Heart rate variability analysis-based brain state monitoring method and wearable system

Abstract

The invention provides a brain power state monitoring method based on heart rate variability analysis and a wearing system, which are applied to the technical field of brain power state monitoring, and comprise the following steps: an information acquisition step: dynamically acquiring heart rate variability data of an organism in real time; and a state monitoring step: describing a curve of the heart rate variability data, and monitoring the mental state based on the obtained curve. According to the method, heart rate variability indexes are applied to dynamically collect and analyze the heart rate variability in real time, the mental and mental states of the body and even the pressure and health states of the human body are dynamically described, the fatigue state is dynamically monitored and observed in real time, and the dynamic changes of the mental state, the mental and mental states and the pressure and health states of the body of the human body are predicted; the method is real-time and accurate, can predict the change of the body, and can predict the fatigue state.

Description

Technical Field

[0001] The present application relates to the technical field of brain state monitoring, and particularly to a brain state monitoring method and a wearable system based on heart rate variability analysis. Background Art

[0002] Fluctuations in brain function state, especially decline or so-called brain fatigue, are a common physiological or pathological phenomenon, mainly manifested as obvious changes in functions such as reaction speed and memory. The decline in brain function not only affects work efficiency, but also easily causes accidental injury accidents during individual activities or production labor processes, resulting in personal injury or property and economic losses. Physical fatigue often occurs in repetitive high-physical-input labor or exercise, and it can also cause brain fatigue. Brain fatigue and physical fatigue usually accompany or occur simultaneously with each other.

[0003] There is a lack of methods for evaluating and detecting the immediate brain function state. Currently, for monitoring fatigue levels, for example, through fatigue questionnaires, by investigating individual subjective feelings to measure and evaluate fatigue levels, to a certain extent objectively describe the functional decline that has occurred in the body; however, fatigue questionnaires have subjective influencing factors, and some people may unconsciously exaggerate or downplay their feelings, thus causing a large deviation in fatigue monitoring.

[0004] Based on this, a new technical solution is needed. Summary of the Invention

[0005] In view of this, the present application provides a brain state monitoring method and a wearable system based on heart rate variability analysis.

[0006] The present application provides the following technical solutions:

[0007] According to a brain state monitoring method based on heart rate variability analysis provided by the present application, it includes the following steps:

[0008] Information acquisition step: Dynamically acquire the heart rate variability data of the body in real time;

[0009] State monitoring step: Depict the curve of the heart rate variability data, and monitor the brain state based on the obtained curve.

[0010] Preferably, in the information acquisition step, signals are collected on the body surface through electrocardiogram signals, and / or photoplethysmography signals, and / or sound pressure signals generated by heartbeats, and time domain indexes and / or frequency domain indexes of heart rate variability are calculated.

[0011] Preferably, in the information acquisition step, signals are collected through a light emitting diode via a photoelectric sensor, and the collected signals are conditioned by a low-pass filter circuit and a post-amplification circuit.

[0012] Preferably, the state monitoring step includes the following steps:

[0013] Factor definition step: Define the relationship diagram between the heart rate variability index and time, depict the curve of the heart rate variability data, and define the curve-related values;

[0014] Defined numerical analysis step: Monitor the mental state according to the defined curve-related values.

[0015] Preferably, in the factor definition step, locate multiple time periods on the curve; the curve-related values include the extreme points of the curve, and / or, the curve tangent rate, and / or, the slope between adjacent extreme points of the curve, and / or, the integral area between adjacent maximum points or adjacent minimum points;

[0016] In the defined numerical analysis step, judge the mental state according to the change of the curve tangent rate; and / or, judge the strength of the mental state through the change of the extreme points of the curve; and / or, judge the strength of the mental state through the change of the slope between adjacent extreme points of the curve; and / or, judge the strength of the mental state through the change of the integral area between adjacent maximum points or adjacent minimum points; and / or, judge the strength of the mental state through the time change of the corresponding heart rate variability index in multiple time periods; and / or, judge the strength of the mental state through the change of the heart rate variability index in the corresponding time periods of multiple time periods.

