A method and system for processing patient health warnings based on multiple cardiac monitoring data

Abstract

The present invention discloses a method and system for processing patient health warnings of multiple cardiac monitoring data. The method includes the following operating steps: collecting cardiac monitoring data for a target patient, preprocessing the sampling frequency and waveform of the cardiac monitoring data to obtain an electrocardiogram signal; confirming the starting point and ending point of the waveform for the electrocardiogram signal, and using the starting point and ending point of the waveform to find the inflection point of the waveform trend; extracting the heart rate mutation characteristics according to the inflection point of the waveform trend, and identifying the evaluation result of the cardiac health risk symptoms for the heart rate mutation characteristics; and giving a warning according to the evaluation result of the cardiac health risk symptoms.

Description

Technical Field

[0001] The present invention relates to the field of cardiology, and particularly to a method and system for processing patient health warnings based on multiple cardiac monitoring data. Background Art

[0002] Heart diseases, especially arrhythmia, heart failure and other diseases, have become health problems with relatively high mortality and disability rates globally.

[0003] With the continuous development of medical technology, cardiac monitoring devices and technologies have gradually matured, making it possible to monitor the cardiac health status of patients in real time.

[0004] These devices can record the electrical activity of the heart in detail through electrocardiogram (ECG) signals, providing important diagnostic information for clinicians.

[0005] However, despite the widespread use of electrocardiograms in clinical practice, due to the influence of factors such as noise, motion artifacts, and environmental interference, the original ECG signals often contain a large amount of noise and incomplete information, affecting the signal quality and reducing the accuracy and reliability of analysis.

[0006] Therefore, how to effectively preprocess the ECG signals to improve the data quality and the accuracy of subsequent analysis is a major challenge in current cardiac monitoring technology.

[0007] In addition, the complexity of ECG signals requires highly accurate waveform analysis techniques for the detection of heart diseases.

[0008] Traditional electrocardiogram analysis methods mostly rely on manual interpretation, which is highly subjective and time-consuming, and cannot achieve fast, automatic real-time monitoring and warning.

[0009] Therefore, how to efficiently extract valuable heart disease features from a large amount of ECG data through intelligent analysis methods and timely discover potential cardiac health risks has become an important research and development direction. Summary of the Invention

[0010] The purpose of the present invention is to provide a method and system for processing patient health warnings based on multiple cardiac monitoring data, which solves the above-mentioned technical problems pointed out in the prior art.

[0011] The present invention provides a method for processing patient health warnings based on multiple cardiac monitoring data, including the following operating steps:

[0012] Collect cardiac monitoring data for the target patient, and perform sampling frequency and waveform preprocessing on the cardiac monitoring data to obtain an electrocardiogram signal;

[0013] Confirm the starting point and ending point of the waveform for the electrocardiogram signal, and use the starting point and ending point of the waveform to find the inflection point of the waveform trend; extract the heart rate mutation characteristics according to the waveform trend inflection point, and identify the heart rate mutation characteristics to output the evaluation result of the heart health risk symptoms;

[0014] Give an early warning according to the evaluation result of the heart health risk symptoms.

[0015] The present invention also proposes a patient health early warning processing system for multiple cardiac monitoring data, including: a collection module; an analysis module; an early warning module;

[0016] The collection module is used to collect cardiac monitoring data for the target patient, and perform sampling frequency and waveform preprocessing on the cardiac monitoring data to obtain an electrocardiogram signal;

[0017] The analysis module is used to confirm the starting point and ending point of the waveform for the electrocardiogram signal, and use the starting point and ending point of the waveform to find the inflection point of the waveform trend; extract the heart rate mutation characteristics according to the waveform trend inflection point, and identify the heart rate mutation characteristics to output the evaluation result of the heart health risk symptoms;

[0018] The early warning module is used to give an early warning according to the evaluation result of the heart health risk symptoms.

[0019] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages:

[0020] Analyzing the above-mentioned patient health early warning processing method and system for multiple cardiac monitoring data provided by the present invention, it can be seen that in specific applications, by performing sampling frequency and waveform preprocessing on the cardiac monitoring data, noise can be removed to obtain a stable and clear electrocardiogram signal;

[0021] Further, a search window is set for the electrocardiogram (ECG) signal to slide and search for characteristic points, obtaining heart rate mutation characteristic points; the first derivative of the waveform of the ECG signal is calculated to identify the change rate, and the second derivative is calculated through the change rate to obtain the acceleration, which helps to reveal the inflection points of the heart rate mutation characteristic points of the ECG signal and understand the change trend of the ECG signal; a preset acceleration change threshold e is set, and it is judged whether the acceleration of the change rate is greater than the acceleration change threshold e, so as to determine whether there are fluctuation points in the ECG signal, and then extract the stable part or transition section of the waveform in the ECG; the waveform of the ECG signal is traversed to find the local maximum points and local minimum points. The local maximum points represent the peak values of the R wave and T wave, and the local minimum points represent the trough values of the Q wave and S wave, so as to obtain the starting point and ending point of the waveform, which are used as the QRS complex to represent the recovery or calm stage of the ECG; the R wave of the QRS complex is extracted to calculate the RR interval, which provides data on the heartbeat cycle and is the basis for studying cardiac rhythm and heart rate fluctuations; calculating the difference between RR intervals can reflect problems such as abnormal heartbeats and arrhythmias; calculating the average value between RR intervals, and calculating the change rate of the RR interval through the average value and the difference value, so as to obtain the periodic gradual change characteristics. By extracting these gradual change characteristics, different physiological or emotional states (such as exercise, stress, relaxation, etc.) can be identified; the periodic gradual change characteristics are used to screen abnormal cycle sequences, and the abnormal fluctuations of the abnormal cycle sequences are marked through the waveform trend inflection points to obtain the heart rate mutation characteristics, and it is judged whether the target patient has acute heart health problems; the heart rate mutation characteristics and periodic gradual change characteristics are used to identify the cardiac results (identifying the heart rate mutation characteristics and periodic gradual change characteristics and outputting the evaluation results of the heart), and it is judged whether the target patient has acute and short-term heart diseases, so as to issue a warning. Description of the Drawings

[0022] Figure 1 It is the main flowchart of a method for processing patient health warnings of multiple cardiac monitoring data in Embodiment 1;

[0023] Figure 2 It is the specific flowchart of cardiac identification of a method for processing patient health warnings of multiple cardiac monitoring data in Embodiment 1;

[0024] Figure 3 It is a schematic diagram for confirming the QRS complex of a method for processing patient health warnings of multiple cardiac monitoring data in Embodiment 1;

[0025] Figure 4 It is the flowchart for judging the evaluation results of the heart using the RR interval of a method for processing patient health warnings of multiple cardiac monitoring data in Embodiment 1;

[0026] Figure 5Flow chart for identifying the heart using the periodic gradual change feature and heart rate mutation feature of a patient health warning processing method for multiple cardiac monitoring data in Embodiment 1;

[0027] Figure 6 Flow chart for identifying the heart according to the QT interval of a patient health warning processing method for multiple cardiac monitoring data in Embodiment 1;

[0028] Figure 7 Schematic diagram of the QT interval of a patient health warning processing method for multiple cardiac monitoring data in Embodiment 1;

[0029] Figure 8 Flow chart of a patient health warning processing system for multiple cardiac monitoring data in Embodiment 2.

