CN107374616B - Autonomic nerve activity judgment method and device and electrocardiogram monitoring device - Google Patents
Autonomic nerve activity judgment method and device and electrocardiogram monitoring device Download PDFInfo
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Abstract
The invention provides a method and a device for judging autonomic nervous activity and an electrocardio monitoring device, wherein the method comprises the following steps: acquiring a single lead electrocardiosignal within a first time, and preprocessing the electrocardiosignal to generate a first signal; identifying an R-wave in the first signal and calculating an RR interval sequence; determining a first relative value between different continuous heart rate acceleration forces and the number of RR intervals and a second relative value between different continuous heart rate deceleration forces and the number of RR intervals based on the calculated RR interval sequence; fitting each first relative value and each second relative value by using a preset exponential model respectively to determine a parameter for judging the activity of the autonomic nerves; judging the activity of the autonomic nerve by using the determined parameters, and outputting a judgment result. The embodiment of the invention has the characteristics of simplicity, convenience and high precision.
Description
Technical Field
The embodiment of the invention relates to the field of autonomic nerve activity judgment, in particular to a method and equipment for judging autonomic nerve activity and electrocardiogram monitoring equipment.
Background
The heart rate refers to beat fluctuation of the inter-cardiac cycle, has important significance for maintaining the internal environment stability of a human body, and researches find that various cardiovascular diseases such as heart failure, hypertension, diabetes and the like are related to the abnormality of the heart rate fluctuation, and reflect the abnormality of the autonomic nerve function of patients.
The traditional method mostly uses a power spectrum of heart rate fluctuation for a period of time to judge the autonomic nerve function, but the spectrum analysis is based on linear hypothesis, and the recent view tends to consider that autonomic nerve regulation is a nonlinear phenomenon, so that indexes which simply reflect the sympathetic nerve or vagus nerve function are more and more concerned because the physiological state can be more simply described.
Disclosure of Invention
The embodiment of the invention provides a method and equipment for judging autonomic nervous activity by utilizing continuous heart rate acceleration force and deceleration force and electrocardio monitoring equipment.
In order to solve the above technical problem, an embodiment of the present invention provides the following technical solutions:
a method of judging autonomic nerve activity, which is applied to an autonomic nerve activity judgment apparatus, and which comprises:
acquiring a single lead electrocardiosignal within a first time, and preprocessing the electrocardiosignal to generate a first signal;
identifying an R-wave in the first signal and calculating an RR interval sequence;
determining, based on the calculated RR interval sequence, a first absolute value corresponding to the number of occurrences of each successive heart rate acceleration force in the RR interval sequence and a second absolute value corresponding to the number of occurrences of each successive heart rate deceleration force in the RR interval sequence, and determining a first relative value of each successive heart rate acceleration force based on a ratio between each first absolute value and the number of RR intervals in the RR interval sequence and a second relative value of each successive heart rate deceleration force based on a ratio between each second absolute value and the number of RR intervals in the RR interval sequence;
fitting each first relative value and each second relative value by using a preset exponential model respectively to determine a parameter for judging the activity of the autonomic nerves;
judging the activity of the autonomic nerve by using the determined parameters, and outputting a judgment result.
Wherein, the obtaining of the single lead electrocardiosignal in the first time comprises:
acquiring a single-lead electrocardiosignal within a second time according to a preset frequency;
obtaining a steady single-lead electrocardiosignal of a first time from the second time;
wherein the first time is less than the second time.
Wherein the generating of the first signal after the preprocessing of the electrocardiographic signal comprises:
filtering the electrocardiosignal by using a preset filter; wherein the preset filter comprises an 8 th order band-pass Butterworth filter.
Wherein the identifying the R-wave in the first signal comprises:
respectively acquiring a first-order differential signal and a second-order differential signal of the first signal;
dividing the second-order differential signal into a plurality of first signal intervals according to a preset length, and determining a first threshold of the second-order differential signal based on half of the minimum value average value of each first signal interval;
dividing the first signal into a plurality of second signal intervals according to the preset length, and determining a second threshold of the first signal based on an average value of differences between a maximum value and a minimum value of each second signal interval;
acquiring a minimum value of each first signal interval of the second-order differential signal by using the first threshold, and acquiring a corresponding maximum value in the first signal by using a data point corresponding to the minimum value of each first signal interval;
an R-wave is determined based on each maximum.
Wherein the calculating the RR interval sequence comprises:
calculating the sequence of RR intervals based on the determined time between R-waves.
Wherein, the fitting each first relative value and each second relative value by using a preset exponential model respectively, and the determining the parameter for judging the autonomic nervous activity includes:
fitting the first relative value of each continuous heart rate acceleration force by using a preset exponential model to determine a first parameter for judging sympathetic nerve activity;
and fitting the second relative value of each continuous heart rate deceleration force by using a preset exponential model to determine a second parameter for judging the vagus nerve activity.
Wherein the method further comprises:
determining whether sympathetic activity is excitatory, inhibitory or normal using the first parameter;
and using the second parameter to determine whether vagal activity is excitatory, inhibitory, or normal.
