Autonomic nervous activity real-time monitoring system based on sliding window

Abstract

The application discloses a sliding window-based real-time monitoring system for autonomic nervous activity, and particularly relates to the technical field of nerve monitoring; the application constructs a multi-dimensional specificity evaluation system, designs specific evaluation indexes for vagus nerves and sympathetic nerves respectively, removes abnormal data interference in combination with a preposed quality control coefficient, realizes three-level grading determination of the degree of nerve excitation through a degree coefficient and a mapping rule library, discards a traditional single-index qualitative determination mode, realizes accurate quantification and fine evaluation of autonomic nervous function, and provides a grading reference basis for clinical intervention.

Description

Technical Field

[0001] This invention relates to the field of neural monitoring technology, and more specifically, to a real-time monitoring system for autonomic neural activity based on a sliding window. Background Technology

[0002] Real-time monitoring of autonomic nervous activity is one of the core technologies for cardiovascular function assessment and human physiological state monitoring. Among them, the heart rate variability related indicators are key quantitative evidence reflecting the tension of the vagus nerve and sympathetic nerve, and have important application value in clinical physical examination, chronic disease management, sports health monitoring and other fields.

[0003] However, existing real-time neural activity monitoring systems still have the following shortcomings in practical applications: However, existing autonomic nervous activity monitoring technologies still have several technical shortcomings in practical applications, making it difficult to meet the monitoring requirements of real-time, accurate, and highly specific monitoring. These shortcomings are specifically reflected in the following aspects: Existing technologies mostly adopt a static data analysis mode with fixed long time periods, which cannot achieve continuous real-time monitoring and dynamic tracking of autonomic nerve activity, cannot process and update indicators of continuous incoming electrocardiogram data streams in real time, cannot capture instantaneous dynamic changes in autonomic nerve tension, and cannot adapt to application scenarios with extremely high real-time requirements such as perioperative monitoring, which greatly limits the application boundaries of the technology. Current technologies offer limited assessment dimensions, failing to provide independent quantitative evaluation of the sympathetic and vagus nerve pathways. Furthermore, they lack the ability to finely categorize functional levels. Most existing monitoring protocols can only qualitatively determine "sympathetic dominance" or "vagus nerve dominance" using a single indicator, unable to comprehensively quantify the activity levels of these two types of nerves across multiple dimensions and parameters. Moreover, they lack specific assessment indicator systems tailored to different nerve dominance states, making it impossible to finely classify the excitability levels of the vagus or sympathetic nerves. This makes it difficult to meet the clinical need for precise assessment of the degree of autonomic nervous system dysfunction and to provide a tiered reference for the development of subsequent intervention plans.

[0004] To address this, a real-time monitoring system for autonomic neural activity based on sliding windows was developed. Summary of the Invention

[0005] To overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide a real-time monitoring system for autonomic neural activity based on a sliding window.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A real-time monitoring system for autonomic neural activity based on a sliding window includes the following modules: The sliding window operation module is used to collect human body surface electrocardiogram signals in real time, perform real-time R wave peak detection on the electrocardiogram signals, generate a continuous RR interval sequence, and preset a fixed-duration sliding time window to perform real-time windowing processing on the continuously flowing RR interval sequence, identify the heart rate deceleration points in the sequence, and use the deceleration points as anchor points to complete the phase sorting and signal averaging calculation. The heart rate index calculation module calculates the real-time heart rate deceleration force index based on the average result of the phase sequence signal. The neural state output module determines the human neural manifestations based on the comparison results of real-time heart rate deceleration force indicators; it pre-edits the neural assessment logic to be implemented for different neural manifestations; after determining the neural assessment logic to be implemented, it comprehensively processes the assessment indicators of the corresponding neural assessment logic and outputs the human neural monitoring assessment results.