[0017] Preferably, in the defined numerical analysis step, obtain the change speed of the tangent rate as an index for predicting the mental state;

[0018] and / or, generate a first marked curve according to the heart rate variability index at the corresponding time points in multiple time periods, and use the change of the tangent rate of the first marked curve as an index for predicting the mental state;

[0019] and / or, generate a second marked curve according to the slope, and use the change of the tangent rate of the second marked curve as an index for predicting the mental state;

[0020] and / or, generate a third marked curve according to the integral value of the integral area, and use the tangent rate of the third marked curve as an index for predicting the mental state;

[0021] and / or, generate a fourth marked curve based on the degree of time change of the corresponding heart rate variability index in multiple time periods, and use the tangent rate of the fourth marked curve as an index for predicting the mental state;

[0022] and / or, generate a fifth marked curve through the degree of change of the heart rate variability index in the corresponding time periods of multiple time periods, and use the tangent rate of the fifth marked curve as an index for predicting the mental state.

[0023] A brain state monitoring wearable system based on heart rate variability analysis provided by the present invention further includes the following modules:

[0024] Information acquisition module: Dynamically acquire the heart rate variability data of the body in real time;

[0025] State monitoring module: Depict the curve of the heart rate variability data and monitor the brain state based on the obtained curve.

[0026] Preferably, in the information acquisition module, signals are collected on the body surface through electrocardiogram signals, and / or photoplethysmography signals, and / or sound pressure signals generated by heartbeats, and time domain indexes and / or frequency domain indexes of heart rate variability are calculated.

[0027] Preferably, in the information acquisition module, signals are collected through a light emitting diode via a photoelectric sensor, and the collected signals are conditioned by a low-pass filter circuit and a post-amplification circuit.

[0028] Preferably, the state monitoring module includes the following modules:

[0029] Factor definition module: Define the relationship diagram between heart rate variability indexes and time, depict the curve of heart rate variability data, and define the relevant values of the curve;

[0030] Defined value analysis module: Monitor the brain state according to the defined relevant values of the curve.

[0031] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above technical solutions adopted in the present application at least include:

[0032] The present application applies heart rate variability indexes to collect and analyze heart rate variability in real time and dynamically, dynamically describe the brain and mental states of the body, and even the stress and health states of the human body, monitor and observe the fatigue state in real time and dynamically, and predict the dynamic changes of the brain state, psychological and mental states, body stress and health states of the human body, which is real-time and accurate, can predict the changes of the body, and can predict the fatigue state. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0034] Figure 1 is the framework schematic diagram of the present application;

[0035] Figure 2 is the schematic diagram of the solution of the present application;

[0036] Figure 3 is the factor definition diagram of the present application;

[0037] Figure 4 is the relationship diagram between the standardized HRV time domain (RR interval) and the standardized reaction speed in the present application;

[0038] Figure 5 is the linear fitting diagram between the standardized HRV time domain (RR interval) and the reaction speed in the present application;

[0039] Figure 6 is the regression analysis diagram between the standardized HRV time domain (RR interval) and the reaction speed in the present application;

[0040] Figure 7 is the relationship diagram between the standardized HRV frequency domain (LF / HF) and the standardized reaction speed in the present application;

[0041] Figure 8 is the linear fitting diagram between the standardized HRV frequency domain (LF / HF) and the standardized reaction speed in the present application;

[0042] Figure 9 is the regression analysis diagram between the standardized HRV frequency domain (LF / HF) and the standardized reaction speed in the present application;

[0043] Figure 10 is the schematic diagram of the data after standardization of the extreme value of heart rate variability (RR interval), reaction speed, slope, curve integral I1, S1 slope, and LF / HF in the present application;

[0044] Figure 11 is the schematic diagram of the regression analysis between the extreme value of heart rate variability index (RR interval) and the reaction speed in the present application;

[0045] Figure 12 is the schematic diagram of the regression analysis between the standardized slope and the reaction speed in the present application;

[0046] Figure 13 is the schematic diagram of the regression analysis between the standardized slope and the reaction speed in the present application;

[0047] Figure 14 is the schematic diagram of the regression analysis between the standardized integral and the reaction speed in the present application;

[0048] Figure 15 is the schematic diagram of the regression analysis between the standardized LF / HF and the reaction speed in the present application. Detailed implementation manners

[0049] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0050] The following describes the implementation manners of the present application through specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope protected by the present application.

[0051] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present application, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspects described herein can be used to implement the device and / or practice the method. Additionally, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0052] It also needs to be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. The drawings only show the components related to the present application, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in its actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0053] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the examples can be practiced without these specific details.