An autonomic nerve activity determination apparatus that applies a determination method of autonomic nerve activity as described above, and that comprises:
the device comprises a preprocessing module, a signal processing module and a signal processing module, wherein the preprocessing module is configured to acquire a single-lead electrocardiosignal within a first time, preprocess the electrocardiosignal and generate a first signal;
an identification module configured to identify an R-wave in the first signal and calculate a sequence of RR intervals;
a processing module configured to determine, based on the calculated RR interval sequence, a first absolute value corresponding to a number of occurrences of each successive heart rate acceleration force in the RR interval sequence, and a second absolute value corresponding to a number of occurrences of each successive heart rate deceleration force in the RR interval sequence, and determine a first relative value of each successive heart rate acceleration force based on a ratio between each of the first absolute values and a number of RR intervals in the RR interval sequence, and determine a second relative value of each successive heart rate deceleration force based on a ratio between each of the second absolute values and a number of RR intervals in the RR interval sequence; fitting each first relative value and each second relative value by using a preset exponential model respectively to determine a parameter for judging the activity of the autonomic nerve; and judging the activity of the autonomic nerve by using the determined parameters, and outputting a judgment result.
The preprocessing module acquires a single-lead electrocardiosignal within a second time according to a preset frequency and acquires a stable single-lead electrocardiosignal within a first time from the second time; wherein the first time is less than the second time.
Wherein the preprocessing module comprises a filter configured to perform the preprocessing by performing a filtering process on the cardiac electrical signal; wherein the filter comprises an 8 th order band-pass Butterworth filter.
Wherein the identification module is configured to obtain a first order differential signal and a second order differential signal of the first signal;
dividing the second-order differential signal into a plurality of first signal intervals according to a preset length, and determining a first threshold of the second-order differential signal based on half of the minimum value average value of each first signal interval;
dividing the first signal into a plurality of second signal intervals according to the preset length, and determining a second threshold of the first signal based on an average value of differences between a maximum value and a minimum value of each second signal interval;
acquiring a minimum value of each first signal interval of the second-order differential signal by using the first threshold, and acquiring a corresponding maximum value in the first signal by using a data point corresponding to the minimum value of each first signal interval;
an R-wave is determined based on each maximum.
Wherein the identification module is further configured to calculate the sequence of RR intervals based on the determined time between R-waves.
Wherein the processing module is further configured to fit the first relative value of each continuous heart rate acceleration force with a preset exponential model to determine a first parameter for determining sympathetic activity;
and fitting the second relative value of each continuous heart rate deceleration force by using a preset exponential model to determine a second parameter for judging the vagus nerve activity.
Wherein the processing module is configured to determine whether sympathetic activity is excitatory, inhibitory, or normal using the first parameter;
and using the second parameter to determine whether vagal activity is excitatory, inhibitory, or normal.
An electrocardiographic monitoring device comprising the autonomic nerve activity judging device as described above; further comprising:
the electrocardiosignal acquisition module is configured to acquire an electrocardiosignal of a user and transmit the electrocardiosignal to the autonomic nervous activity judgment device to judge autonomic nervous activity;
an output module configured to receive the judgment result of the autonomic nervous activity judgment device and output and display the judgment result.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
1. the method avoids the linear hypothesis based on the spectrum analysis, judges the autonomic nerve function only through the microstructure of the heart rate change, and is simple and high in precision;
2. the judgment parameters adopted in the embodiment of the invention are not limited to single parameters, and the autonomic nerve function is judged by analyzing the distribution characteristics of the continuous heart rate acceleration force and the continuous heart rate deceleration force, so that the precision is higher.
Drawings
FIG. 1 is a schematic flow chart of a method for determining autonomic nervous activity in an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating the identification of R-waves in the first signal according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of continuous heart rate acceleration forces and continuous heart rate deceleration forces in an embodiment of the present invention;
fig. 4 is a schematic structural view of an autonomic nerve activity determination apparatus in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electrocardiograph monitoring device according to an embodiment of the present invention.
Detailed Description
The following detailed description of specific embodiments of the present invention is provided in connection with the accompanying drawings, which are not intended to limit the invention.
It will be understood that various modifications may be made to the embodiments disclosed herein. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It should also be understood that, although the invention has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of the invention, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as not to obscure the present disclosure with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
The embodiment of the invention is described in detail below with reference to the accompanying drawings, and provides a method for determining autonomic nervous activity, which can be applied to autonomic nervous activity determination devices, such as an electrocardiographic monitoring device, and determines autonomic nervous function activity by using acquired continuous heart rate acceleration force and continuous heart rate deceleration force, and the autonomic nervous function is determined only by a microstructure of heart rate change without using a linear assumption based on spectral analysis, and the method is simple and high in precision.
Fig. 1 is a schematic flow chart of a method for determining autonomic nervous activity according to an embodiment of the present invention, which may include:
acquiring a single lead electrocardiosignal within a first time, and preprocessing the electrocardiosignal to generate a first signal;
identifying an R-wave in the first signal and calculating an RR interval sequence;
determining, based on the calculated RR interval sequence, a first absolute value corresponding to the number of occurrences of each successive heart rate acceleration force in the RR interval sequence and a second absolute value corresponding to the number of occurrences of each successive heart rate deceleration force in the RR interval sequence, and determining a first relative value of each successive heart rate acceleration force based on a ratio between each first absolute value and the number of RR intervals in the RR interval sequence and a second relative value of each successive heart rate deceleration force based on a ratio between each second absolute value and the number of RR intervals in the RR interval sequence;
fitting each first relative value and each second relative value by using a preset exponential model respectively to determine a parameter for judging the activity of the autonomic nerves;
judging the activity of the autonomic nerve by using the determined parameters, and outputting a judgment result.