[0030] Label: Acquisition module 10; Analysis module 20; Warning module 30. Detailed implementation manners

[0031] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments.

[0032] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0033] Next, the present invention will be further described in detail through specific embodiments in conjunction with the accompanying drawings.

[0034] Embodiment 1

[0035] As Figure 1 shown, the present invention provides a patient health warning processing method for multiple cardiac monitoring data, including the following operating steps:

[0036] S1: Collect cardiac monitoring data for the target patient, perform sampling frequency and waveform preprocessing on the cardiac monitoring data, and obtain an electrocardiogram signal;

[0037] It should be noted that by performing sampling frequency and waveform preprocessing on the cardiac monitoring data, noise can be removed and the signal quality can be optimized, thereby ensuring the accuracy of subsequent analysis; the obtained electrocardiogram signal provides a stable and clear data basis for subsequent cardiac health analysis;

[0038] S2: Confirm the starting point and ending point of the waveform of the electrocardiogram signal, use the starting point and ending point of the waveform to find the inflection point of the waveform trend; extract the heart rate mutation feature according to the inflection point of the waveform trend, and identify the heart rate mutation feature to output the evaluation result of the cardiac health risk symptom;

[0039] It should be noted that by analyzing the starting point and ending point of the waveform and finding the inflection point of the waveform trend, abnormal changes (such as sudden heart rate changes) in the electrocardiogram signal can be efficiently detected;

[0040] Determining the characteristics of sudden heart rate changes and evaluating the results of heart diseases based on these characteristics as acute risk or chronic risk helps to identify potential heart problems in advance, including arrhythmia, heart failure, etc., and has relatively important reference value especially in judging the situation of sinus arrhythmia;

[0041] S3: Give an early warning according to the evaluation results of heart health risk symptoms;

[0042] It should be noted that according to the above technical solution, based on the evaluation results of heart diseases from electrocardiogram signals, an early warning can be issued in time to help medical staff discover and handle potential heart risks; such an early warning can provide enough time for intervention before the disease develops to the acute stage, thereby reducing the risk of acute attacks and improving the survival rate of patients;

[0043] Specifically, as Figure 2 shown, in step S2, confirm the starting point and ending point of the waveform of the electrocardiogram signal, and use the starting point and ending point of the waveform to find the inflection point of the waveform trend; extract the characteristics of sudden heart rate changes according to the inflection point of the waveform trend, and identify the characteristics of sudden heart rate changes to output the evaluation results of heart health risk symptoms. The specific operation steps are as follows:

[0044] S21: Set a search window for the electrocardiogram signal, and slide-search feature points on the electrocardiogram signal according to the search window to obtain the characteristic points of sudden heart rate changes;

[0045] It should be noted that according to the sampling frequency of the signal and the characteristics of the waveform, a reasonable search window is set; for example, the QRS complex in the electrocardiogram is usually between 100 milliseconds and 300 milliseconds, so the search window can be set within this range. The search window is affected by the heart rate. When the heart rate changes, the period and waveform characteristics will also change, so the window size needs to be adjusted in real time; the faster the heart rate, the shorter the window may be, and the slower the heart rate, the longer the window will be appropriately;

[0046] For the convenience of processing, the method of using a sliding search window is adopted to gradually slide-search the characteristic points of sudden heart rate changes along the electrocardiogram signal sequence;

[0047] S22: Calculate the first derivative of the waveform of the electrocardiogram signal (i.e., the rate of change of the waveform), to obtain the rate of change of the waveform of the electrocardiogram signal at each moment (i.e., the slope, where the slope is a mathematical concept used to describe the degree of inclination of a straight line or a curve; in the electrocardiogram signal, the slope indicates the speed of signal change and also reflects the steep change of the waveform rising and falling), and the calculation formula is: ;

[0048] In the formula, slope(i) is the rate of change between the i-th heart rate mutation feature point and the i + 1-th heart rate mutation feature point, and Δt is the time interval between two heart rate mutation feature points;

[0049] Calculate the second derivative of the electrocardiogram signal using the rate of change of the waveform at each moment of the electrocardiogram signal, to obtain the acceleration of the rate of change (i.e., the rate of change of the slope), and the calculation formula is: ;

[0050] It should be noted that expressed using the first derivative formula, if x(t) represents the potential change in the electrocardiogram, then it represents the change speed of the electrocardiogram signal, or the degree of inclination of the signal at that moment; the greater the slope, the faster the signal changes, and the smaller the change is slower; through the first derivative, regions where the signal changes relatively violently can be identified, such as the steep rise or fall of the waveform;

[0051] Next, calculate the second derivative of the electrocardiogram signal, expressed using the formula, which represents the change of the rate of change of the electrocardiogram signal, also called acceleration; the second derivative reflects the degree of change of the rate of change of the signal; if the first derivative represents the change speed of the electrocardiogram signal, then the second derivative represents the change of the change speed, that is, the acceleration of the electrocardiogram signal, which helps to reveal the inflection points of the heart rate mutation feature points of the electrocardiogram signal, especially in the transition region of the waveform;

[0052] When the acceleration of the electrocardiogram signal is relatively large, the rate of change of the electrocardiogram signal has changed significantly, usually the inflection point of the waveform; for example, in the electrocardiogram, the start and end positions of the QRS complex are often places where the acceleration changes significantly;

[0053] When the acceleration of the electrocardiogram signal is zero or close to zero, the rate of change of the electrocardiogram signal tends to be stable, usually the flat top or flat bottom part of the waveform;

[0054] S23: Preset an acceleration change threshold e, and determine whether the acceleration of the rate of change is greater than the acceleration change threshold e;

[0055] If so, determine the heart rate mutation feature point at the waveform position of the electrocardiogram signal as a fluctuation point;

[0056] Otherwise, it is determined that there are no fluctuation points in the waveform of the electrocardiogram signal;

[0057] It should be noted that when the acceleration of the change rate (i.e., the rate of change of the slope) is greater than the set threshold, it usually indicates that the signal has undergone a rapid change or mutation; for example, at the boundaries of the QRS complex, especially near the R wave, the electrical activity in the electrocardiogram changes rapidly, resulting in a large mutation rate of the change rate; at the position of the R wave, the depolarization of the ventricle is very rapid, causing a large change in potential, so the acceleration of the change rate is large;

[0058] When the rate of change of the slope of the waveform is less than the set threshold, this usually indicates that the rate of change of the waveform is relatively gentle and there are no obvious fluctuation points; this situation usually corresponds to the stable part or transition section of the waveform in the electrocardiogram, and does not involve areas with sharp changes such as the QRS complex (i.e., the starting and ending phases of the P wave represent atrial depolarization, and its potential change is relatively gentle, so the rate of change of the slope is relatively small; obvious mutations usually do not occur in the P wave; the transition section of the T wave represents ventricular repolarization. Although the T wave has certain changes, its rate of change is slower compared to the rapid changes of the QRS complex; therefore, the rate of change of the slope at the start and end of the T wave is usually small; the transition area of the waveform is between the QRS complex and the T wave, and the rate of change of the waveform may gradually slow down, and the rate of change of the slope is lower than the threshold, usually indicating that the waveform tends to be stable, representing the recovery or calm phase of the electrocardiogram);

[0059] S24: Traverse the waveform of the electrocardiogram signal to find the local maximum points and local minimum points among the heart rate mutation characteristic points of the waveform of the electrocardiogram signal;