[0007] Specifically, phase sorting and signal averaging calculation; Iterate through the RR interval series within the sliding window and select all that satisfy the condition. of The end point of the interval serves as the deceleration anchor point, where... represents the RR interval duration of the i-th cardiac cycle in the sequence; Using each identified deceleration anchor point as the center, select 5 cardiac cycles on each side of the center to form a single heart rate deceleration segment with a length of 11 cardiac cycles. All deceleration segments are arranged in phase alignment with the deceleration anchor point as the reference. For all phase-aligned deceleration segments, the arithmetic mean of the cardiac cycle durations with the same sequence number is calculated to obtain the sequence value after sorting and averaging.

[0008] Specifically, the calculation logic for the real-time heart rate deceleration force index; Four sets of sequence values ​​are extracted, including the mean of the cardiac cycle corresponding to the deceleration anchor point, the mean of the first cardiac cycle after the anchor point, the mean of the first cardiac cycle before the anchor point, and the mean of the second cardiac cycle before the anchor point. Based on the input integer average sequence values, the real-time index of heart rate deceleration force is calculated in real time according to a preset formula.

[0009] Specifically, the logic for judging human neurological manifestations; Neurological manifestations include vagal dominance and sympathetic dominance; If the real-time heart rate deceleration force index is higher than the corresponding set reference range, it is determined to be vagal nerve dominance; otherwise, it is determined to be sympathetic nerve dominance.

[0010] Specifically, after determining the neurological manifestations, the pre-quality control coefficients corresponding to the neurological manifestations are output through comprehensive processing in combination with effectiveness indicators; effectiveness indicators include the percentage of effective cardiac cycles, the number of effective deceleration anchors, and the heart rate deviation rate. If the pre-test quality control coefficient is lower than the preset quality control threshold coefficient, the current neurological performance assessment result is considered invalid; otherwise, the current neurological performance assessment result is considered valid, and evaluation indicators for different neurological performances are extracted for comprehensive processing. Based on the assessment indicators corresponding to vagal nerve dominance, a comprehensive processing method is used to output the vagal nerve degree coefficient; based on the assessment indicators corresponding to sympathetic nerve dominance, a comprehensive processing method is used to output the sympathetic nerve degree coefficient.

[0011] Specifically, extract the total number of periods between RRs within the window and the number of abnormal RRs removed using preset filtering rules; Subtracting the abnormal RR intervals from the total RR intervals and then dividing by the total RR intervals yields the percentage of effective cardiac cycles. Within the statistics window The number of deceleration anchor points is used to obtain the effective number of deceleration anchor points; The heart rate value is converted from the arithmetic mean of the RR intervals within the window. The absolute difference between this value and the preset reference heart rate is calculated, and then divided by the reference heart rate to obtain the heart rate deviation rate.

[0012] Specifically, the evaluation indicators corresponding to vagal dominance include the root mean square of the difference between adjacent RR intervals, deceleration characteristics, and high-frequency power in the HRV frequency domain; Through formula Calculate the root mean square of the difference between adjacent RR intervals. N is the total number of RR intervals; Deceleration characteristics include the rate of deceleration events and the average deceleration magnitude per event; The deceleration event occurrence rate is obtained by dividing the number of deceleration anchor points within the window by the total number of RR intervals. right The number of deceleration anchor points, for The difference is summed, and the sum is divided by the number of deceleration anchors to obtain the average deceleration amplitude per cycle. The RR interval sequence was resampled, and a Hanning window was added for post-seat FFT power spectrum analysis. The power of the preset high-frequency band was integrated, and the natural logarithm was taken to obtain the HRV frequency domain high-frequency power.

[0013] Specifically, the assessment indicators corresponding to sympathetic dominance include heart rate acceleration capacity, acceleration characteristics, and balance ratio; Iterate through the RR interval series of the current sliding window and select all that satisfy the condition. of The end point of the interval serves as the acceleration anchor point; where Given the RR interval duration of the j-th effective cardiac cycle in the sequence, five effective cardiac cycles are selected on each side of each identified acceleration anchor point to form a single heart rate acceleration segment with a length of 11 cardiac cycles. All single heart rate acceleration segments are arranged in phase alignment based on the acceleration anchor point. For all acceleration segments after phase alignment, the arithmetic mean of the cardiac cycle duration with the same sequence number is calculated to obtain the sequence value after integer average. Four sets of sequence values ​​are extracted, including the mean of the cardiac cycle corresponding to the acceleration anchor point, the mean of the first cardiac cycle after the anchor point, the mean of the first cardiac cycle before the anchor point, and the mean of the second cardiac cycle before the anchor point. Based on the input integer average sequence value, the real-time index of heart rate acceleration is calculated in real time according to the preset formula.