The method for judging the activity of the autonomic nerve provided by the embodiment of the invention can be applied to equipment for judging the activity of the autonomic nerve, such as electrocardio monitoring equipment. In addition, the autonomic nervous activity evaluation device in the embodiment of the invention can be integrated into devices such as an intelligent bracelet or other electrocardio monitors, so that the device is multifunctional and more convenient for users to use, and can also be used as a single device.
When the judging device of the autonomic nervous activity can acquire the single lead electrocardiosignal of the user, the single lead electrocardiosignal in the first time is acquired, and the acquired electrocardiosignal is preprocessed to generate a first signal. According to the embodiment of the invention, the generated first signal can be used for analyzing different continuous acceleration forces and deceleration forces of the electrocardiosignal in the continuous period, so that the parameter capable of judging the autonomic nerve function activity and the judgment result are determined and output.
Specifically, in the embodiment of the present invention, the acquiring a single lead electrocardiographic signal within a first time includes:
acquiring a single-lead electrocardiosignal within a second time according to a preset frequency;
obtaining a steady single-lead electrocardiosignal of a first time from the second time;
wherein the first time is less than the second time.
That is, a preprocessing module may be included in the apparatus for determining autonomic nervous activity, and the preprocessing module may collect the single lead electrocardiosignals in the second time range from the obtained electrocardiosignals according to a preset frequency, and may obtain the stable electrocardiosignals in the second time range, such as the stable single lead electrocardiosignals in the first time range, in order to improve the accuracy of the determination. For example, the predetermined frequency f in the embodiment of the present inventionsMay be 256HZ, the second time may be greater than 6 hours, and the first time may be less than or equal to 5 hours. The sampling frequency, the first time and the second time may be set according to the requirement, but the invention is not limited thereto.
Further, in this embodiment of the present invention, the generating the first signal after preprocessing the cardiac signal may include: filtering the electrocardiosignal by using a preset filter; the preset filter comprises an 8-order band-pass Butterworth filter, the filtering range of the 8-order band-pass Butterworth filter is 0.05-45Hz, noise such as power frequency interference in the electrocardiosignals can be removed through the filtering operation, and the accuracy of the electrocardiosignals is guaranteed.
In addition, as shown in fig. 2, a schematic flow chart of identifying an R wave in the first signal according to an embodiment of the present invention is shown, where the schematic flow chart may include:
respectively acquiring a first-order differential signal and a second-order differential signal of the first signal;
dividing the second-order differential signal into a plurality of first signal intervals according to a preset length, and determining a first threshold of the second-order differential signal based on half of the minimum value average value of each first signal interval;
dividing the first signal into a plurality of second signal intervals according to the preset length, and determining a second threshold of the first signal based on an average value of differences between a maximum value and a minimum value of each second signal interval;
acquiring a minimum value of each first signal interval of the second-order differential signal by using the first threshold, and acquiring a corresponding maximum value in the first signal by using a data point corresponding to the minimum value of each first signal interval;
an R-wave is determined based on each maximum.
That is, the embodiment of the present invention may identify the R wave in the first signal by using a differential identification method. The specific process is as follows:
let y (n) be the first signal after preprocessing, where n is 1,2 … l, where n represents the number of sampling points, and y (n) represents the intensity of the first signal. First-order difference signals d (n) ═ y (n +1) -y (n) of the first signal and second-order difference signals e (n) ═ d (n +1) -d (n) of the first signal are obtained, where n denotes the number of points sampled, i.e., the nth sample point.
Dividing e (n) according to a preset length, calculating a minimum value of each interval, calculating a mean value of each minimum value, and taking half of the mean value as a minimum value threshold (first threshold) of e (n), namely:
wherein th1For the first threshold, k represents the number of minima, and min () represents the minimum function.
Similarly, y (n) is divided by a preset length, the difference between the maximum value and the minimum value of each interval is obtained, then the average value of the difference values of the intervals is obtained, and the value is used as the threshold value (i.e. the second threshold) of the QRS amplitude of the filtered signal, namely:
therein, th2Representing a second threshold, k representing a maximum and a minimumMax () represents a maximum function and min () represents a minimum function.
Further, obtaining e (n) < th1The minimum value min (e) of each signal section in (a) is set as ime (i), i is 1,2 … m (m is the number of local minimum values), the maximum value corresponding to each minimum value in y (n) is r (i), and the position is n (i) is ime (i) -2; then the R-wave has been detected, and the sequence of the time between the R-wave and the R-wave is the RR interval:
where RR (i) denotes a time difference between an ith RR interval, i.e., a time point of an i +1 th R wave, and an ith R-wave time point.
Also, in an embodiment of the invention, after determining the RR interval sequence, the frequency of different continuous heart rate acceleration forces and continuous heart rate deceleration rates (i.e., the first relative value and the second relative value described below) within the RR interval sequence may be calculated.