[0060] Calculate the amplitude difference of the extreme points between the local maximum points and the local minimum points to obtain the extreme difference points;

[0061] Preset an extreme point threshold r, and determine whether the value of the extreme difference point is greater than the extreme point threshold r;

[0062] Otherwise, it is determined that there are no atypical waveforms in the waveform of the electrocardiogram signal;

[0063] If so, it is determined that there are pseudo-extreme points among the heart rate mutation characteristic points of the waveform of the electrocardiogram signal, and the heart rate mutation characteristic points of the pseudo-extreme points are removed;

[0064] Use the local maximum points to represent the peak values of the R wave and the T wave;

[0065] Use the local minimum points to represent the trough values of the Q wave and the S wave;

[0066] It should be noted that identifying local maxima and minima is crucial for determining the key feature points of the waveform, especially for the R wave, T wave, Q wave, and S wave. By traversing the electrocardiogram (ECG) signal, the points of local maxima can be found, usually at the peaks of the R wave and T wave. The R wave is the strongest peak, and the peak of the T wave is usually lower. The points of local minima in the ECG signal are found, where the Q wave and S wave are the first and second negative peaks in the ECG respectively. The Q wave is usually the earliest negative wave to appear, and the S wave usually follows the R wave (i.e., as Figure 3 shown, the normal duration of the QRS complex is 0.06 - 0.10 s, which is 1.5 to 2.5 small grids and will never exceed 3 small grids).

[0067] The amplitude difference between local extreme points can be calculated through local maximum points and local minimum points (such as the amplitude difference between the R wave and S wave). This difference helps to distinguish normal and abnormal (i.e., atypical waveform) waveform patterns. Some pseudo-extreme points may be encountered when detecting the extreme difference points. These pseudo points may be noise or other atypical waveforms, and these pseudo-extremes need to be removed.

[0068] S25: Preset the threshold position t for the rate of change, and determine whether the rate of change first exceeds the threshold position t.

[0069] If not, then determine that the current rate of change of the heart diagram signal is the fluctuation point where it last exceeded the threshold t, and use it as the end point of the waveform.

[0070] If so, then determine that the current rate of change of the heart diagram signal is the fluctuation point where it first exceeds the threshold position t, and use it as the starting point of the waveform.

[0071] It should be noted that in ECG signal processing, the rate of change of the waveform refers to the speed of the potential change of the ECG signal (i.e., the slope of the ECG signal). The QRS complex is the part with the largest rate of change in the ECG. Therefore, we can effectively distinguish the QRS complex (i.e., the components of the QRS complex are divided into the Q wave: the first downward deflection wave in the QRS complex; the R wave: the upward deflection wave in the QRS complex; the S wave: the downward deflection wave after the R wave; which form the QRS complex. The QRS complex is the part with the largest rate of change in the ECG and can also best reflect the normal or abnormal state of the ECG signal) from other relatively flat waveforms (such as the P wave and T wave) by setting a threshold for the rate of change. Selecting a suitable threshold for the rate of change (i.e., the threshold position t) helps to distinguish the start and end points of the QRS complex.

[0072] By determining whether the change rate of the electrocardiogram signal exceeds a preset threshold position t for the first time, the starting point of the QRS complex can be determined. The starting point of the QRS complex usually occurs where the signal potential rises sharply, that is, at the moment when the first slope exceeds the threshold. This position is usually the starting position of the R wave of the QRS complex, marking the beginning of ventricular depolarization;

[0073] If the electrocardiogram signal does not exceed the preset change rate threshold again, then we can regard the position where it last exceeded the threshold as the ending point of the QRS complex. This position usually appears after the S wave, marking the end of the ventricular depolarization process;

[0074] S26: Obtain the starting point time and ending point time of the QRS complex through the starting point and ending point of the waveform, and calculate through the starting point time and ending point time to obtain the duration of the QRS complex;

[0075] It should be noted that by determining the starting point and ending point of the QRS complex, the duration of the QRS complex can be calculated; the duration is an important indicator for evaluating the ventricular depolarization process and is very important for further analysis of the electrocardiogram (such as detecting cardiac rhythm problems); abnormal duration of the QRS complex may indicate certain problems in the cardiac conduction system, such as ventricular hypertrophy, conduction block, etc.;

[0076] S27: Mark the boundary points of the internal waveform segments through the starting point and ending point of the QRS complex;

[0077] Extract the waveform segments of all R waves (i.e., all R waves of the QRS complex in the electrocardiogram signal) of the electrocardiogram signal through the boundary points, calculate the time difference between every two adjacent R waves in the electrocardiogram signal to obtain the RR interval sequence (i.e., RR is every two adjacent R waves in the electrocardiogram signal, and the RR interval sequence is the time value of each RR interval extracted from the electrocardiogram signal);

[0078] Calculate the difference for each RR interval in the RR interval sequence to obtain the RR interval difference;

[0079] Calculate the standard deviation of the difference using the RR interval difference. The calculation formula is: ;

[0080] In the formula, is the mean value of the RR interval difference sequence; Denoted as the difference in RR intervals; N is the number of data points (i.e., the data points are the actual values extracted from the electrocardiogram signal, and each RR interval value (i.e., the time difference between two adjacent R waves) is a data point; if an electrocardiogram signal is monitored, after R wave detection, a series of RR intervals will be obtained, and these RR intervals are the data points; when calculating the difference in RR intervals, the difference calculation will be based on these data points; for example, if 10 RR intervals (R1 to R10) are extracted, then there are 10 data points).

[0081] It should be noted that by determining the starting point and ending point of the QRS complex, the boundary points of each waveform segment can be accurately marked (such as the starting and ending points of each R wave in the QRS complex), and these boundary points are the basis for electrocardiogram analysis and diagnosis, helping doctors judge the heart health status.

[0082] The R wave is usually the strongest wave in the electrocardiogram, representing ventricular depolarization and being a marker for detecting heartbeats; therefore, taking the R wave of the electrocardiogram signal as an example, early warning and identification of the heart are carried out. The RR interval refers to the time difference between two adjacent R waves. By extracting the time interval between every two adjacent R waves from the electrocardiogram signal (i.e., the value of each RR interval), an RR interval sequence is obtained, which provides data on the heartbeat cycle and is the basis for studying heart rhythm and heart rate fluctuations. Using RR(i) to represent the time difference between the i-th two adjacent R waves in the RR interval sequence, and using RR(i + 1) to represent the time difference between two adjacent R waves adjacent to the time difference of RR(i) (i.e., RR(i) and RR(i + 1) are the time differences between two adjacent RR intervals), through the formula Denotes the calculation of the difference in RR intervals (i.e., the difference in RR intervals is ). The difference reflects the fluctuation of the heartbeat cycle change; when the heart rate is stable, the difference in RR intervals is smaller; while when the heart rate changes greatly, the difference in RR intervals is larger (i.e., it may indicate symptoms of arrhythmia).

[0083] Calculating the standard deviation of RR interval differences (SDRR) is a statistical measure of the RR interval differences, indicating the degree of fluctuation of the RR interval differences, which can identify abnormal heartbeats, arrhythmias, etc., and reflect heart health.

[0084] S28: Calculate the average value of the previous and next n data points for each RR interval in the RR interval sequence.

[0085] Calculate the change rate (i.e., slope) of the RR interval sequence through the average value and the standard deviation of differences.

[0086] Obtain the periodic gradual change characteristics of the RR interval sequence through the change rate of the RR interval sequence.