[0014] Specifically, acceleration characteristics include the occurrence rate of acceleration events and the average magnitude of a single acceleration. The acceleration event occurrence rate is obtained by dividing the number of acceleration anchors within the window by the total number of RR intervals. right The number of acceleration anchor points, to perform The difference is summed, and the sum is divided by the number of acceleration anchors to obtain the average single acceleration amplitude. The RR interval sequence was resampled, and FFT power spectrum analysis was performed after applying a Hanning window; the power in the preset low-frequency band was integrated, and the natural logarithm was taken to obtain the HRV frequency domain low-frequency power; The balance ratio is obtained by dividing the low-frequency power in the HRV frequency domain by the high-frequency power in the HRV frequency domain.

[0015] Specifically, for the neurological manifestations of the human body, the relevant degree coefficients are input into a pre-built mapping rule base to determine the vagus nerve level or sympathetic nerve level. The vagus nerve grade includes mild vagus nerve, moderate vagus nerve, and severe vagus nerve; the sympathetic nerve grade includes mild sympathetic nerve, moderate sympathetic nerve, and severe sympathetic nerve. The results of vagus nerve grade or sympathetic nerve grade determination are used as the results of human neurological monitoring and evaluation.

[0016] The technical effects and advantages of this invention are as follows: (1) This invention constructs a multi-dimensional specific assessment system, designs exclusive assessment indicators for vagal and sympathetic nerve dominance respectively, combines pre-quality control coefficients to eliminate abnormal data interference, and realizes three-level classification of nerve excitation level through degree coefficient and mapping rule base. It abandons the traditional single-indicator qualitative judgment mode, realizes accurate quantification and refined assessment of autonomic nerve function, and provides a graded reference for clinical intervention. (2) This invention innovatively integrates phase ordering, signal averaging calculation and frequency domain power spectrum analysis technology. By aligning the phase of deceleration / acceleration anchor points, the accuracy of index calculation is improved. Combined with HRV high and low frequency power analysis and ratio calculation, a multi-dimensional verification system is constructed to effectively distinguish between true and false neural dominance states. At the same time, personalized benchmark values ​​are used to replace general thresholds, which significantly improves the accuracy and individual adaptability of monitoring results. (3) This invention uses a sliding window to process ECG data in real time, and combines the Pan-Tompkins algorithm and the 3σ threshold method to achieve accurate analysis of ECG signals. With the continuous sliding window, the indicators are refreshed in seconds, breaking through the limitations of traditional static long-term analysis. It can capture the instantaneous changes in autonomic nerve tension in real time, and is suitable for scenarios with high real-time requirements such as perioperative monitoring, which greatly expands the application boundaries of autonomic nerve monitoring technology. Attached Figure Description

[0017] Figure 1 This is a flowchart of the real-time monitoring system for autonomic neural activity based on a sliding window, as described in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, the modules of the real-time monitoring system for autonomic neural activity based on a sliding window are as follows: The sliding window processing module is used to acquire human body surface electrocardiogram (ECG) signals in real time. It uses the Pan-Tompkins algorithm to detect the R-wave peaks in the preprocessed ECG signals in real time, generating a continuous RR interval sequence. The 3σ interval threshold method is used to identify and remove abnormal data such as premature beats, missed beats, and artifacts that exceed the normal physiological range in the sequence. A preset sliding time window of fixed duration (a continuous sliding window of 100 seconds, which is updated in real time with the ECG data stream to achieve second-level refresh of monitoring indicators) is used to perform real-time windowing processing on the continuously flowing RR interval sequence, identify the heart rate deceleration points in the sequence, and use the deceleration points as anchor points to complete the phase ordering and signal averaging calculation. By default, the acquired ECG signals are filtered and denoised in real time to remove power frequency interference and electromyographic noise. The built-in low-pass filter, with a cutoff frequency of 30Hz, is used to filter out 50Hz power frequency interference and high-frequency electromyographic noise in the ECG signals.