[0054] In view of this, through in-depth research and improvement exploration of fatigue monitoring, the applicant has found that the current monitoring of fatigue levels mainly includes the following methods: 1. Fatigue questionnaires, which measure and evaluate fatigue levels by investigating individuals' subjective feelings; 2. Monitoring the physiological reaction behaviors of people. Many domestic and foreign scientific research institutions or companies have developed methods to monitor and evaluate the changes in the palpebral fissure width of people's eyelids as an indicative indicator of fatigue occurrence; 3. Estimating the body's fatigue state by the concentration of biological indicators such as LDH (lactate dehydrogenase) in the blood; 4. Some researchers at home and abroad use heart rate variability (HRV) as an indicative indicator for fatigue measurement and evaluation. After determining a threshold, if it is exceeded, it is considered that there is a functional decline or fatigue.

[0055] At present, fatigue questionnaires, changes in the palpebral fissure width of people's eyelids, biochemical indicators in the blood, and heart rate variability as indicative indicators for fatigue measurement and evaluation can all objectively describe the functional decline of the body to a certain extent, but there are many disadvantages. For example, in fatigue questionnaires, there are subjective influencing factors. Some people may unconsciously exaggerate or downplay their feelings; the palpebral fissure width is a change caused by the contraction of the eye muscles. In addition to fatigue, if there is an eye infection, or a foreign object stimulates the cornea or the skin of the eye area, the eyes will also have reflexive or involuntary blinking, which will cause changes in the applied palpebral fissure width, resulting in misjudgment; for biochemical indicators in the blood, such as changes in the levels of lactic acid, etc., they are not specific fatigue indicators and there will be large deviations. The HRV indicator is currently also used to judge whether fatigue occurs, but its judgment method is to set a population standard threshold. Through a signal acquisition and analysis device, the HRV indicator results of an individual are obtained, and then compared with the standard threshold to obtain the standard for whether fatigue occurs. Due to the large differences between individuals, using the same standard value is obviously inaccurate and unreasonable, and there is a great risk of misjudgment. In fact, the baseline values and related change ranges of various physiological signals of different individuals will be different, and using the same numerical value as the judgment standard is not scientific.

[0056] The above methods are difficult to give objective, scientific, and accurate judgments. More importantly, these methods are all data observed when fatigue occurs or after it occurs, or after significant fatigue, and cannot monitor and observe in real time and dynamically, let alone make predictions.

[0057] Based on this, the following will describe the technical solutions provided by each embodiment of the present application in conjunction with the accompanying drawings.

[0058] The embodiments of this specification propose a mental state monitoring method based on heart rate variability distraction, such as Figure 1 、 Figure 2 and Figure 3As shown, it includes the following steps: Information acquisition step: Dynamically acquire the heart rate variability data of the body in real time. State monitoring step: Depict the curve of the heart rate variability data and monitor the mental state based on the obtained curve.

[0059] In one embodiment, in the information acquisition step, signals are collected on the body surface through an electrocardiogram signal, and / or a photoplethysmogram signal, and / or a sound pressure signal generated by the heartbeat, and the time domain index and / or frequency domain index of the heart rate variability are calculated.

[0060] In one embodiment, in the information acquisition step, signal acquisition is performed through a light-emitting diode passing through a photoelectric sensor, and the collected signal is conditioned by a low-pass filter circuit and a post-amplification circuit.

[0061] The first part is the signal acquisition part. As Figure 1 and Figure 2 shown, for the signal acquisition method, there are multiple methods to collect heart rate variability indexes. Mainly through the ECG signal, or the PPG signal, or the sound pressure signal generated by the heartbeat, signals are collected on the body surface, and the HRV time domain indexes (RR interval related indexes or PNN indexes, which can also be extended to PNN50\45\40 indexes) and frequency domain signals (low frequency, high frequency, and low-to-high frequency ratio, etc.) are calculated. For example, calculate the mean standard deviation of the RR intervals within 5 minutes (it can be 1 minute, 5 minutes, 30 minutes, or even continuously recorded and calculated, which can be adjusted according to needs, and the method is similar), perform dynamic continuous recording, and depict the numerical records of the entire recording duration into a curve. Smooth the curve (perform corresponding smoothing processing according to the selected analysis period). Among them, ECG represents electrocardiogram, PPG represents photoplethysmography (photoplethysmogram), and RR interval means that in the electrocardiogram signal, the RR interval refers to the time interval between two consecutive R waves, usually in milliseconds (ms). The PNN index (PNN Index) is an important parameter in heart rate variability (HRV) analysis, used to evaluate the autonomic nerve regulation function of the heart. For example, PNN50 represents the number of pairs of adjacent normal heartbeat time interval differences exceeding 50 milliseconds (ms) in the electrocardiogram, accounting for the percentage of the total number of all normal heartbeat intervals, that is, the percentage of the number of adjacent cardiac cycles with RR interval (i.e., the time interval between two heartbeats) differences greater than 50 milliseconds in the total number of cardiac cycles.