[0087] Record the waveform trend inflection points of the RR interval sequence due to the change rate according to the periodic gradual change characteristics;

[0088] It should be noted that the n-point moving average is a method of smoothing signals. Through the processing of the n-point moving average, the interference caused by instantaneous fluctuations or noise in the electrocardiogram can be effectively removed, making the change trend of the heart rate on a long time scale more obvious; it can help identify the changes in the heart rate over a long period of time, such as the slowdown of the heart rate during rest or the acceleration of the heart rate during exercise;

[0089] Further extract the periodic gradual change characteristics of the RR interval sequence; for example, a constant negative slope indicates a gradual slowdown of the heartbeat, and a constant positive slope indicates a gradual acceleration of the heartbeat; the periodic gradual change characteristics reveal the gradual change pattern of the heart rate over time; under certain physiological or psychological states, the heartbeat may gradually accelerate or gradually slow down; for example, the heartbeat gradually accelerates during exercise and gradually slows down during rest; by extracting these gradual change characteristics, different physiological or emotional states (such as exercise, stress, relaxation, etc.) can be identified;

[0090] Calculating the slope for each time point through the average value and the standard deviation of the difference provides information on the change rate of the heart rate (i.e., the change rate); the change in the heart rate is usually not sudden but gradual; by calculating the slope at each moment, the process of the acceleration or deceleration of the heart rate can be quantified; for example, during exercise, the heartbeat gradually accelerates and the slope value will gradually increase; during rest, the heartbeat gradually slows down and the slope value gradually decreases; such quantitative analysis helps to understand the physiological process more precisely;

[0091] The periodic gradual change characteristics refer to the pattern in which the cardiac rhythm or heart rate changes gradually over time, usually manifested as a gradual increase or decrease in the inter-beat interval (i.e., the RR interval), a gradual slowdown or acceleration of the heart rate, and the gradual accumulation of changes in the QRS complex; this gradual change may be caused by the gradual change of the cardiac physiological state, autoregulation, or external stimuli, etc.; such as the acceleration during exercise and the deceleration during rest, the heart rate gradually changes from a normal frequency to a slower or faster rhythm, and this change may occur over a long period of time, reflecting the changes in the heart during the physiological adaptation process;

[0092] Calculate the change in the slope through the continuous change trend of the periodic gradual change characteristics to identify the inflection points of the heart rate change (such as the turning point from acceleration to deceleration, that is, the peak or valley point in the waveform segment of the R wave); the acceleration and deceleration of the heart rate are usually not constant but have certain inflection points; through the change in the slope, these turning points can be accurately captured, which is very important for monitoring the changes in the individual's physiological state;

[0093] S29: The RR interval sequence screens out abnormal cycle sequences according to the periodic gradual change characteristics, marks the abnormal fluctuations of the abnormal cycle sequences through the waveform trend inflection points, and obtains the heart rate mutation characteristics; identifies the heart rate mutation characteristics and the periodic gradual change characteristics to output the evaluation result of the heart;

[0094] It should be noted that the heart rate mutation characteristics refer to the obvious and rapid mutation of the heart rate in a short period of time, usually manifested as a sudden increase or decrease in heart rate, and the change is relatively large; this kind of mutation may be caused by heart disease, sinoatrial node arrhythmia, conduction system abnormality or certain external stimuli; the process includes sudden acceleration of heart rate (such as the occurrence of tachycardia or atrial fibrillation), sudden deceleration of heart rate (such as bradycardia or sinus arrest), and sudden occurrence of arrhythmia (such as premature beats, ventricular fibrillation, etc.); for example, in an electrocardiogram, a sudden increase or decrease in heart rate may be related to acute diseases or abnormal heart function, and the amplitude of heart rate change is relatively large and usually short in time;

[0095] The periodic gradual change characteristics usually show gradual and continuous changes, the change amplitude of the heart rate is small, and it may last for a long time, usually related to normal physiological adaptation or chronic problems; the heart rate mutation characteristics show sudden and rapid changes, the change amplitude of the heart rate is large, usually caused by pathological reasons (such as arrhythmia, acute cardiac events), and usually occurs in a short period of time; therefore, although both the periodic gradual change characteristics and the heart rate mutation characteristics involve changes in heart rate, their time scales, change patterns and physiological mechanisms are different;

[0096] Specifically, as Figure 4 shown, in step S29, the RR interval sequence screens out abnormal cycle sequences according to the periodic gradual change characteristics, marks the abnormal fluctuations of the abnormal cycle sequences through the waveform trend inflection points, and obtains the heart rate mutation characteristics; identifies the heart rate mutation characteristics and the periodic gradual change characteristics to output the evaluation result of the heart, and the specific operation steps are as follows:

[0097] S291: Preset the normal cycle range u according to the periodic gradual change characteristics (that is, preset the normal cycle range u according to the characteristics of identifying the physiological state of an individual by the periodic gradual change characteristics (that is, the characteristics of being able to identify the acceleration of heart rate during exercise and the deceleration of heart rate during rest)), and judge whether the RR interval in the RR interval sequence is within the normal cycle range u (that is, when the RR interval is within the preset normal cycle range u, and the RR interval is within the normal cycle range u, it means that the heart beat is normal);

[0098] If the RR interval is equal to the normal cycle range u, it is determined that the RR interval is normal and no abnormality occurs;

[0099] If the RR interval is greater than the normal cycle range u, it is determined that the RR interval is too long and is used as an overlong cycle;

[0100] If the RR interval is less than the normal cycle range u, it is determined that the RR interval is too short and regarded as a short cycle;

[0101] Establish a set of the long cycles and short cycles as the abnormal cycle sequence;

[0102] It should be noted that according to the characteristic of recognizing the physiological state of an individual based on the cycle gradual change feature (i.e., the feature of being able to recognize the acceleration of the heart rate during exercise and the deceleration of the heart rate during rest), the normal cycle range u is preset. By using the normal cycle range u, it is judged whether there is an abnormal heart rate range in the RR intervals of the RR interval sequence (i.e., here it is explained that the RR interval has been described in the above step S26 as obtained from the time difference between two adjacent R waves in the electrocardiogram signal. Therefore, the RR interval is a time unit, representing the heart rate condition of the R wave in the RR interval (i.e., the speed of the heartbeat); while the preset normal cycle range u represents the heart rate condition of the normal human R wave within a certain time, that is, within the time of the RR interval. Therefore, it is possible to judge whether the heart rate of the RR interval is normal through the normal cycle range u);

[0103] If the RR interval is within the normal cycle range u, it indicates an abnormal heartbeat cycle, which may be problems such as arrhythmia; if the RR interval is less than the normal cycle range u, it indicates a too fast heartbeat (short cycle); if the RR interval is greater than the normal cycle range u, it indicates a too slow heartbeat (long cycle);

[0104] S292: Count the quantity of the abnormal cycle sequence and calculate the proportion of the abnormal cycle sequence in the RR interval sequence;

[0105] If the proportion of the abnormal cycle sequence is greater than half of the RR interval sequence, preset the cycle mutation threshold o of the RR interval difference;

[0106] Judge whether the RR interval difference (i.e., here the RR interval difference is the RR interval difference between the possibly abnormal long cycle and short cycle, the difference of the abnormal cycles in the abnormal cycle sequence, and does not belong to the RR interval difference of the normal heart rate) is greater than the cycle mutation threshold o;

[0107] If not, it is determined that there is no abnormal heart problem in the RR intervals of this abnormal cycle sequence;

[0108] If so, it is determined that there is a cycle mutation in the RR intervals of this abnormal cycle sequence;

[0109] It should be noted that counting the quantity of the RR intervals in the abnormal cycle sequence and then judging whether the quantity of the RR intervals in the abnormal cycle sequence accounts for half or more of the total RR interval sequence helps to judge the overall stability of the electrocardiogram signal; if so, it may indicate a problem with heart health;

[0110] The abnormal fluctuation of the RR interval difference is one of the early signals for detecting arrhythmia.