[0020] Specifically: Iterate through the RR interval series within the sliding window and select all that satisfy the condition. of The end point of the interval serves as the deceleration anchor point, where... represents the RR interval duration of the i-th cardiac cycle in the sequence; The RR interval is the time interval between two consecutive R waves (ventricular depolarization waves) on an electrocardiogram (ECG), and its length is directly negatively correlated with heart rate. Prolonged RR interval → slowed heart rate (i.e., "heart rate deceleration"); A shortened RR interval leads to an increased heart rate (i.e., "accelerated heart rate").

[0021] When satisfied When the duration of the (i+1)th cardiac cycle is longer than that of the ith cycle, it is a direct quantitative representation of the heart rate shifting from "faster" to "slower", i.e., a heart rate deceleration event.

[0022] Using each identified deceleration anchor point as the center, select 5 cardiac cycles on each side of the center to form a single heart rate deceleration segment with a length of 11 cardiac cycles. All deceleration segments are arranged in phase alignment with the deceleration anchor point as the reference. For all phase-aligned deceleration segments, the arithmetic mean of the cardiac cycle durations with the same sequence number is calculated to obtain the sequence value after sorting and averaging.

[0023] The heart rate index calculation module calculates the real-time index of heart rate deceleration force, which reflects vagal nerve function, based on the average result of phase sequence signal. Specifically: Four sets of sequence values ​​were extracted, including the mean of the cardiac cycle corresponding to the deceleration anchor point, the mean of the first cardiac cycle after the anchor point, the mean of the first cardiac cycle before the anchor point, and the mean of the second cardiac cycle before the anchor point. Based on the input integer average sequence value, the real-time index DCr of heart rate deceleration force is calculated in real time according to a preset formula. The calculation formula is: ; The mean value of the cardiac cycle corresponding to the deceleration anchor point represents the length of the RR interval at the moment when heart rate deceleration occurs, and is the starting point for the vagus nerve to begin functioning. This represents the mean of the first cardiac cycle after the anchor point, indicating the length of the RR interval at the peak of heart rate deceleration. This represents the mean of the first cardiac cycle before the anchor point, indicating the baseline heart rate before vagal nerve excitation. The mean value of the second cardiac cycle before the anchor point represents the baseline RR interval of the two cycles before the heart rate deceleration occurs. It is used to further stabilize the baseline and avoid the influence of random fluctuations in a single cycle. The higher the DCr value, the greater the net amplitude of heart rate deceleration, and the stronger the inhibitory effect of the vagus nerve on heart rate (the higher the excitability of the vagus nerve). A lower DCr value indicates a smaller net amplitude of heart rate deceleration, insufficient vagal tone, and that the sympathetic nervous system may be relatively dominant.

[0024] The neural state output module determines the human neural manifestations based on the comparison results of real-time heart rate deceleration force indicators; it pre-edits the neural assessment logic to be implemented for different neural manifestations; after determining the neural assessment logic to be implemented, it comprehensively processes the assessment indicators of the corresponding neural assessment logic and outputs the human neural monitoring assessment results. Specifically: Neurological manifestations include vagal dominance and sympathetic dominance; If the real-time heart rate deceleration force index is higher than the corresponding set reference range, it is determined to be vagal nerve dominance; Conversely, it is determined to be sympathetic dominance; If the index is within the reference range, it is determined to be in a state of autonomic nervous system balance. The reference range for the indicators is set by collecting 5 to 10 minutes of effective electrocardiogram data in a stable state of rest, relaxation, fasting, and undisturbed conditions, calculating the individual DCr baseline value, and then setting it based on the baseline value by medical staff.