[0062] In one embodiment, the state monitoring step includes the following steps: Factor definition step: Define the relationship diagram between the heart rate variability index and time, depict the curve of the heart rate variability data, and define the curve-related numerical values. Define numerical analysis step: Monitor the mental state according to the defined curve-related numerical values.

[0063] In one embodiment, in the factor definition step, multiple time periods are located on the curve; the curve-related values include curve extreme points, and / or, curve tangent rates, and / or, the slopes between adjacent extreme points of the curve, and / or, the integral area between adjacent maximum points or adjacent minimum points.

[0064] In the defined numerical analysis step, the mental state is judged according to the change of the curve tangent rate; and / or, the strength of the mental state is judged by the change of the curve extreme points; and / or, the strength of the mental state is judged by the change of the slope between adjacent extreme points of the curve; and / or, the strength of the mental state is judged by the change of the integral area between adjacent maximum points or adjacent minimum points; and / or, the strength of the mental state is judged by the time change of the corresponding heart rate variability indexes in multiple time periods; and / or, the strength of the mental state is judged by the change of the heart rate variability indexes in the corresponding time periods of multiple time periods.

[0065] In one embodiment, in the defined numerical analysis step, the change speed of the tangent rate is obtained as an index for predicting the mental state.

[0066] and / or, a first marked curve is generated according to the heart rate variability indexes at the corresponding time points in multiple time periods, and the change of the tangent rate of the first marked curve is used as an index for predicting the mental state.

[0067] and / or, a second marked curve is generated according to the slope, and the change of the tangent rate of the second marked curve is used as an index for predicting the mental state.

[0068] and / or, a third marked curve is generated according to the integral value of the integral area, and the tangent rate of the third marked curve is used as an index for predicting the mental state.

[0069] and / or, the time change degree of the corresponding heart rate variability indexes in multiple time periods is used to generate a fourth marked curve, and the tangent rate of the fourth marked curve is used as an index for predicting the mental state.

[0070] and / or, the change degree of the heart rate variability indexes in the corresponding time periods of multiple time periods is used to generate a fifth marked curve, and the tangent rate of the fifth marked curve is used as an index for predicting the mental state.

[0071] The second part defines relevant application factors, such as Figure 3 As shown, the abscissa is the time axis for each 24 hours, and signal acquisition records and analyses for multiple 24 hours can be continuously carried out infinitely. The ordinate is the heart rate variability index, which can be an HRV time domain index or an HRV frequency domain index. Figure 3The middle dotted line is a dotted smooth connection curve of numerical points continuously recording heart rate variability parameters (the calculation period can be adjusted as needed, which can be the result calculated in units of 1 minute, or can be calculated every 3 minutes, 5 minutes or several minutes). The horizontal short line is the curve position when the tangent of the continuous curve is 0, that is, the position of the adjacent extreme values of heart rate variability parameters. T1 - T6 represent the time points corresponding to the extreme values of heart rate variability parameters within a certain 24-hour period; T21 - T22 represent the corresponding time points of the second 24-hour period, and the naming rules for the third and subsequent 24-hour periods can be deduced by analogy. The magnitudes of the extreme values in the time domain and frequency domain indexes of HRV within each 24 hours correspond to the fluctuations of the human body's functional state within 24 hours, and there are mainly 6 extreme values. The first extreme value point is the low point L1, which is the extreme value around 0 - 4 o'clock (low point, deep sleep stage, factors such as mental and physical strength are at the lowest point. Except for disease states, a low value indicates sufficient rest, meaning a better state the next day), and the time point at this time is defined as T1; the second extreme value point is the high point H1, which is the extreme value around 5 - 10 o'clock (high point, the process from sleep to wakefulness and after breakfast, the higher it is, the better the state, and the larger the difference between H1 - L1, the better the state), and the time point at this time is defined as T2; the third extreme value point is the low point L2, which is the extreme value around 9 - 13 o'clock (low point, the lowest point after the high point of breakfast), and the time point at this time is defined as T3; the fourth extreme value point is the high point H2, which is the extreme value around 11 - 14 o'clock (high point, lunch), and the time point at this time is defined as T4; the fifth extreme value point is the low point L3, which is the extreme value around 12 - 16 o'clock (low point), and the time point at this time is defined as T5; the sixth extreme value point is the high point H3, which is the extreme value around 18 - 21 o'clock (high point, dinner), and the time point at this time is defined as T6.