Specifically, the RR interval sequence may be used to obtain the continuous heart rate deceleration force during the continuous period of each heart rate acceleration and the continuous heart rate acceleration force during the continuous period of each heart rate deceleration. And determining a first absolute value representing the number of occurrences of each successive heart rate acceleration force in the sequence of RR intervals, and determining a second absolute value representing the number of occurrences of each successive heart rate deceleration force in the sequence of RR intervals, and determining a first relative value for each successive heart rate acceleration force using a ratio between the first absolute values and the number of RR intervals in the sequence of RR intervals, and determining a second relative value for each successive heart rate deceleration force based on a ratio between the second absolute values and the number of RR intervals in the sequence of RR intervals. The determined first and second relative values may indicate the frequency of occurrence of each of the continuous heart rate acceleration and deceleration forces in the sequence of RR intervals.
The RR interval sequence(s) is used as a vertical coordinate, the sequence numbers of the cardiac cycles (the number of RR intervals) are used as horizontal coordinates, a sequence chart of heart rate continuous deceleration (acceleration) with different cycle values is made, and then the respective absolute values of continuous heart rate deceleration forces (acceleration forces) with different continuous cycles can be calculated. Such as: the RR interval jump-by-jump lengthening means that the heart rate is slowed down jump-by-jump, if RR (i) is more than or equal to RR (i +1) and less than or equal to RR (i +2) … and more than or equal to RR (i + n) and more than or equal to RR (i + n +1), the deceleration period is n and is marked as D (n); otherwise, the shortening of the RR interval jump by jump indicates that the heart rate jump by jump is accelerated, if RR (i) is less than or equal to RR (i +1) is more than or equal to RR (i +2) … is more than or equal to RR (i + n) is less than or equal to RR (i + n +1), the acceleration period is n, and is marked as A (n); fig. 3 is a schematic diagram of continuous heart rate acceleration and deceleration forces in an embodiment of the present invention. If the RR interval R wave value continuously rises from 0.732s to 0.836s and includes 5 rising points, it may be recorded as the continuous heart rate deceleration force DR5, so as to obtain AR2, DR4, AR5, etc. as shown in fig. 3, and at the same time, the frequency of DRn occurrence (second relative value) and the frequency of ARn occurrence (first relative value) may be counted, that is, the number of times DRn occurs (second absolute value) may be first calculated, the number of times ARn occurs may be calculated, the number of times ARn occurs is a first absolute value, then the sum of the absolute values cd (n) of the second absolute value and ca (n) of the first absolute value may be counted, where n is greater than or equal to 2.
Dividing CD (n) and CA (n) by total number RR of RR intervals in the whole recording time periodtotalThen, the relative values of the continuous heart rate deceleration force DRn and the continuous heart rate acceleration force ARn which last for different periods can be obtained: DRn ═ CD (n)/RRtotal，2≤n≤10ARn＝CA(n)/RRtotal。
In addition, in the embodiment of the present invention, the fitting the continuous heart rate acceleration force and the continuous heart rate deceleration force by using the preset exponential model respectively, and determining the parameter for determining the autonomic nervous activity may include:
fitting the continuous heart rate acceleration force by using a preset index model to determine a first parameter for judging sympathetic nerve activity;
and fitting the continuous heart rate deceleration force by using a preset exponential model to determine a second parameter for judging the vagus nerve activity.
The preset model may have an expression of y ═ ae-bxThat is, the exponential model y ═ ae may be used in the embodiment of the present invention-bxTo the tested AR2 ℃Distribution of ARn and DR 2-DRn is fitted, wherein the parameter b (first parameter) fitted by AR 2-ARn reflects sympathetic activity, and the parameter b (second parameter) fitted by DR 2-DR 10 reflects vagus nerve activity. Wherein the determining autonomic nervous activity using the determined parameters comprises: determining whether sympathetic activity is excitatory, inhibitory or normal using the first parameter; and using the second parameter to determine whether vagal activity is excitatory, inhibitory, or normal.
In the embodiment of the present invention, it may be determined that the sympathetic activity is excitatory, inhibitory or normal by combining the first parameter, the age, the sex, and other parameter information related to the user, for example, when the first parameter is within a first preset range, it may be determined that the sympathetic activity is normal; below the first predetermined range, sympathetic activity may be judged as inhibition, and above the first predetermined range, sympathetic activity may be judged as excitation. It should be noted here that, for different users, values of the first preset range are also different, and those skilled in the art may configure the corresponding first preset range according to situations of different users.
Similarly, when the second parameter is within a second preset range, the activity of the vagus nerve can be judged to be normal; below the second predetermined range may be judged vagal activity as inhibitory and above the second predetermined range may be judged vagal activity as excitatory. It should be noted here that, for different users, the values of the second preset range are also different, and those skilled in the art may configure the corresponding second preset range according to the situations of different users.