[0111] Arrhythmias such as atrial fibrillation and ventricular premature beats often manifest as abnormal changes in the RR interval, and these changes can be identified by calculating the RR interval difference; therefore, by presetting the periodic mutation threshold o of the RR interval difference and judging whether the RR interval difference is greater than the periodic mutation threshold o, potential arrhythmia problems of the heart rate can be judged;

[0112] S293: Mark the waveform trend inflection point of the periodically mutated RR interval as the periodic mutation point;

[0113] Calculate the mutation amplitude between two adjacent RR intervals through each marked periodic mutation point, and obtain the mutation magnitude through the mutation amplitude;

[0114] Obtain the heart rate mutation characteristics of the QRS complex through all the mutation magnitudes;

[0115] It should be noted that the periodic mutation point is marked as the inflection point of the RR interval waveform trend; the RR interval is the time interval between two heartbeats, and the mutation point reflects the sharp change in heart rate, such as the occurrence of arrhythmia; by identifying these inflection points, potential abnormal fluctuations can be warned;

[0116] Between each periodic mutation point, calculate the mutation amplitude between two adjacent RR intervals; the mutation magnitude reflects the drastic change in the heartbeat cycle, usually corresponding to the abnormal fluctuation in the electrocardiogram; this helps to detect and quantify the drastic fluctuation of the heart rate, which is very important especially when judging whether there is arrhythmia;

[0117] The QRS complex represents the cardiac contraction process and is the key clue that can best reflect the abnormal cardiac activity, and the R wave can best reflect the change process of the QRS complex on the heart rate; therefore, all the mutation magnitudes constitute the heart rate mutation characteristics of the QRS complex;

[0118] The heart rate mutation characteristics, as the basis for reflecting the sharp change in heart rate, are the main use for predicting abnormal fluctuations in electrocardiogram signals;

[0119] S294: Extract the mutation parameters and gradual change parameters for the heart rate mutation characteristics and the periodic gradual change characteristics respectively; use the mutation parameters and the gradual change parameters to comprehensively evaluate the cardiac warning; the mutation parameters include: the mutation occurrence frequency; the gradual change parameters include: the gradual change frequency;

[0120] It should be noted that the periodic gradual change feature usually shows a gradual and continuous change, with a small change range of the heart rate, which may last for a long time and is usually related to normal physiological adaptation or chronic problems; while the heart rate mutation feature shows a sudden and sharp change, with a large change range of the heart rate, which is usually caused by pathological reasons (such as arrhythmia, acute cardiac events) and usually occurs within a short period of time;

[0121] Therefore, by extracting the gradual change parameters and mutation parameters through two different features, the periodic gradual change feature may be a precursor to heart mutations. Especially in chronic heart problems, the gradual change state of the heart may ultimately lead to the occurrence of heart rate mutations. Therefore, by monitoring the change of the periodic gradual change feature, the potential heart rate mutation risk can be identified in advance;

[0122] The mutation parameters are mainly used to detect acute problems and sudden risks of the heart, such as arrhythmia, acute cardiac events, etc.; the gradual change parameters help to reveal chronic heart diseases or potential physiological adaptation problems, such as heart failure, gradual decline of heart function, etc.;

[0123] Specifically, as Figure 5 shown, in step S294, the mutation parameters and the gradual change parameters are respectively extracted from the heart rate mutation feature and the periodic gradual change feature; the mutation parameters and the gradual change parameters are used to comprehensively evaluate the heart warning, and the specific operation steps are as follows:

[0124] S2941: Analyze the periodic mutation points of the heart rate mutation feature through the duration of the QRS complex, and take the duration of the QRS complex as the mutation duration of the heart rate mutation feature;

[0125] The above periodic mutation points are marked as the inflection points of the RR interval waveform trend; the RR interval is the time interval between two heartbeats, and the periodic mutation points reflect the sharp change of the heart rate. At the same time, the duration of the QRS complex has been obtained in step S27. When the duration of the QRS complex changes significantly, it can be considered that there is a sudden change in cardiac conduction, which may lead to a mutation in the RR interval, and the mutation duration is the change duration of the QRS complex duration. If the duration of the QRS complex increases or decreases and continuously changes for a certain period of time, this period of change time can be used as the mutation duration (TD) of the heart rate mutation feature;

[0126] S2942: Obtain the mutation occurrence frequency for the frequencies of the too-long periods and too-short periods of the abnormal periodic sequence in the mutation duration;

[0127] As explained in step S291, the abnormal cycle sequence is an abnormal mutation that appears in the RR interval sequence. By calculating the time intervals (i.e., RR intervals, which are the time intervals between two heartbeats) of the RR intervals of the too-long cycles and too-short cycles during the duration of the QRS complex (i.e., the mutation duration), the frequency of the occurrence of cycle mutation points is recorded, thereby obtaining the mutation occurrence frequency;

[0128] S2943: Extract the gradient parameters for the cycle gradient feature to obtain the gradient rate;

[0129] As described in step S28, the cycle gradient feature is obtained by calculating the slope of the average value and the standard deviation of the differences of the RR intervals in the RR interval sequence, which provides the rate information of the heart rate change (i.e., the change rate) for each time point. Therefore, the change rate of the RR interval can be directly used as the gradient rate of the cycle gradient feature;

[0130] S2944: Determine whether the gradient rate of the cycle gradient feature is too long (i.e., whether the acceleration of the change rate increases or decreases, that is, whether the time interval of the RR interval is too long, reflecting that the gradient rate and the change trend can reveal potential chronic heart diseases (such as heart failure, myocardial ischemia, etc.));

[0131] If not, it is determined that the cardiac monitoring data collected from the target patient is normal;

[0132] If so, further determine whether a mutation occurs when monitoring the mutation occurrence frequency in the state where the gradient rate is too long (i.e., in the context of a gradually changing heart rate, if a sudden mutation occurs, it may mean the occurrence of a transformation from a chronic problem to an acute problem; for example, when the gradual change of the heart rate becomes faster and the amplitude increases, the sudden rapid fluctuation of the heart rate (mutation) may indicate the occurrence of a serious arrhythmia);

[0133] If not, it is determined that the cardiac monitoring data collected from the target patient is abnormal, and the target patient has a chronic heart disease (i.e., first, the target patient checks whether there is a chronic heart disease with a stable acceleration of the gradient rate (i.e., the change rate) through the cycle gradient feature. If a chronic heart disease appears, at this time, it is necessary to check whether there is a situation where the gradual change of the heart rate may suddenly become faster and faster (i.e., the acceleration of the change rate continues to increase). Therefore, it is necessary to monitor the obtained fixed mutation occurrence frequency to see whether a mutation phenomenon occurs at a certain frequency, so as to convert the chronic heart disease into an acute heart disease, thereby prompting the medical staff after the subsequent warning is issued to prevent the aggravation of the target patient's condition and timely treatment);

[0134] If so, it is determined that the cardiac monitoring data collected from the target patient is abnormal, and the target patient has an acute heart disease;

[0135] It should be noted that by judging whether the gradual change rate of the periodic gradual change feature is too long, it is possible to identify abnormal heart rate change rates, and these changes may be related to potential chronic heart diseases (such as heart failure, myocardial ischemia, etc.); chronic heart diseases, such as heart failure, myocardial ischemia, etc., are usually accompanied by changes in the gradual change rate, especially changes in heart rate or RR interval; if the acceleration of this change (i.e., the change in the gradual change rate) increases or accelerates, it means that the condition may be deteriorating; by monitoring these changes, potential acute events can be detected early, and patients can be prevented from entering the acute attack state without timely treatment.