[0072] L1 - L3 represent the numerical values of heart rate variability parameters of the lowest 3 extreme values of heart rate variability within the first 24 hours; H1 - H3 represent the numerical values of heart rate variability parameters of the highest 3 extreme values of heart rate variability within the first 24 hours. S1 - S6 represent the connecting lines of the data of the 6 highest and lowest extreme values of heart rate variability within the 24-hour period. I1, I3 and I5 respectively represent the integral areas of the dotted line curve in terms of time within the time periods of T1 - T3, T3 - T5 and T5 - T21 of heart rate variability parameters; I2, I4 and I6 respectively represent the integral areas of the dotted line curve in terms of time within the time periods of T2 - T4, T4 - T6 and T6 - T22 of heart rate variability parameters.

[0073] Secondly, define the numerical analysis algorithm and its application significance. (1) Application of the tangent rate on the smooth curve. Within each 24-hour period, the tangent rate at each point of the smooth dotted line changes continuously. At any point on the curve, when the tangent rate is positive, the mental state is rising and the reaction speed increases; conversely, if the tangent rate is negative, the mental state is declining and the reaction speed slows down. The point where the tangent rate is 0 is the turning point from high to low or from low to high. The corresponding time point is the extreme point, which is the best or worst time point of the mental state within a certain time period. The change speed of the tangent rate, that is, the tangent rate of the tangent rate (the continuous tangent rate values are plotted as a curve and then the tangent rate is calculated again), when the tangent rate changes from 0 at a certain position to a maximum absolute value and then changes in the opposite direction to the next 0 value (when going from low to high, the tangent rate changes from 0 to a positive value and keeps increasing, and then gradually changes back to 0 after reaching the highest point; conversely, when going from high to low, the tangent rate changes from 0 to a negative value and the absolute value of the negative value keeps increasing, and then gradually changes back to 0 after reaching the highest absolute value point), it is from one extreme value of the mental state to the extreme value of the opposite mental state. The change of the tangent rate can be used as a parameter for predicting the mental state. (2) The extreme values of the heart rate variability index at time points T1 - T6 in each 24-hour cycle represent the strength of the mental state. From low to high, it represents that the mental state becomes better and stronger, and vice versa. If the heart rate variability parameters at time points T1 - T6 in multiple consecutive 24-hour periods are continuously monitored and recorded, such as T1, T21, T31, T41... (corresponding to L1, L21, L31, L41...), and these values of L1, L21, L31, L41... are marked as a curve, if the values are upward, it indicates that the mental stress and mental state are not good; if the values are downward, it indicates that the mental stress and mental state are being relieved and getting better; if it is stable, it indicates that the mental stress and mental state are stable. Different from the above, for T2, T22, T32, T42... (corresponding to H1, H21, H31, H41...), the relevant values can be plotted as a curve. If the tangent rate is calculated for each point of this curve, when the tangent rate is 0, it represents a turning point in the change of the mental state. For example, when changing from low to high (such as during rest, training, or exercise, the mental state is continuously improving), when reaching the high point (tangent rate is 0), the mental state reaches the best. Vice versa. The change of the tangent rate can be used as an indicator for predicting the trend. Similar to T2, the same can be inferred for T3 - T6. (3) Within each 24-hour period, the slopes of the 6 straight lines S1 - S6 represent the change of the mental state. For the three lines S1, S3, and S5, when their respective slopes increase, it indicates that the mental state improves or becomes stronger, and conversely, it becomes worse or weaker; for the three lines S2, S4, and S6, when the absolute values of their respective slopes (which are negative values) increase, it indicates that the mental state improves or becomes stronger, and conversely, it becomes worse or weaker. If the slopes of S1 - S6 at time points T1 - T6 in multiple consecutive 24-hour periods are continuously monitored and recorded, and these parameters are plotted as a curve, the change of the values on the curve represents the change of the mental state within a time unit of one day (every 24 hours).A larger numerical value represents an improvement or enhancement in mental ability, and vice versa. If the tangent of this curve is calculated, when the tangent is 0, it represents a turn in the change of mental state. For example, when changing from low to high (such as during rest, training, or exercise, with the mental state continuously improving), when reaching the high point (tangent = 0), the mental state is at its best. Vice versa. The changing pattern of the tangent can be used as an indicator for predicting the trend of mental state changes. (4) The significance of the integration of I1 - I6 over time. In consecutive records of multiple 24 - hour periods, I1 on the first day, I21 on the second day, and subsequent I31, I41, I51..., the integral values will change. If they continue to increase, it represents an improvement in mental state, and vice versa. If the integral values are plotted as a curve and the tangent of this curve is calculated, when the tangent is 0, it represents a turn in the change of mental state. This analysis method is applicable to the analysis of the integration of each time period from I2 - I6, and also applicable to taking the sum of I1 - I6 within the entire 24 - hour period as the integral value for one day. After plotting the numerical values as a curve, the analysis method is the same as above. Therefore, the tangent of the curve can be used as the basis for judging the trend of mental state changes. (5) The position changes of the time points T1 - T6 within each of the consecutive multiple 24 - hour periods also have significance. Especially for the time point T1, if the time is advanced, it indicates a decline in the mental state of the body. If it is postponed, it indicates a recovery from fatigue or an abnormal state to a normal state, or an improvement in mental state through various training means. The remaining time points T2 - T6 also have the same significance. If the advanced or postponed time is used as the data for investigation, the magnitude of the advanced or postponed time can be used as a scale for evaluating mental decline or improvement. The larger the magnitude, the more obvious the decline or rise in mental state. If the rising or falling data are plotted and connected by lines, the change in the tangent of the curve represents the speed of mental rise or fall. The point where the tangent is 0 is the turning point of a certain segment of the change. Therefore, the tangent can be used as an indicator for trend judgment. (6) For the heart rate variability parameters within a fixed time period of each of the consecutive multiple 24 - hour periods, such as from 22:00 to 22:30 on the first day, from 22:00 to 22:30 on the second day, on the third day, the fourth day, and subsequent days, the heart rate variability parameters within the same time period each day can be analyzed according to the analysis methods described in (1) - (5) above.

