, and uses a statistical algorithm to obtain the heart rate variability factor HRV. The heart rate variability factor HRV is specifically obtained in the following manner: Where N is the number of RR intervals, i = 1, 2, 3, ..., N, Rjj i Represented as the i-th RR interval, Rjj avg Expressed as the mean RR interval.

4. A sleep quality analysis system based on human HRV measurement according to claim 3, characterized in that: The preliminary judgment unit is used to preset a threshold value and compare the heart rate variability factor HRV with the set threshold value to preliminarily judge the heart rate fluctuation difference of the current user in the sleep state. The specific judgment content is as follows: If the heart rate variability factor HRV exceeds the set threshold, it will be preliminarily determined that the current user's heart rate fluctuation in the sleeping state is in a normal state; If the heart rate variability factor HRV does not exceed the set threshold, it will be preliminarily determined that the heart rate fluctuation of the current user in the sleeping state is not in a normal state, and a sleep quality fluctuation command will be issued to the cloud platform.

5. The sleep quality analysis system based on human HRV measurement according to claim 2, characterized in that: The neural activity module includes a frequency domain analysis unit and a neural bias selection unit; The frequency domain analysis unit is used to analyze the balance state between the parasympathetic nerves and the sympathetic nerves according to the relevant electrical activity data, so as to obtain the power of the heart rate variability signal in the high frequency range and the power of the heart rate variability signal in the low frequency range, and record them as HF power P1 and LF power P2 respectively. The specific acquisition method is as follows: Wherein, Ppz(f) represents the power spectrum density; the integration range of HF power P1 is the high frequency range, i.e., 0.15 Hz-0.4 Hz; the integration range of LF power P2 is the low frequency range, i.e., 0.04 Hz-0.15 Hz; and f represents the frequency.

6. A sleep quality analysis system based on human HRV measurement according to claim 5, characterized in that: The neural bias selection unit is used to calculate the neural activity ratio Jhb according to the HF power P1 and the LF power P2. The neural activity ratio Jhb is obtained by the following formula: Based on the value of the neural activity ratio Jhb, the neural activity bias of the current user in the sleep state is judged. The specific judgment content is as follows: If the nerve activity ratio Jhb>1, it is judged that the parasympathetic nerve activity is dominant in the current user's sleep state; If the nerve activity ratio Jhb<1, it is judged that the sympathetic nerve activity of the current user is dominant in the sleep state; If the nerve activity ratio Jhb=1, it is determined that the sympathetic nerve activity and the parasympathetic nerve activity of the current user in the sleep state are in a relatively stable state.

7. A sleep quality analysis system based on human HRV measurement according to claim 2, characterized in that: The sleep state comprehensive analysis module includes a recovery ability unit, a change unit and a comprehensive analysis unit; The recovery capacity unit is used to construct a heart rate recovery ratio Xhb according to relevant personalized data, which is specifically obtained by the following formula: In the formula, Xz max Expressed as the maximum heart rate value in the sleeping state, Xz rest It is represented as the resting heart rate value in the sleeping state, and Xz(t) is represented as the heart rate value when entering the recovery state t after the maximum heart rate value.

8. A sleep quality analysis system based on human HRV measurement according to claim 7, characterized in that: The change unit is used to obtain the body position change rate Tbz and the sleep fragmentation rate Spz according to the relevant personalized data, which are specifically obtained by the following formula: Where Hzsc represents the number of times the body posture changes during sleep, Jxcs represents the number of times the body wakes up during sleep, and Zssc represents the total sleep duration.

9. The sleep quality analysis system based on human HRV measurement according to claim 7, characterized in that: The comprehensive analysis unit is used to construct a sleep quality prediction model using a convolutional neural network, and input the neural activity ratio Jhb, the body position change rate Tbz, the sleep fragmentation rate Spz and the heart rate recovery ratio Xhb into the sleep quality prediction model. After linear normalization, the sleep quality evaluation index Sgzs is obtained by fitting, which is specifically obtained in the following way: Wherein, Hxc represents the respiratory difference, a1, a2, a3, a4 and a5 represent the weight values ​​of the heart rate recovery ratio Xhb, the neural activity ratio Jhb, the sleep fragmentation rate Spz, the body position change rate Tbz and the respiratory difference Hxc, respectively, and V represents the correction constant.

10. The sleep quality analysis system based on human HRV measurement according to claim 1, characterized in that: The feedback module includes a comparison unit and a compensation unit; The comparison unit is used to pre-set an evaluation threshold Q, and compare and analyze the evaluation threshold Q with the sleep quality evaluation index Sgzs to comprehensively estimate the quality of the current user's sleep. The specific contents are as follows: If the sleep quality evaluation index Sgzs exceeds the evaluation threshold Q, it is estimated that the current user's sleep quality is in a normal state; If the sleep quality evaluation index Sgzs does not exceed the evaluation threshold Q, it is estimated that the current user's sleep quality is not in a normal state; The compensation unit is used to take corresponding compensation measures according to the corresponding sleep quality obtained in the comparison unit, and the specific contents are as follows: If the sleep state is normal, the user will be advised to maintain the current sleep environment and be prompted to do some light relaxation activities before going to bed, including meditation, deep breathing exercises and stretching exercises. If it is not in a normal state, it will help users regulate sympathetic and parasympathetic nervous activities, and guide users to meditate, progressive muscle relaxation and deep breathing exercises, while reminding users to adjust their work and rest time and maintain a regular sleep schedule.

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