[0025] After determining the neurological manifestations, the pre-control coefficients corresponding to the neurological manifestations are output by combining the effectiveness indicators. The effectiveness indicators include the percentage of effective cardiac cycles, the number of effective deceleration anchors, and the heart rate deviation rate. The specific calculation process is as follows: That is, using formulas The pre-quality control coefficient was calculated. ;in These represent the percentage of effective cardiac cycles, the number of effective deceleration anchors, and the average heart rate, respectively. , as well as The preset effective cardiac cycle threshold percentage, effective deceleration anchor point threshold number, and average threshold heart rate; , as well as The weighting coefficients are set, and their sum is one.

[0026] Extract the total number of RR intervals within the window and the number of abnormal RRs removed using a preset filtering rule; the preset filtering rule is the 3σ interval threshold method. Subtracting the abnormal RR intervals from the total RR intervals and then dividing by the total RR intervals yields the percentage of effective cardiac cycles. Within the statistics window The number of deceleration anchor points is used to obtain the effective number of deceleration anchor points; The heart rate value converted from the arithmetic mean of the RR intervals within the window is then compared with the preset reference heart rate by calculating the absolute difference, and then divided by the reference heart rate to obtain the heart rate deviation rate. The reference heart rate can be the midpoint between 40 beats / min and 180 beats / min (autonomic assessment has no physiological significance under extreme heart rate conditions).

[0027] The arithmetic mean of all RR intervals is used to obtain the average duration of a single cardiac cycle. The average number of heartbeats per minute (heart rate) can be obtained by dividing the total duration of 1 minute by the average duration of a single heartbeat.

[0028] If the pre-test quality control coefficient is lower than the preset quality control threshold coefficient, the judgment result of the current neural performance is considered invalid. Conversely, if the current neurological manifestation is not found, the assessment result is considered valid, and evaluation indicators for different neurological manifestations are extracted and processed comprehensively. The evaluation indicators corresponding to vagal dominance include the root mean square of the difference between adjacent RR intervals, deceleration characteristics, and high-frequency power in the HRV frequency domain; Through formula Calculate the root mean square of the difference between adjacent RR intervals. N is the total number of RR intervals; If only DCr increases The absence of a corresponding increase suggests that the condition is a pseudovagal dominance caused by occasional fluctuations in the RR interval.

[0029] Deceleration characteristics include the rate of deceleration events and the average deceleration magnitude per event; The deceleration event occurrence rate is obtained by dividing the number of deceleration anchor points within the window by the total number of RR intervals. right The number of deceleration anchor points, for The difference is summed, and the sum is divided by the number of deceleration anchors to obtain the average deceleration amplitude per cycle. Advantages of true vagus nerve: The incidence of deceleration events is in the relatively high physiological range (20%-50%), and the average deceleration amplitude per event is significantly higher than the individual baseline value.

[0030] The RR interval sequence was resampled, and power spectrum analysis was performed using a Hanning window followed by a seated FFT. The power in the preset high-frequency band (0.15-0.4Hz) was integrated, and the natural logarithm was taken to obtain the high-frequency power in the HRV frequency domain. In true vagal dominance, the high-frequency power of the HRV frequency domain is significantly higher than the individual baseline value, which is the gold standard for vagal nerve activity in the frequency domain and further eliminates false positives.

[0031] Based on the assessment indicators corresponding to vagal nerve dominance, a vagal nerve degree coefficient is output after comprehensive processing. The specific calculation process is as follows: Using formula The vagal nerve dominance coefficient was obtained by calculating the assessment index corresponding to the vagal nerve dominance. , , , as well as These represent the root mean square of the difference between adjacent RR intervals, the deceleration event rate, the average single deceleration magnitude, and the high-frequency power in the HRV frequency domain, respectively. The highest value in the reference range corresponding to the real-time heart rate deceleration force indicator is taken. , , as well as The benchmark assessment metrics for vagal dominance include the root mean square of the difference between adjacent RR intervals, the incidence of reference deceleration events, the average single deceleration amplitude, and the high-frequency power in the reference HRV frequency domain. , , as well as The weighting coefficients are set, and their sum is one.