For example, in the present invention, 30 patients with simple snoring (age 47.8 ± 15.6 years old, male/female 3:1, apnea hypopnea index 1.3 ± 0.8) and 30 patients with severe obstructive sleep apnea (age 51.2 ± 11.3 years old, male/female 3:1, apnea hypopnea index 45.7 ± 8.5) were compared, and the patients with severe obstructive sleep apnea were mostly suffered from sympathetic hyperexcitability and vagus nerve inhibition, and the results of the use rank sum test showed that b was a good candidate for the treatment of severe obstructive sleep apneaARAnd bDRAll have displays among groupsSignificant difference (b)AR：1.56±0.68vs 1.17±0.43，p＝0.037；bDR: 1.51 ± 0.74vs 1.03 ± 0.37, and p is 0.011), which proves that the parameter can be used as a parameter basis for judging the function of the autonomic nerve.
In conclusion, the method avoids the linear hypothesis based on the spectrum analysis, judges the autonomic nervous function activity only through the microstructure of the heart rate change, and is simple and high in precision; in addition, the judgment parameters adopted in the embodiment of the invention are not limited to single parameters, and the autonomic nerve function is judged by analyzing the distribution characteristics of the continuous heart rate acceleration force and the continuous heart rate deceleration force, so that the precision is higher.
In addition, an embodiment of the present invention further provides an autonomic nervous activity determination apparatus to which the method for determining autonomic nervous activity described in the above embodiment is applied, and as shown in fig. 4, the apparatus may include: a pre-processing module 100, an identification module 200 and a processing module 300.
The preprocessing module 100 may obtain a single-lead electrocardiosignal within a first time, and preprocess the electrocardiosignal to generate a first signal;
the identification module 200 may receive the first signal from the pre-processing module 100 and identify R-waves in the first signal and calculate RR interval sequences;
the processing module 300 may determine, based on the RR interval sequence calculated by the identification module, a first absolute value corresponding to the number of occurrences of each successive heart rate acceleration force in the RR interval sequence, and a second absolute value corresponding to the number of occurrences of each successive heart rate deceleration force in the RR interval sequence, based on the calculated RR interval sequence, and determine a first relative value of each successive heart rate acceleration force based on a ratio between each of the first absolute values and the number of RR intervals in the RR interval sequence, and determine a second relative value of each successive heart rate deceleration force based on each of the second absolute values and the number of RR intervals in the RR interval sequence; fitting by using each first relative value and each second relative value to determine a parameter for judging the activity of autonomic nerves; and judging the activity of the autonomic nerve by using the determined parameters, and outputting a judgment result.
The autonomic nervous activity determination device provided by the embodiment of the invention can be integrated in an electrocardiogram monitoring device, and can also be integrated in an intelligent bracelet or other electrocardiogram monitoring devices, so that the device is multifunctional and more convenient for users to use, but the invention is not limited thereto. The autonomic nerve activity determination apparatus in the embodiment of the present invention may also be used as a separate detection apparatus.
When acquiring an electrocardiographic signal of a user, the preprocessing module 100 in the embodiment of the present invention may acquire a single-lead electrocardiographic signal within a first time, and generate a first signal after preprocessing the acquired electrocardiographic signal. According to the embodiment of the invention, the generated first signal can be utilized to analyze the continuous acceleration force and deceleration force of the electrocardiosignal, so that the autonomic nerve function can be determined and output.
Specifically, in the embodiment of the present invention, the acquiring, by the preprocessing module 100, the single leadership telecommunication number in the first time includes: acquiring a single-lead electrocardiosignal within a second time according to a preset frequency; obtaining a steady single-lead electrocardiosignal of a first time from the second time; wherein the first time is less than the second time.
Namely, the preprocessing module can acquire the single lead electrocardiosignal in the second time range from the acquired electrocardiosignals according to the preset frequency, and can acquire the stable electrocardiosignals in the second time range, such as the stable single lead electrocardiosignals in the first time range, in order to improve the accuracy of judgment. For example, the predetermined frequency f in the embodiment of the present inventionsThe second time period may be greater than 6 hours and the first time period may be less than or equal to 5 hours at 256 HZ.
Further, the generating of the first signal after the preprocessing module preprocesses the electrocardiographic signal in the embodiment of the present invention may include: and carrying out filtering processing on the electrocardiosignals by utilizing a preset filter. Namely, the preprocessing module may include a preset filter, and the preset filter may perform filtering processing on the electrocardiographic signal, so as to remove noise such as power frequency interference in the electrocardiographic signal, wherein the preset filter includes an 8-order band-pass butterworth filter, a filtering range of the 8-order band-pass butterworth filter is 0.05 to 45Hz, and the noise such as power frequency interference in the electrocardiographic signal can be removed through the filtering operation, thereby ensuring accuracy of the electrocardiographic signal.
The identification module 200 in the embodiment of the present invention may identify an R wave in the first signal, and the process may include:
respectively acquiring a first-order differential signal and a second-order differential signal of the first signal;
dividing the second-order differential signal into a plurality of first signal intervals according to a preset length, and determining a first threshold of the second-order differential signal based on half of the minimum value average value of each first signal interval;
dividing the first signal into a plurality of second signal intervals according to the preset length, and determining a second threshold of the first signal based on an average value of differences between a maximum value and a minimum value of each second signal interval;
acquiring a minimum value of each first signal interval of the second-order differential signal by using the first threshold, and acquiring a corresponding maximum value in the first signal by using a data point corresponding to the minimum value of each first signal interval;
an R-wave is determined based on each maximum.