[0074] Compared with the aforementioned fatigue and mental state detection technology, this application is a real-time dynamic monitoring method that focuses on dynamics, real-time, and continuity; through trend analysis, the algorithm can continuously judge and predict subsequent state changes. This technology includes the definition and application of the signal acquisition part and factors. This method monitors the changes in individual related indicators in real time, introduces its own continuous monitoring data, and conducts a before-and-after comparison method, eliminating the drawbacks of using absolute standard values ​​as evaluation criteria, and pays more attention to individualized data analysis, with better scientificity and practicality; the dynamic change trend analysis technology of the RR interval-related indicators of the time domain indicators of heart rate variability and the PNN50 indicators is used as a monitoring method to dynamically monitor the time-course changes of individual heart rate variability, and judge the changing trend of the body's mental state by analyzing the change rate of the heart rate variability indicators.

[0075] The embodiment of this specification also discloses a wearable system for monitoring mental state based on heart rate variability analysis, including the following modules: Information acquisition module: real-time dynamic acquisition of the body's heart rate variability data. State monitoring module: draws a curve of the heart rate variability data, and monitors the mental state based on the obtained curve.

[0076] In one embodiment, in the information acquisition module, signals are collected on the body surface through electrocardiogram signals, and / or photoplethysmographic marker signals, and / or sound pressure signals generated by heartbeats, and time domain indicators and / or frequency domain indicators of heart rate variability are calculated.

[0077] In one embodiment, in the information acquisition module, signal acquisition is performed through a light emitting diode via a photoelectric sensor, and the acquired signal is conditioned through a low-pass filter circuit and a post-amplifier circuit.

[0078] In one embodiment, the state monitoring module includes the following modules: a factor definition module: defines a relationship diagram between the heart rate variability index and time, draws a curve of the heart rate variability data, and defines curve-related values. A value analysis module: monitors the mental state according to the defined curve-related values.

[0079] The present specification also discloses a method for monitoring mental state based on heart rate variability analysis, by continuously monitoring individual reaction speed and HRV indexes (including RR interval, PNN50, SDNN and frequency domain indexes) of 5 subjects, and the data are as follows (average of 5 subjects). Figure 4 As shown in the figure, it is the relationship between the standardized HRV time domain (RR interval) and the standardized reaction speed; Figure 5 As shown in, it is the linear fit between the standardized HRV time domain (RR interval) and the reaction speed; Figure 6 As shown, this is the regression analysis of standardized HRV time domain (RR interval) and reaction speed; Figure 7As shown, it is the relationship between standardized HRV frequency domain (LF / HF) and standardized reaction speed; as Figure 8 shown, it is the linear fitting of standardized HRV frequency domain (LF / HF) and standardized reaction speed; as Figure 9 shown, it is the regression analysis of standardized HRV frequency domain (LF / HF) and standardized reaction speed. Among them, SDNN represents the standard deviation of all sinus rhythm RR intervals (i.e., NN intervals), and the unit is millisecond (ms). It reflects the degree of change in the time interval between two consecutive heartbeats of the heart, and LF / HF represents the ratio of low frequency to high frequency. Figure 8 and Figure 9 In, y represents the ordinate, x represents the abscissa, and R 2 That is, R-Squared represents the coefficient of determination, MultipleR represents the multiple correlation coefficient, and Adjusted R Square represents the adjusted R 2 .

