Patient health early warning processing method and system based on various heart monitoring data

Abstract

The invention discloses a patient health early warning processing method and system based on various heart monitoring data, and the method comprises the following operation steps: collecting the heart monitoring data of a target patient, carrying out the sampling frequency and waveform preprocessing of the heart monitoring data, and obtaining an electrocardiogram signal; determining a waveform starting point and a waveform ending point of the electrocardiogram signal, and searching a waveform trend inflection point by using the waveform starting point and the waveform ending point; extracting heart rate mutation features according to the waveform trend inflection points, and performing heart health risk symptom assessment result identification on the heart rate mutation features; and performing early warning according to the assessment result of the heart health risk symptom.

Description

Technical Field

[0001] The present invention relates to the field of heart, and in particular to a method and system for processing health warning of patients with multiple heart monitoring data. Background Art

[0002] Heart disease, especially arrhythmia, heart failure and other diseases, has become a health problem with high mortality and disability rates worldwide.

[0003] With the continuous development of medical technology, heart monitoring equipment and technology have gradually matured, making it possible to monitor the patient's heart health status in real time.

[0004] These devices are able to record the heart's electrical activity in detail through electrocardiogram (ECG) signals, providing clinicians with important diagnostic information.

[0005] However, although ECG is widely used in clinical practice, due to the influence of factors such as noise, motion artifacts, and environmental interference, the original ECG signal often contains a lot of noise and incomplete information, which affects the quality of the signal and reduces the accuracy and reliability of the analysis.

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

[0007] In addition, the complexity of ECG signals requires highly accurate waveform analysis technology to detect heart disease.

[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 early warning.

[0009] Therefore, how to efficiently extract valuable heart disease characteristics from massive electrocardiogram data through intelligent analysis methods and timely detect potential heart health risks has become an important direction of research and development. Summary of the invention

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

[0011] The present invention provides a patient health warning processing method for multiple heart monitoring data, including the following steps: Collecting cardiac monitoring data from a target patient, performing sampling frequency and waveform preprocessing on the cardiac monitoring data, and obtaining an electrocardiogram signal; 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 assessment result of the heart health risk symptom; Provide early warning based on assessment results of heart health risk symptoms.

[0012] The present invention also proposes a patient health early warning processing system for multiple heart monitoring data, including: 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.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages: By analyzing the patient health warning processing method and system for multiple heart monitoring data provided by the present invention, it can be known that in specific applications, by preprocessing the sampling frequency and waveform of the heart monitoring data, noise can be removed to obtain a stable and clear electrocardiogram signal; Furthermore, a search window is set to slide and search for characteristic points of the electrocardiogram signal to obtain characteristic points of sudden changes in heart rate; the first-order derivative of the electrocardiogram signal waveform is calculated to obtain an identifiable rate of change, and the acceleration is obtained by calculating the second-order derivative of the rate of change, which helps to reveal the inflection point of the characteristic point of sudden changes in heart rate of the electrocardiogram signal and understand the changing trend of the electrocardiogram signal; a preset acceleration change threshold e is set to determine whether the acceleration of the rate of change is greater than the acceleration change threshold e, thereby determining whether there is a fluctuation point in the electrocardiogram signal, thereby extracting the stable part or transition segment of the waveform in the electrocardiogram; the waveform of the electrocardiogram signal is traversed to find local maximum points and local minimum points, and the local maximum points represent the peak values ​​of the R wave and the T wave, and the local minimum points represent the trough values ​​of the Q wave and the S wave, thereby obtaining the starting point and the ending point of the waveform, which are used as the QRS complex to represent the recovery or calm stage of the electrocardiogram; extracting the QRS complex The RR interval is calculated from the R wave of the RS wave group, which provides data on the heart cycle and is the basis for studying heart rhythm and heart rate fluctuations; the difference between the RR intervals is calculated to reflect abnormal heartbeats, arrhythmias and other problems; the average value between the RR intervals is calculated, and the rate of change of the RR interval is calculated by the average value and the difference value, thereby obtaining the periodic gradual change feature. By extracting these gradual change features, different physiological or emotional states (such as exercise, stress, relaxation, etc.) can be identified; the periodic gradual change feature screens abnormal periodic sequences, and the abnormal fluctuations of the abnormal periodic sequences are marked by the inflection point of the waveform trend to obtain the heart rate mutation feature, and determine whether the target patient has acute heart health problems; the heart rate mutation feature and the periodic gradual change feature are identified to produce heart results (the heart rate mutation feature and the periodic gradual change feature are identified to output the heart evaluation results), and the target patient's acute and short-term heart disease is determined, thereby issuing an early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a main flow chart of a patient health warning processing method for multiple heart monitoring data according to Embodiment 1; Figure 2 A specific flow chart of heart recognition of a patient health warning processing method for multiple heart monitoring data according to embodiment 1; Figure 3 A schematic diagram of confirming a QRS complex of a patient health warning processing method for multiple heart monitoring data according to Embodiment 1; Figure 4 This is a flow chart of a patient health warning processing method for multiple heart monitoring data in Example 1, which uses the RR interval to judge the evaluation result of the heart; Figure 5 A flowchart of a patient health warning processing method for multiple heart monitoring data in Example 1 for identifying the heart by using periodic gradual change characteristics and heart rate mutation characteristics; Figure 6This is a flow chart of a patient health warning processing method for multiple heart monitoring data according to embodiment 1 for identifying the heart according to the QT interval; Figure 7 A schematic diagram of the QT interval of a patient health warning processing method for multiple heart monitoring data in Example 1; Figure 8 This is a flow chart of a patient health warning processing system for multiple heart monitoring data according to Example 2.