[0032] The evaluation indicators corresponding to sympathetic dominance include heart rate acceleration capacity, acceleration characteristics, and balance ratio; Iterate through the RR interval series of the current sliding window and select all that satisfy the condition. of The end point of the interval serves as the acceleration anchor point; where Let be the RR interval duration of the j-th valid cardiac cycle in the sequence. This means that the duration of the (j+1)th cardiac cycle is shorter than that of the jth cycle, which is a direct quantitative representation of the change in heart rate from "slower" to "faster", i.e., a heart rate acceleration event.

[0033] Using each identified acceleration anchor point as the center, select 5 effective cardiac cycles on each side of the center to form a single heart rate acceleration segment with a length of 11 cardiac cycles; for all single heart rate acceleration segments, perform phase alignment based on the acceleration anchor point to ensure that the cardiac cycle sequence number of each acceleration segment corresponds one-to-one. For all acceleration segments after phase alignment, the arithmetic mean of the cardiac cycle duration with the same sequence number is calculated to obtain the sequence value after sorting and averaging. Four sets of sequence values ​​were extracted, including the mean of the cardiac cycle corresponding to the acceleration anchor point, the mean of the first cardiac cycle after the anchor point, the mean of the first cardiac cycle before the anchor point, and the mean of the second cardiac cycle before the anchor point. Based on the input integer average sequence value, the real-time heart rate acceleration index ACr is calculated in real time according to a preset formula. The calculation formula is: ; To accelerate the mean of the cardiac cycle corresponding to the anchor point, This represents the mean of the first cardiac cycle after the anchor point. This represents the average value of the first cardiac cycle before the anchor point. This is the average value of the second cardiac cycle before the anchor point.

[0034] True sympathetic dominance: ACr is higher than the upper limit of the individual reference range, forming a symmetrical antagonism with the decrease in DCr, confirming that the decrease in DCr is caused by sympathetic-mediated heart rate acceleration.

[0035] Acceleration characteristics include the occurrence rate of acceleration events and the average magnitude of a single acceleration event; The acceleration event occurrence rate is obtained by dividing the number of acceleration anchors within the window by the total number of RR intervals. right The number of acceleration anchor points, to perform The difference is summed, and the sum is divided by the number of acceleration anchors to obtain the average single acceleration amplitude. True sympathetic nervous system advantages: The incidence of acceleration events is in the relatively high physiological range (20%-50%), and the average single acceleration amplitude is significantly higher than the individual baseline value.

[0036] The RR interval sequence was resampled, and FFT power spectrum analysis was performed after applying a Hanning window; the power in the preset low-frequency band (0.04-0.15Hz) was integrated, and the natural logarithm was taken to obtain the HRV frequency domain low-frequency power; The balance ratio is obtained by dividing the low-frequency power in the HRV frequency domain by the high-frequency power in the HRV frequency domain. True sympathetic dominance: The LF / HF ratio is significantly higher than the individual's resting baseline value, which matches the increase in ACr.

[0037] Based on the evaluation indicators corresponding to the sympathetic nerve dominance, a sympathetic nerve degree coefficient is output after comprehensive processing. The specific calculation process is as follows: Using formula The sympathetic nerve predominance coefficient was obtained by calculating the assessment index corresponding to the vagus nerve dominance. , , , These represent the acceleration event occurrence rate, the average single acceleration magnitude, and the balance ratio, respectively. , , as well as The benchmark assessment metrics for sympathetic dominance include reference heart rate acceleration, reference acceleration event rate, reference average single acceleration amplitude, and reference balance ratio. , , as well as The weighting coefficients are set, and their sum is one.

[0038] For the neurological manifestations of the human body, the vagus nerve level or sympathetic nerve level is determined by inputting the associated degree coefficients into a pre-built mapping rule base. If the nerve pattern shows vagal dominance, the vagal nerve level is converted using the vagal nerve degree coefficient-vagal nerve level mapping rule; where the vagal nerve level includes mild vagal, moderate vagal, and severe vagal. This involves setting three sets of coefficient intervals corresponding to the vagus nerve severity coefficient, with each set of coefficient intervals corresponding to a vagus nerve level; the higher the vagus nerve severity coefficient, the higher the probability of matching severe vagus nerve.