[0080] Through curve fitting, it is found that the goodness of fit between the reaction speed and the heart rate variability index (including time domain and frequency domain) is very high. It shows that the HRV index in the frequency domain can be used as a reaction scale for the reaction speed, that is to say, the HRV frequency domain can indicate the change of the reaction speed and the change of the brain working state.

[0081] This embodiment of the specification also discloses a brain state monitoring method based on heart rate variability analysis. By continuously collecting 21-day ECG or PPG electrocardiogram data from 6 subjects, calculate the HRV indexes (including RR interval, PNN50, SDNN, and frequency domain indexes) every 5 minutes within each 24 hours, and plot the curve of the HRV data within 24 hours. Among the collected data, calculate the relevant data of 6 volunteers at the time points T1-T6 respectively, and the analysis methods include the above-mentioned curve slope, extreme values of heart rate variability indexes, slopes of the straight lines S1-S6, integrals of each period I1-I6, and changes at the corresponding time points of the time points T1-T6. In addition, except at the T1 time point, near the T2-T6 time points, near 9:30, 13:30, 15:30, 19:00, and 21:00, measure the reaction speed of each person, calculate the average value of 6 people, and perform regression analysis. The above-mentioned relevant data are continuously collected and analyzed for 21 days. Figures 6 - 11 What is shown in is the regression analysis of the average value of 6 people after continuously collecting 21-day data at T2 (9:30), and the regression analysis of the reaction speed index and the extreme value of heart rate variability (time domain index), slope, curve integral I1, slope of S1, and LF / HF (frequency domain index) is performed, and curve fitting is done, and the linear relationship is very. As Figure 10 shown, it is the data after standardization of the extreme value of heart rate variability (RR interval), reaction speed, slope, curve integral I1, slope of S1, and LF / HF; as Figure 11As shown, regression analysis of the extreme value of the heart rate variability index (RR interval) and reaction speed; as Figure 12 shown, regression analysis of the standardized slope and reaction speed; as Figure 13 shown, regression analysis of the standardized tangent rate and reaction speed; as Figure 14 shown, regression analysis of the standardized integral and reaction speed; as Figure 15 shown, regression analysis of the standardized LF / HF and reaction speed.

[0082] For the indexes obtained by converting, increasing or decreasing based on the detection data of the time domain and frequency domain of the heart rate variability indexes or based on the basic principles or algorithms of the heart rate variability indexes, when continuous data analysis is carried out after dynamic recording, the analysis method proposed in this application can be used to detect and predict the mental fatigue of the body.

[0083] This application uses the time domain indexes (RR interval, PNN50 and other time threshold indexes in heart rate variability, such as NN50, SDNN, SDANN, etc.) in heart rate variability, and also includes frequency domain indexes such as LF, HF and LF / HF, etc., to collect and analyze the heart rate variability in real time and dynamically. Through the established new algorithm, for example, first collect the heart rate variability indexes and depict the heart rate variability indexes as curves according to time; within 24 hours, the tangent rate (derivative) can be obtained in real time and dynamically on the basis of the above curves, and the change direction of mental function or state can be judged according to the change direction of the tangent rate; for continuous multiple 24 hours, according to the definitions of various aspects of the curve (including extreme value, slope, area integral, time point movement, etc.) described above, the newly defined indexes can be depicted as broken lines (smooth processing, approximate curve), and the tangent rate or derivative can be obtained dynamically, and the mental function or state can be judged according to the change of the tangent rate value, so as to dynamically describe the mental and mental state of the body, and even the pressure and health state of the human body. Based on the basic data of the heart rate variability indexes, new algorithms can be developed to predict the dynamic changes of the human mental state, psychological and mental state, body pressure and health state, which are real-time and accurate, and can predict the changes of the body.