[0136] By monitoring the frequency of heart rate mutations, it is possible to detect severe fluctuations in the heart rhythm in a timely manner; mutant fluctuations often indicate the occurrence of severe arrhythmias. If detected and treated in a timely manner, the risk of acute heart disease can be greatly reduced, and the patient's condition can be prevented from worsening through rapid intervention; when the gradual change rate is abnormal, by monitoring the frequency of heart rate mutations, the transition from chronic heart problems to acute problems can be detected in a timely manner; for example, in the case of an accelerating gradual change rate or an increasing acceleration, if there is a sudden severe fluctuation in heart rate, this may be a sign of arrhythmia or other acute conditions.

[0137] Specifically, as Figure 6 shown, in step S294, comprehensive evaluation of heart warning using mutation parameters and gradual change parameters also includes using the QT interval to identify heart warning for long QT syndrome. The specific operation steps are as follows:

[0138] S2941’: Obtain the RR interval time series of the electrocardiogram signal by calculating the RR intervals between all R waves in the QRS complex.

[0139] It should be noted that the RR interval is the time interval between two adjacent R waves, and the R wave is the most prominent part of the QRS complex, representing the main part of ventricular depolarization.

[0140] Calculate the intervals between multiple R waves to form a time series, which is called the RR interval time series; this can be regarded as a periodic time series that describes the fluctuations of the heart rate (or cardiac cycle) within a certain period of time; the RR interval time series can not only reflect the normal heart rhythm but also capture abnormal situations, especially in the diagnosis of diseases such as arrhythmia and long QT syndrome, it has a good recognition effect.

[0141] S2942’: Take the starting point of the QRS complex as the starting point of the Q wave.

[0142] Take the ending point of the QRS complex as the starting point of the T wave.

[0143] It should be noted that the starting point and ending point of the QRS complex have been obtained in step S26. The QRS complex includes: Q wave, R wave, and S wave. The Q wave is the first wave band in the QRS complex. Therefore, the starting point of the QRS complex is also the starting point of the Q wave;

[0144] The T wave represents the process of ventricular repolarization. It is a waveform immediately following the QRS complex in the electrocardiogram signal and is usually a positive wave. Since the T wave appears immediately after the QRS complex, the ending point of the QRS complex can be used as the starting point of the T wave;

[0145] S2943’: Collect a waveform segment after the QRS complex in the electrocardiogram signal as the initially obtained T wave;

[0146] Identify the stationary state of the baseline of the waveform segment of the initially obtained T wave after the QRS complex in the electrocardiogram signal; preset the baseline stability threshold p of the electrocardiogram signal;

[0147] Judge whether the baseline of the waveform segment of the T wave is less than the baseline stability threshold p;

[0148] If not, it is determined that the baseline of the waveform segment of the T wave is unstable, and the ending point of the T wave cannot be determined from the waveform segment of the T wave;

[0149] If so, it is determined that the baseline of the waveform segment of the T wave is stable, and the waveform segment of the initially obtained T wave is obtained;

[0150] Divide the waveform segment of the initially obtained T wave into several small segments [t1, t2], [t2, t3], … [ti, ti+1], calculate the difference of each small segment, and obtain the difference value of each small segment. The calculation formula is:

[0151] ;

[0152] In the formula, represents the waveform segment of the initially obtained T wave after the QRS complex of the electrocardiogram signal;

[0153] Judge whether the difference values of each small segment are consistent;

[0154] If not, it is determined that the difference values of each small segment are inconsistent, the baseline of the waveform segment of the initially obtained T wave fluctuates greatly, and the waveform segment after the QRS complex in the electrocardiogram signal is re-collected as the T wave (that is, when re-collecting the waveform segment as the T wave, the distance of the waveform segment is increased on the basis of the previously collected waveform segment of the T wave to obtain the newly collected waveform segment of the T wave);

[0155] If so, it is determined that the difference values of each small segment are consistent, it is determined that the baseline of the waveform segment of the T wave is stable, and the finally confirmed T wave is obtained;

[0156] It should be noted that a waveform segment after the QRS complex in the collected electrocardiogram (ECG) signal is used as the initially obtained T wave, and it is identified whether the baseline of the waveform segment of the initially obtained T wave is a straight line. First, through the preset baseline stability threshold p of the ECG signal, it is initially judged whether the baseline of the waveform segment of the T wave is stable. If the baseline of the T wave is a straight line, the signal change in this area should be stable. Therefore, it is judged whether the baseline of the waveform segment of the T wave is stable through the preset baseline stability threshold p of the ECG signal. If the baseline of the waveform segment of the T wave is in a stable state, then it is further verified whether the baseline of the waveform segment of the T wave is a straight line, and the difference of the signal is used to analyze the regularity of the baseline change. By dividing the waveform segment into multiple small segments and calculating the difference of each segment of the waveform (i.e., the change amount between adjacent points), the trend of the baseline change can be obtained. If the difference of each small segment is small and consistent, it indicates that the baseline of the waveform segment is relatively stable and the baseline is a straight line, and thus the T wave is obtained.

[0157] S2944’: Determine the end point of the T wave through the waveform segment of the T wave;

[0158] Calculate the QT interval based on the starting point of the Q wave and the end point of the T wave;

[0159] Obtain the QT interval time series of the ECG signal based on the QT interval;

[0160] It should be noted that this time series reflects the time changes of ventricular depolarization and repolarization in each cardiac cycle; by analyzing the QT interval time series, information such as heart rate changes, QT interval changes, and possible cardiac abnormalities (such as QT interval prolongation or shortening) can be obtained; as Figure 7 shown, the position of the QT interval in the ECG signal, and the relationship positions of the QT interval with the starting point of the QRS complex and the end point of the T wave.

[0161] S2945’: Perform gradual change recognition on the QT interval time series through the periodic gradual change characteristics, and calculate the change rate of the QT interval. The calculation formula is: ;

[0162] In the formula, is the sum of the weighting coefficients (i.e., usually the sum of the coefficients used when calculating the weighted average; the role of this part is to weight the change amount (for example, using exponential decay or other weighting functions) so that the data closer to the current moment has a greater impact on the change rate);

[0163] wi is a weighting factor (i.e., this is a weighting coefficient. For different time points i, the weighting coefficient wi is set according to the selected weighting function (e.g., exponential decay); generally, the most recent time point i = 0 has the largest weight, and as time goes by, the weight gradually decreases; this weight is usually: , where α is the decay factor, which determines the decay rate of the weight; in order to effectively capture the periodicity of the QT interval (i.e., when the heart rate increases, the QT interval shortens; when the heart rate slows down, the QT interval lengthens, forming a periodic fluctuation) and the characteristics of periodic gradual change, the decay factor (such as exponential decay) can play an important role in weighted averaging or weighted analysis; the decay factor controls the weights of different time points, making the closer time points (usually more relevant to the current moment) have a greater weight in the weighted calculation, while the contribution of the farther time points (with less influence) to the calculation result gradually decreases; specifically, in the periodic fluctuation of the QT interval, the data at the current moment usually best reflects the immediate periodic state; for example, when the heart rate increases, the QT interval will briefly shorten, and this change has the greatest impact on the current heart state; therefore, in the periodic change, the nearest few data points will have the strongest impact on the current trend).