That is to say, the identification module 200 in the embodiment of the present invention may identify the R wave in the first signal by using a differential identification method. The specific process is as follows:
let y (n) be the first signal after preprocessing, where n is 1,2 … l, where n represents the number of sampling points, and y (n) represents the intensity of the first signal. The identifying module 200 may obtain first-order difference signals d (n) ═ y (n +1) -y (n) of the first signal, and second-order difference signals e (n) ═ d (n +1) -d (n) of the first signal, and divide e (n) according to a preset length to obtain minimum values of each signal interval, and then calculate a mean value of each minimum value, and use half of the mean value as a minimum value threshold (first threshold) of e (n), that is:
wherein th1For the first threshold, k represents the number of minima, and min () represents the minimum function.
Similarly, the identifying module 200 may further divide y (n) according to a preset length, calculate a difference between a maximum value and a minimum value of each interval, calculate an average value of difference values of each interval, and use the value as a threshold value (i.e., a second threshold) of the QRS amplitude of the filtered signal, that is:
therein, th2Represents the second threshold, k represents the number of maximum and minimum values, max () represents the maximum function, and min () represents the minimum function.
Further, the recognition module 200 can also obtain e (n) < th1The minimum value min (e) of each signal section in (a) is set as ime (i), i is 1,2 … m (m is the number of local minimum values), the maximum value corresponding to each minimum value in y (n) is r (i), and the position is n (i) is ime (i) -2; then the R-wave has been detected, and the sequence of the time between the R-wave and the R-wave is the RR interval:
where RR (i) denotes a time difference between an ith RR interval, i.e., a time point of an i +1 th R wave, and an ith R-wave time point.
Further, in the embodiment of the present invention, a flowchart of a method for determining a continuous heart rate acceleration force and a continuous heart rate deceleration force of an electrocardiograph signal by the processing module 300 may include:
firstly, continuous heart rate deceleration force in each heart rate acceleration duration period and continuous heart rate acceleration force in each heart rate deceleration duration period are obtained by using the RR interval sequence. And determining a first absolute value representing the number of occurrences of each successive heart rate acceleration force in the sequence of RR intervals, and determining a second absolute value representing the number of occurrences of each successive heart rate deceleration force in the sequence of RR intervals, and determining a first relative value for each successive heart rate acceleration force using a ratio between the first absolute values and the number of RR intervals in the sequence of RR intervals, and determining a second relative value for each successive heart rate deceleration force based on a ratio between the second absolute values and the number of RR intervals in the sequence of RR intervals. The determined first and second relative values may indicate the frequency of occurrence of each of the continuous heart rate acceleration and deceleration forces in the sequence of RR intervals. The RR interval sequence(s) is used as a vertical coordinate, the sequence number of the cardiac cycle is used as a horizontal coordinate, a sequence chart of heart rate continuous deceleration (acceleration) with different cycle values is made, and then the respective absolute values of continuous heart rate deceleration force (acceleration force) with different duration cycles can be calculated. Such as: the RR interval jump-by-jump lengthening means that the heart rate is slowed down jump-by-jump, if RR (i) is more than or equal to RR (i +1) and less than or equal to RR (i +2) … and more than or equal to RR (i + n) and more than or equal to RR (i + n +1), the deceleration period is n and is marked as D (n); otherwise, the shortening of the RR interval jump by jump indicates that the heart rate jump by jump is accelerated, if RR (i) is less than or equal to RR (i +1) is more than or equal to RR (i +2) … is more than or equal to RR (i + n) is less than or equal to RR (i + n +1), the acceleration period is n, and is marked as A (n); fig. 3 is a schematic diagram of continuous heart rate acceleration and deceleration forces in an embodiment of the present invention. The RR interval R-wave value of 0.732s to 0.836s continuously rises and includes 5 rising points in total, as a continuous period of the continuous heart rate deceleration force, it can be recorded as DR5, so that AR2, DR4, AR5, etc. shown in fig. 3 can be obtained, and at the same time, the frequency of DRn occurrence (second relative value) and the frequency of ARn occurrence (first relative value) can be counted, i.e., the number of times DRn occurs (second absolute value) can be calculated first, and the number of times ARn is calculated (first absolute value), then the continuous heart rate deceleration forces with different continuous periods (n 2-10) in the first signal and the continuous heart rate acceleration forces can be accumulated to form an absolute value cd (n) as the sum of the second absolute value, and ca (n) as the sum of the first absolute value, where n is greater than or equal to 2.
Dividing CD (n) and CA (n) by total number RR of RR intervals in the whole recording time periodtotalThen, the relative values of the continuous heart rate deceleration force DRn and the continuous heart rate acceleration force ARn which last for different periods can be obtained: DRn ═ CD (n)/RRtotal，2≤n≤10ARn＝CA(n)/RRtotal。
In addition, in the embodiment of the present invention, the fitting the continuous heart rate acceleration force and the continuous heart rate deceleration force by using the preset exponential model respectively, and determining the parameter for determining the autonomic nervous activity may include:
fitting the continuous heart rate acceleration force by using a preset index model to determine a first parameter for judging sympathetic nerve activity;
and fitting the continuous heart rate deceleration force by using a preset exponential model to determine a second parameter for judging the vagus nerve activity.