[0084] In this specification, the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments described later, the description is relatively simple, and the relevant parts can be referred to the partial description of the foregoing embodiments.

[0085] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in this application should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for monitoring mental state based on heart rate variability analysis, characterized in that: The steps include: Information acquisition steps: real-time dynamic acquisition of the body's heart rate variability data; State monitoring step: draw a curve of the heart rate variability data, and monitor the mental state based on the obtained curve.

2. The method for monitoring mental state based on heart rate variability analysis according to claim 1, characterized in that: In the information acquisition step, signals are collected on the body surface through electrocardiogram signals, and / or photoplethysmographic marker signals, and / or sound pressure signals generated by heartbeats, and time domain indicators and / or frequency domain indicators of heart rate variability are calculated.

3. The method for monitoring mental state based on heart rate variability analysis according to claim 2, characterized in that: In the information acquisition step, the signal is collected by a light emitting diode through a photoelectric sensor, and the collected signal is conditioned by a low-pass filter circuit and a post-amplifier circuit.

4. The method for monitoring mental state based on heart rate variability analysis according to claim 1, characterized in that: The state monitoring step comprises the following steps: Factor definition steps: define the relationship diagram between heart rate variability index and time, draw the curve of heart rate variability data, and define the relevant values ​​of the curve; Define the numerical analysis steps: monitor the mental state according to the defined curve related values.

5. The method for monitoring mental state based on heart rate variability analysis according to claim 4, characterized in that: In the factor definition step, multiple time periods are located on the curve; the curve-related values ​​include the extreme value points of the curve, and / or the tangent rate of the curve, and / or the slope between adjacent extreme value points of the curve, and / or the integral area between adjacent maximum value points or adjacent minimum values; In the step of defining numerical analysis, the mental state is judged according to the change of the tangent rate of the curve; and / or, the strength of the mental state is judged by the change of the extreme points of the curve; and / or, the strength of the mental state is judged by the change of the slope of adjacent extreme points of the curve; and / or, the strength of the mental state is judged by the change of the integral area between adjacent maximum points or adjacent minimum points; and / or, the strength of the mental state is judged by the time change of the corresponding heart rate variability index in multiple time periods; and / or, the strength of the mental state is judged by the change of the heart rate variability index in the corresponding time periods of multiple time periods.

6. The method for monitoring mental state based on heart rate variability analysis according to claim 5, characterized in that: In the step of defining numerical analysis, the change rate of the shear rate is obtained as an indicator for predicting the mental state; and / or, generating a first marking curve according to the heart rate variability index at corresponding time points in a plurality of time periods, and using the tangent rate change of the first marking curve as an index for predicting the mental state; and / or, generating a second marking curve according to the slope, and using a change in the tangent rate of the second marking curve as an indicator for predicting the mental state; and / or, generating a third labeling curve according to the integral value of the integral area, and the tangent rate of the third labeling curve is used as an indicator for predicting the mental state; and / or, generating a fourth marking curve based on the time variation degree of the corresponding heart rate variability index in a plurality of time periods, and the tangent rate of the fourth marking curve is used as an index for predicting the mental state; And / or, a fifth marking curve is generated by the degree of change of the heart rate variability index in corresponding time periods of multiple time periods, and the tangent rate of the fifth marking curve is used as an indicator for predicting the mental state.

7. A wearable system for monitoring mental status based on heart rate variability analysis, characterized in that: Includes the following modules: Information acquisition module: real-time dynamic acquisition of the body's heart rate variability data; State monitoring module: draws a curve of heart rate variability data and monitors the mental state based on the obtained curve.

8. The wearable system for monitoring mental state based on heart rate variability analysis according to claim 7, characterized in that: In the information acquisition module, signals are collected on the body surface through electrocardiogram signals, and / or photoplethysmographic marker signals, and / or sound pressure signals generated by heartbeats, and time domain indicators and / or frequency domain indicators of heart rate variability are calculated.

9. The wearable system for monitoring mental state based on heart rate variability analysis according to claim 8, characterized in that: In the information acquisition module, the light emitting diode is used to collect signals through a photoelectric sensor, and the collected signals are conditioned through a low-pass filter circuit and a post-amplifier circuit.

10. The wearable system for monitoring mental status based on heart rate variability analysis according to claim 7, characterized in that: The state monitoring module includes the following modules: Factor definition module: define the relationship diagram between heart rate variability index and time, draw the curve of heart rate variability data, and define the relevant values ​​of the curve; Define a numerical analysis module: monitor the mental state according to the defined curve-related values.

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