[0164] represents the value of the QT interval at time t - i;

[0165] represents the value of the QT interval at time t - i - 1 (i.e., this term is used to calculate the change in the QT interval between adjacent time points, usually used to measure the change in the QT interval between consecutive moments);

[0166] represents the difference between the QT interval at the current moment and the QT interval at the previous moment (i.e., represents the change in the QT interval);

[0167] It should be noted that long QT syndrome is usually not a sudden event, but a gradually changing process. The QT interval may show periodic changes. Therefore, the periodic characteristics of periodic gradual change are used to identify the gradual change of the QT interval time series (i.e., the gradual change identification represents the identification of the gradually changing process of the QT interval and the calculation of the periodic change of the change rate of the QT interval);

[0168] S2846’: Set the time joint window by the change rate of the RR interval and the change rate of the QT interval;

[0169] Simultaneously detect the heart rate mutation of the heart rate mutation characteristics of the QT interval time series and the RR interval time series within the time joint window;

[0170] If the heart rate suddenly increases within the time joint window, it is determined that the QT interval has a periodic prolongation, and a warning signal for QT interval prolongation syndrome is issued;

[0171] If the heart rate does not change within the time joint window, it is determined that the QT interval is normal;

[0172] It should be noted that the sudden change in heart rate may affect the stability of the QT interval, resulting in the prolongation or irregular fluctuation of the QT interval; therefore, the sudden change in heart rate may be closely related to the occurrence of QT interval prolongation syndrome;

[0173] Use Joint Window Analysis to synchronously analyze the time series of the QT interval and the RR interval; by setting a time joint window, simultaneously monitor the heart rate changes in the time series of the QT interval and the RR interval (that is, monitor whether there is a sudden change in heart rate). If the heart rate suddenly increases within a certain time joint window, there is a tendency for the QT interval to have a periodic prolongation, which may be a warning signal for QT interval prolongation syndrome;

[0174] Embodiment 2

[0175] Such as Figure 8 shown, the present invention also provides a patient health warning processing system for multiple cardiac monitoring data, including: a collection module 10; an analysis module 20; a warning module 30;

[0176] The collection module 10 is used to collect cardiac monitoring data for the target patient, preprocess the sampling frequency and waveform of the cardiac monitoring data, and obtain an electrocardiogram signal;

[0177] The analysis module 20 is used to confirm the starting point and the ending point of the waveform of the electrocardiogram signal, use the starting point and the ending point of the waveform to find the inflection point of the waveform trend; extract the heart rate mutation characteristics according to the inflection point of the waveform trend, and identify the heart rate mutation characteristics to output the evaluation result of the cardiac health risk symptoms;

[0178] The warning module 30 is used to issue a warning according to the evaluation result of the cardiac health risk symptoms.

[0179] In summary, it can be seen from the patient health warning processing method and system for multiple cardiac monitoring data proposed in the embodiments of the present invention that by preprocessing the sampling frequency and waveform of the cardiac monitoring data, noise can be removed to obtain a stable and clear electrocardiogram signal;

[0180] Further, a search window is set for the electrocardiogram signal to slide and search for characteristic points to obtain heart rate mutation characteristic points; the first derivative of the waveform of the electrocardiogram signal is calculated to obtain the change rate that can be recognized, and the second derivative is calculated through the change rate to obtain the acceleration, which helps to reveal the inflection point of the heart rate mutation characteristic point of the electrocardiogram signal and know the change trend of the electrocardiogram signal; a preset acceleration change threshold e is set, and it is judged whether the acceleration of the change rate is greater than the acceleration change threshold e, so as to determine whether there are fluctuation points in the electrocardiogram signal, and then the stable part or transition section of the waveform in the electrocardiogram is extracted; the waveform of the electrocardiogram signal is traversed to find the local maximum points and local minimum points. The local maximum points represent the peak values of the R wave and T wave, and the local minimum points represent the trough values of the Q wave and S wave, so as to obtain the starting point and ending point of the waveform, which are used as the QRS complex to represent the recovery or calm stage of the electrocardiogram.

[0181] The R wave of the QRS complex is extracted to calculate the RR interval, which provides data on the heartbeat cycle and is the basis for studying cardiac rhythm and heart rate fluctuations; calculating the difference between RR intervals can reflect problems such as abnormal heartbeats and arrhythmias; calculating the average value between RR intervals, and calculating the change rate of the RR interval through the average value and the difference value, so as to obtain the periodic gradual change characteristics. By extracting these gradual change characteristics, different physiological or emotional states (such as exercise, stress, relaxation, etc.) can be identified; using the periodic gradual change characteristics to screen abnormal cycle sequences, and marking the abnormal fluctuations of the abnormal cycle sequences through the waveform trend inflection points to obtain heart rate mutation characteristics, and judging whether the target patient has acute heart health problems; identifying the heart results based on the heart rate mutation characteristics and periodic gradual change characteristics, and judging the acute and short-term heart diseases of the target patient, so as to issue an early warning.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; those of ordinary skill in the art can modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A patient health warning processing method for multiple heart monitoring data, characterized in that: The steps are as follows: Collecting cardiac monitoring data from a target patient, performing sampling frequency and waveform preprocessing on the cardiac monitoring data, and obtaining an electrocardiogram signal; Confirming the starting point and the ending point of the waveform of the electrocardiogram signal, and using the starting point and the ending point of the waveform to find the inflection point of the waveform trend; Extracting heart rate mutation features according to the inflection points of the waveform trend, identifying the heart rate mutation features and outputting an assessment result of heart health risk symptoms; Provide early warning based on the assessment results of heart health risk symptoms; The starting point and the ending point of the waveform of the electrocardiogram signal are confirmed, and the specific operation steps are as follows: Setting a search window for the electrocardiogram signal, and slidingly searching for characteristic points of the electrocardiogram signal according to the search window to obtain characteristic points of sudden heart rate changes; The first-order derivative of the electrocardiogram signal waveform is calculated to obtain the rate of change of the electrocardiogram signal waveform at each moment. The calculation formula is: ; In the formula, It is The heart rate mutation feature point and The rate of change between the heart rate mutation feature points, is the time interval between two characteristic points of heart rate mutation; The second-order derivative of the electrocardiogram signal is calculated using the rate of change of the waveform of the electrocardiogram signal at each moment to obtain the acceleration of the rate of change. The calculation formula is: ; Preset an acceleration change threshold e, and determine whether the acceleration of the change rate is greater than the acceleration change threshold e; If so, the heart rate mutation characteristic point at the waveform position of the electrocardiogram signal is determined as a fluctuation point; If not, it is determined that there is no fluctuation point in the waveform of the electrocardiogram signal; Traversing the waveform of the electrocardiogram signal to find the local maximum value and the local minimum value in the heart rate mutation feature points of the waveform of the electrocardiogram signal; Calculate the extreme value point amplitude difference between the local maximum point and the local minimum point to obtain the extreme value difference point; Preset an extreme point threshold r, and determine whether the value of the extreme difference point is greater than the extreme point threshold r; If not, it is determined that the waveform of the electrocardiogram signal does not have an atypical waveform; If so, it is determined that there is a pseudo extreme point in the heart rate mutation feature point of the waveform of the electrocardiogram signal, and the heart rate mutation feature point of the pseudo extreme point is removed; The local maximum point is used to represent the peak values ​​of the R wave and the T wave; The local minimum point is used to represent the trough values ​​of the Q wave and the S wave; Preset a threshold position t for the change rate, and determine whether the change rate exceeds the threshold position t for the first time; If not, the current rate of change of the cardiogram signal is determined to be the fluctuation point at which the last time the rate of change exceeded the threshold value t, which is taken as the end point of the waveform; If so, the current rate of change of the cardiogram signal is determined to be the fluctuation point that first exceeds the threshold position t, which is taken as the starting point of the waveform.