The preset model may have an expression of y ═ ae-bxThat is, the exponential model y ═ ae may be used in the embodiment of the present invention-bxThe distribution of tested AR 2-ARn and DR 2-DRn was fitted, where the parameter b (first parameter) fitted by AR 2-ARn reflects sympathetic activity and the parameter b (second parameter) fitted by DR 2-DR 10 reflects vagal activity. Wherein the determining autonomic nervous activity using the determined parameters comprises:
determining whether sympathetic activity is excitatory, inhibitory or normal using the first parameter; and using the second parameter to determine whether vagal activity is excitatory, inhibitory, or normal.
In the embodiment of the present invention, it may be determined that the sympathetic activity is excitatory, inhibitory or normal by combining the first parameter, the age, the sex, and other parameter information related to the user, for example, when the first parameter is within a first preset range, it may be determined that the sympathetic activity is normal; below the first predetermined range, sympathetic activity may be judged as inhibition, and above the first predetermined range, sympathetic activity may be judged as excitation. It should be noted here that, for different users, values of the first preset range are also different, and those skilled in the art may configure the corresponding first preset range according to situations of different users.
Similarly, when the second parameter is within a second preset range, the activity of the vagus nerve can be judged to be normal; below the second predetermined range may be judged vagal activity as inhibitory and above the second predetermined range may be judged vagal activity as excitatory. It should be noted here that, for different users, the values of the second preset range are also different, and those skilled in the art may configure the corresponding second preset range according to the situations of different users.
For example, in the example of the present invention, 30 patients with simple snoring (age 47.8 ± 15.6 years old, male/female 3:1, apnea hypopnea index 1.3 ± 0.8) and 30 patients with severe obstructive sleep apnea (age 51.2 ± 11.3 years old, male/female 3:1, apnea hypopnea index 45.7 ± 8.5) are compared, and the patients with severe obstructive sleep apnea are mostly with sympathetic hyperexcitability and vague nerve inhibition, and the result of the use rank and test shows that b is the condition of sympathetic hyperexcitability and vague nerve inhibitionARAnd bDRSignificant differences were observed between groups (b)AR：1.56±0.68vs 1.17±0.43，p＝0.037；bDR: 1.51 ± 0.74vs 1.03 ± 0.37, and p is 0.011), which proves that the parameter can be used as a parameter basis for judging the function of the autonomic nerve.
In addition, the first preset range and the second preset range may be set according to the age and physical condition of the user, and a person skilled in the art may know the selection rule of the first preset range and the second preset range, which is not described herein again.
In addition, the processing module 300 may output the determination result to other electronic devices through the communication device, or may output the determination result through a display module or a voice output module in the determination device, so as to facilitate the user to obtain the determination information of the neural function.
In addition, as shown in fig. 5, an embodiment of the present invention further provides an electrocardiograph monitoring device, which includes the autonomic nervous activity determination device 1 described in the foregoing embodiment, an electrocardiograph signal acquisition module 2, and an output module 3, where the electrocardiograph signal acquisition module 2 can acquire an electrocardiograph signal of a user, and transmit the electrocardiograph signal to the autonomic nervous activity determination device 1 to determine autonomic nervous activity; the electrocardiosignal acquisition module 2 may include a sensor for detecting an electrocardiosignal, or may include other electronic devices capable of acquiring an electrocardiosignal. The output module 3 may receive the judgment result of the autonomic nervous activity judgment device, and output and display the judgment result.
The output module 3 in the embodiment of the present invention may include a display module, a voice output module, and the like, and the specific output module 3 may be connected to the processing module 300 in the autonomic nervous activity determination apparatus, so as to obtain a determination result, and display the determination result to a user or an operator in a display manner or a voice output manner, which is convenient for the user to use.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the electronic device to which the data processing method described above is applied may refer to the corresponding description in the foregoing product embodiments, and details are not repeated herein.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.
Claims (15)
1. A method of judging autonomic nerve activity, which is applied to an autonomic nerve activity judgment apparatus, and which comprises:
acquiring a single lead electrocardiosignal within a first time, and preprocessing the electrocardiosignal to generate a first signal;
identifying an R-wave in the first signal and calculating an RR interval sequence;
determining, based on the calculated RR interval sequence, a first absolute value corresponding to the number of occurrences of each successive heart rate acceleration force in the RR interval sequence and a second absolute value corresponding to the number of occurrences of each successive heart rate deceleration force in the RR interval sequence, and determining a first relative value of each successive heart rate acceleration force based on a ratio between each first absolute value and the number of RR intervals in the RR interval sequence and a second relative value of each successive heart rate deceleration force based on a ratio between each second absolute value and the number of RR intervals in the RR interval sequence;
fitting each first relative value and each second relative value by using a preset exponential model respectively to determine a parameter for judging the activity of the autonomic nerves;
judging the activity of the autonomic nerve by using the determined parameters, and outputting a judgment result.
2. The method of claim 1, wherein said acquiring a single lead cardiac signal over a first time comprises:
acquiring a single-lead electrocardiosignal within a second time according to a preset frequency;
obtaining a steady single-lead electrocardiosignal of a first time from the second time;
wherein the first time is less than the second time.