[0039] If the neural pattern shows sympathetic dominance, then the sympathetic level is converted using the sympathetic degree coefficient-sympathetic level mapping rule, where the sympathetic level includes mild sympathetic, moderate sympathetic, and severe sympathetic. This involves setting three sets of coefficient intervals corresponding to the sympathetic nerve intensity coefficient, with each set of coefficient intervals corresponding to a sympathetic nerve level; the higher the sympathetic nerve intensity coefficient, the higher the probability of matching severe sympathetic nerve.

[0040] The results of vagal nerve grade or sympathetic nerve grade determination are used as the results of human neurological monitoring and evaluation. The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0041] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0042] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0043] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0044] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0045] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0046] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0047] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A real-time monitoring system for autonomic neural activity based on a sliding window, characterized in that, Includes the following modules: The sliding window operation module is used to collect human body surface electrocardiogram signals in real time, perform real-time R wave peak detection on the electrocardiogram signals, generate a continuous RR interval sequence, and preset a fixed-duration sliding time window to perform real-time windowing processing on the continuously flowing RR interval sequence, identify the heart rate deceleration points in the sequence, and use the deceleration points as anchor points to complete the phase sorting and signal averaging calculation. The heart rate index calculation module calculates the real-time heart rate deceleration force index based on the average result of the phase sequence signal. The neural state output module determines the human neural state based on the comparison results of real-time heart rate deceleration force indicators. The system pre-edits the neural assessment logic required for different neural manifestations. After determining the neural assessment logic to be implemented, it comprehensively processes the assessment indicators of the corresponding neural assessment logic and outputs the neural monitoring and assessment results of the human body.

2. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 1, characterized in that: Phase sorting and signal averaging calculation; Iterate through the RR interval series within the sliding window and select all that satisfy the condition. of The end point of the interval serves as the deceleration anchor point, where... represents the RR interval duration of the i-th cardiac cycle in the sequence; Using each identified deceleration anchor point as the center, select 5 cardiac cycles on each side of the center to form a single heart rate deceleration segment with a length of 11 cardiac cycles. All deceleration segments are arranged in phase alignment with the deceleration anchor point as the reference. For all phase-aligned deceleration segments, the arithmetic mean of the cardiac cycle durations with the same sequence number is calculated to obtain the sequence value after sorting and averaging.

3. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 2, characterized in that: The calculation logic of real-time heart rate deceleration force index; Four sets of sequence values ​​are extracted, including the mean of the cardiac cycle corresponding to the deceleration anchor point, the mean of the first cardiac cycle after the anchor point, the mean of the first cardiac cycle before the anchor point, and the mean of the second cardiac cycle before the anchor point. Based on the input integer average sequence values, the real-time index of heart rate deceleration force is calculated in real time according to a preset formula.

4. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 1, characterized in that: The logic for determining human neurological manifestations; Neurological manifestations include vagal dominance and sympathetic dominance; If the real-time heart rate deceleration force index is higher than the corresponding set reference range, it is determined to be vagal nerve dominance; otherwise, it is determined to be sympathetic nerve dominance.

5. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 4, characterized in that: After determining the neurological manifestations, the pre-control coefficients corresponding to the neurological manifestations are output by combining the effectiveness indicators. The effectiveness indicators include the percentage of effective cardiac cycles, the number of effective deceleration anchors, and the heart rate deviation rate. If the pre-test quality control coefficient is lower than the preset quality control threshold coefficient, the current neurological performance assessment result is considered invalid; otherwise, the current neurological performance assessment result is considered valid, and evaluation indicators for different neurological performances are extracted for comprehensive processing. Based on the assessment indicators corresponding to vagal nerve dominance, a comprehensive processing method is used to output the vagal nerve degree coefficient; based on the assessment indicators corresponding to sympathetic nerve dominance, a comprehensive processing method is used to output the sympathetic nerve degree coefficient.

6. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 5, characterized in that: Extract the total number of periods between RRs within the window and the number of abnormal RRs removed using preset filtering rules; Subtracting the abnormal RR intervals from the total RR intervals and then dividing by the total RR intervals yields the percentage of effective cardiac cycles. Within the statistics window The number of deceleration anchor points is used to obtain the effective number of deceleration anchor points; The heart rate value is converted from the arithmetic mean of the RR intervals within the window. The absolute difference between this value and the preset reference heart rate is calculated, and then divided by the reference heart rate to obtain the heart rate deviation rate.

7. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 5, characterized in that: The evaluation indicators corresponding to vagal dominance include the root mean square of the difference between adjacent RR intervals, deceleration characteristics, and high-frequency power in the HRV frequency domain; Through formula Calculate the root mean square of the difference between adjacent RR intervals. N is the total number of RR intervals; Deceleration characteristics include the rate of deceleration events and the average deceleration magnitude per event; The deceleration event occurrence rate is obtained by dividing the number of deceleration anchor points within the window by the total number of RR intervals. right The number of deceleration anchor points, for The difference is summed, and the sum is divided by the number of deceleration anchors to obtain the average deceleration amplitude per cycle. The RR interval sequence was resampled, and FFT power spectrum analysis was performed after adding a Hanning window. The power of the preset high-frequency band was integrated, and the natural logarithm was taken to obtain the HRV frequency domain high-frequency power.

8. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 5, characterized in that: The evaluation indicators corresponding to sympathetic dominance include heart rate acceleration capacity, acceleration characteristics, and balance ratio; Iterate through the RR interval series of the current sliding window and select all that satisfy the condition. of The end point of the interval serves as the acceleration anchor point; in Given the RR interval duration of the j-th effective cardiac cycle in the sequence, five effective cardiac cycles are selected on each side of each identified acceleration anchor point to form a single heart rate acceleration segment with a length of 11 cardiac cycles. All single heart rate acceleration segments are arranged in phase alignment based on the acceleration anchor point. For all acceleration segments after phase alignment, the arithmetic mean of the cardiac cycle duration with the same sequence number is calculated to obtain the sequence value after sorting and averaging. Four sets of sequence values ​​are extracted, including the mean of the cardiac cycle corresponding to the acceleration anchor point, the mean of the first cardiac cycle after the anchor point, the mean of the first cardiac cycle before the anchor point, and the mean of the second cardiac cycle before the anchor point. Based on the input integer average sequence values, the real-time index of heart rate acceleration is calculated in real time according to a preset formula.

9. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 8, Its features are: Acceleration characteristics include the occurrence rate of acceleration events and the average magnitude of a single acceleration event; The acceleration event occurrence rate is obtained by dividing the number of acceleration anchors within the window by the total number of RR intervals. right The number of acceleration anchor points, to perform The difference is summed, and the sum is divided by the number of acceleration anchors to obtain the average single acceleration amplitude. The RR interval sequence was resampled, and FFT power spectrum analysis was performed after applying a Hanning window; the power in the preset low-frequency band was integrated, and the natural logarithm was taken to obtain the HRV frequency domain low-frequency power; The balance ratio is obtained by dividing the low-frequency power in the HRV frequency domain by the high-frequency power in the HRV frequency domain.

10. The real-time monitoring system for autonomic neural activity based on a sliding window according to claim 5, characterized in that: For the neurological manifestations of the human body, the vagus nerve level or sympathetic nerve level is determined by inputting the associated degree coefficients into a pre-built mapping rule base. The vagus nerve grade includes mild vagus nerve, moderate vagus nerve, and severe vagus nerve; the sympathetic nerve grade includes mild sympathetic nerve, moderate sympathetic nerve, and severe sympathetic nerve. The results of vagus nerve grade or sympathetic nerve grade determination are used as the results of human neurological monitoring and evaluation.

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