2. According to claim 1, a patient health warning processing method for multiple heart monitoring data is characterized in that: Use the starting point and the ending point of the waveform to find the inflection point of the waveform trend. The specific steps are as follows: The starting point time and the ending point time of the QRS complex are obtained by the starting point and the ending point of the waveform, and the duration of the QRS complex is obtained by calculation by the starting point time and the ending point time; Marking boundary points of internal waveform segments by the starting point and the ending point of the QRS complex; Extracting waveform segments of all R waves of the electrocardiogram signal through the boundary points, calculating the time difference between every two adjacent R waves in the electrocardiogram signal, and obtaining an RR interval sequence; Calculating the difference of each RR interval in the RR interval sequence to obtain the RR interval difference; The standard deviation of the difference is calculated using the RR period difference, and the calculation formula is: ； In the formula, is the mean of the RR interval difference series; It is expressed as the RR interval difference; N is the number of data points; Calculate the average value of n data points before and after each RR interval in the RR interval sequence; The rate of change of the RR interval series is calculated by the mean value and the standard deviation of the difference; Obtaining the periodic gradual change characteristics of the RR interval sequence through the change rate of the RR interval sequence; The waveform trend inflection point of the RR interval sequence due to the change rate is recorded according to the periodic gradual change characteristics.

3. A patient health warning processing method for multiple heart monitoring data according to claim 2, characterized in that: Extracting the heart rate mutation feature according to the inflection point of the waveform trend, identifying the heart rate mutation feature and outputting the evaluation result of the heart health risk symptom, the specific operation steps are as follows: Calling the RR interval sequence to filter out abnormal cycle sequences according to the cycle gradual change characteristics, marking abnormal fluctuations of the abnormal cycle sequences through the inflection points of the waveform trend, and obtaining the heart rate mutation characteristics; The heart rate sudden change feature and the periodic gradual change feature are identified and the heart evaluation result is output.

4. The patient health warning processing method of multiple heart monitoring data according to claim 3 is characterized in that: Call the RR interval sequence to filter out abnormal periodic sequences based on the periodic gradual change characteristics. The specific steps are as follows: Preset a normal cycle range u according to the cycle gradual change feature, and determine whether the RR interval in the RR interval sequence is within the normal cycle range u; If the RR interval is equal to the normal cycle range u, it is determined that the RR interval is normal and no abnormality occurs; If the RR interval is greater than the normal cycle range u, the RR interval is determined to be too long and is regarded as an excessively long cycle; If the RR interval is less than the normal cycle range u, the RR interval is determined to be too short and is regarded as an excessively short cycle; The excessively long period and the excessively short period are combined to form a set as an abnormal period sequence.

5. A patient health warning processing method for multiple heart monitoring data according to claim 4, characterized in that: The abnormal fluctuation of the abnormal periodic sequence is marked by the inflection point of the waveform trend to obtain the heart rate mutation characteristics. The specific operation steps are as follows: Counting the number of the abnormal cycle sequences and calculating the proportion of the abnormal cycle sequences to the RR interval sequences; If the proportion of abnormal cycle sequences is greater than half of the RR interval sequences, a cycle mutation threshold o of the RR interval difference is preset; Determine whether the RR interval difference is greater than a cycle mutation threshold o; If not, it is determined that there is no abnormal heart problem in the RR intervals in the abnormal cycle sequence; If so, it is determined that there is a cycle mutation in the RR interval of the abnormal cycle sequence; The inflection point of the waveform trend of the RR interval of the cycle mutation is marked as a cycle mutation point; The mutation amplitude of two adjacent RR intervals is calculated by marking each cycle mutation point, and the mutation amplitude is obtained by the mutation amplitude; The heart rate mutation characteristics of the QRS complex are obtained through all mutation amplitudes.

6. A patient health warning processing method for multiple heart monitoring data according to claim 5, characterized in that: The heart rate sudden change feature and the periodic gradual change feature are identified and the heart evaluation result is outputted. The specific operation steps are as follows: Extracting mutation parameters and gradual change parameters from the heart rate mutation feature and the period gradual change feature respectively; Comprehensively evaluate cardiac warning using the mutation parameters and gradual change parameters; The mutation parameters include: mutation frequency; The gradient parameters include: gradient frequency.

7. A patient health warning processing method for multiple heart monitoring data according to claim 6, characterized in that: The mutation parameters and the gradual change parameters are extracted from the heart rate mutation characteristics and the period gradual change characteristics respectively; the mutation parameters and the gradual change parameters are used to comprehensively evaluate the heart warning. The specific operation steps are as follows: Analyze the period mutation point of the heart rate mutation characteristic through the duration of the QRS complex, and use the duration of the QRS complex as the mutation duration of the heart rate mutation characteristic; Obtain the frequency of occurrence of mutations by calculating the frequency of occurrence of excessively long periods and excessively short periods of the abnormal period sequence in the duration of the mutation; Extracting gradient parameters from the periodic gradient feature to obtain a gradient rate; Determining whether the gradual change rate of the periodic gradual change feature is too long; If not, it is determined that the cardiac monitoring data collected from the target patient is normal; If so, it is further determined whether a mutation occurs by monitoring the frequency of mutation when the gradual rate is too long; If not, it is determined that the heart monitoring data collected by the target patient is abnormal, and the target patient has chronic heart disease; If so, it is determined that the cardiac monitoring data collected from the target patient is abnormal and the target patient has an acute heart disease.

8. A patient health warning processing system for multiple heart monitoring data, used to implement a patient health warning processing method for multiple heart monitoring data according to any one of claims 1 to 7, characterized in that: The system includes: a collection module; an analysis module; an early warning module; The acquisition module is used to collect cardiac monitoring data from the target patient, perform sampling frequency and waveform preprocessing on the cardiac monitoring data, and obtain an electrocardiogram signal; The analysis module is used to confirm the starting point and the ending point of the waveform of the electrocardiogram signal, and use the starting point and the ending point of the waveform to find the inflection point of the waveform trend; extract the heart rate mutation feature according to the inflection point of the waveform trend, identify the heart rate mutation feature and output the evaluation result of the heart health risk symptom; The early warning module is used to issue early warnings based on the assessment results of heart health risk symptoms.

Images