3. The method of claim 1, wherein the pre-processing the cardiac signal to generate a first signal comprises:
filtering the electrocardiosignal by using a preset filter; wherein the preset filter comprises an 8 th order band-pass Butterworth filter.
4. The method of claim 1, wherein the identifying the R-wave in the first signal comprises:
respectively acquiring a first-order differential signal and a second-order differential signal of the first signal;
dividing the second-order differential signal into a plurality of first signal intervals according to a preset length, and determining a first threshold of the second-order differential signal based on half of the minimum value average value of each first signal interval;
dividing the first signal into a plurality of second signal intervals according to the preset length, and determining a second threshold of the first signal based on an average value of differences between a maximum value and a minimum value of each second signal interval;
acquiring a minimum value of each first signal interval of the second-order differential signal by using the first threshold, and acquiring a corresponding maximum value in the first signal by using a data point corresponding to the minimum value of each first signal interval;
an R-wave is determined based on each maximum.
5. The method of claim 4, wherein the calculating the sequence of RR intervals comprises:
calculating the sequence of RR intervals based on the determined time between R-waves.
6. The method of claim 1, wherein the fitting each of the first relative values and each of the second relative values with a preset exponential model respectively, and the determining the parameter for determining the autonomic nervous activity comprises:
fitting the first relative value of each continuous heart rate acceleration force by using a preset exponential model to determine a first parameter for judging sympathetic nerve activity;
and fitting the second relative value of each continuous heart rate deceleration force by using a preset exponential model to determine a second parameter for judging the vagus nerve activity.
7. The method of claim 6, wherein the determining autonomic nerve activity using the determined parameters comprises:
determining whether sympathetic activity is excitatory, inhibitory or normal using the first parameter;
and using the second parameter to determine whether vagal activity is excitatory, inhibitory, or normal.
8. An autonomic nerve activity determination apparatus to which a determination method of autonomic nerve activity as claimed in any one of claims 1 to 7 is applied, and comprising:
the device comprises a preprocessing module, a signal processing module and a signal processing module, wherein the preprocessing module is configured to acquire a single-lead electrocardiosignal within a first time, preprocess the electrocardiosignal and generate a first signal;
an identification module configured to identify an R-wave in the first signal and calculate a sequence of RR intervals;
a processing module configured to determine, based on the calculated RR interval sequence, a first absolute value corresponding to a number of occurrences of each successive heart rate acceleration force in the RR interval sequence, and a second absolute value corresponding to a number of occurrences of each successive heart rate deceleration force in the RR interval sequence, and determine a first relative value of each successive heart rate acceleration force based on a ratio between each of the first absolute values and a number of RR intervals in the RR interval sequence, and determine a second relative value of each successive heart rate deceleration force based on a ratio between each of the second absolute values and a number of RR intervals in the RR interval sequence; fitting each first relative value and each second relative value by using a preset exponential model respectively to determine a parameter for judging the activity of the autonomic nerve; and judging the activity of the autonomic nerve by using the determined parameters, and outputting a judgment result.
9. The apparatus of claim 8, wherein the preprocessing module acquires the single-lead electrocardiosignal at a second time according to a preset frequency and obtains a stable single-lead electrocardiosignal at a first time from the second time; wherein the first time is less than the second time.
10. The apparatus of claim 8, wherein said preprocessing module comprises a filter configured for said preprocessing by filtering said cardiac electrical signal; wherein the filter comprises an 8 th order band-pass Butterworth filter.
11. The device of claim 8, wherein the identification module is configured to obtain a first order differential signal and a second order differential signal of the first signal;
dividing the second-order differential signal into a plurality of first signal intervals according to a preset length, and determining a first threshold of the second-order differential signal based on half of the minimum value average value of each first signal interval;
dividing the first signal into a plurality of second signal intervals according to the preset length, and determining a second threshold of the first signal based on an average value of differences between a maximum value and a minimum value of each second signal interval;
acquiring a minimum value of each first signal interval of the second-order differential signal by using the first threshold, and acquiring a corresponding maximum value in the first signal by using a data point corresponding to the minimum value of each first signal interval;
an R-wave is determined based on each maximum.
12. The device of claim 10, wherein the identification module is further configured to calculate the sequence of RR intervals based on a determined time between R-waves.
13. The apparatus of claim 8, wherein the processing module is further configured to determine a first parameter for determining sympathetic activity by fitting a preset exponential model to the first relative value for each successive cardioversion acceleration force;
and fitting the second relative value of each continuous heart rate deceleration force by using a preset exponential model to determine a second parameter for judging the vagus nerve activity.
14. The device of claim 13, wherein the processing module is configured to determine sympathetic activity as excitatory, inhibitory, or normal using the first parameter;
and using the second parameter to determine whether vagal activity is excitatory, inhibitory, or normal.
15. An electrocardiographic monitoring device comprising the autonomic nerve activity determination device according to any one of claims 8 to 14; further comprising:
the electrocardiosignal acquisition module is configured to acquire an electrocardiosignal of a user and transmit the electrocardiosignal to the autonomic nervous activity judgment device to judge autonomic nervous activity;
an output module configured to receive the judgment result of the autonomic nervous activity judgment device and output and display the judgment result.
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