[0015] Label: collection module 10; analysis module 20; early warning module 30. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0017] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0018] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0019] Embodiment 1 like Figure 1 As shown, the present invention provides a patient health warning processing method for multiple heart monitoring data, including the following steps: S1: Collect cardiac monitoring data from the target patient, perform sampling frequency and waveform preprocessing on the cardiac monitoring data, and obtain an electrocardiogram signal; It should be noted that by preprocessing the sampling frequency and waveform of the heart monitoring data, noise can be removed and the signal quality can be optimized, thus ensuring that subsequent analysis is accurate; the obtained electrocardiogram signal provides a stable and clear data basis for subsequent heart health analysis; S2: 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 assessment result of the heart health risk symptoms; It should be noted that by analyzing the starting and ending points of the waveform and finding the inflection point of the waveform trend, abnormal changes in the ECG signal (such as sudden changes in heart rate) can be detected efficiently; Determining the characteristics of heart rate mutations and assessing whether the outcome of heart disease is acute risk or chronic risk based on these characteristics can help identify potential heart problems in advance, including arrhythmias, heart failure, etc., especially in determining sinus arrhythmia. S3: Provide early warning based on the assessment results of heart health risk symptoms; It should be noted that according to the above technical solution, the heart disease assessment results based on the electrocardiogram signal can issue early warnings in time to help medical personnel discover and deal with potential heart risks; such early warnings can provide sufficient time for intervention before the disease develops to the acute stage, thereby reducing the risk of acute attacks and improving the patient's survival rate; Specifically, Figure 2 As shown, in step S2, the starting point and the ending point of the waveform of the electrocardiogram signal are confirmed, and the waveform trend inflection point is found by using the starting point and the ending point of the waveform; the heart rate mutation feature is extracted according to the waveform trend inflection point, and the heart rate mutation feature is identified to output the evaluation result of the heart health risk symptom. The specific operation steps are as follows: S21: setting a search window for the electrocardiogram signal, slidingly searching for feature points of the electrocardiogram signal according to the search window, and obtaining feature points of heart rate mutation; It should be noted that a reasonable search window should be set according to the sampling frequency and waveform characteristics of the signal; 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. In order to facilitate processing, a sliding search window method is used to gradually slide along the ECG signal sequence to search for characteristic points of heart rate mutations; S22: Calculate the first-order derivative of the ECG signal waveform (i.e., the rate of change of the waveform) to obtain the rate of change of the ECG signal waveform at each moment (i.e., the slope, which is a mathematical concept used to describe the degree of inclination of a straight line or curve; in the ECG signal, the slope indicates how fast the signal changes, and also reflects the steep changes of the rise and fall of the waveform). The calculation formula is: ; Where 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; The second-order derivative of the ECG signal is calculated using the rate of change of the ECG signal waveform at each moment to obtain the acceleration of the rate of change (i.e., the slope change rate). The calculation formula is: ; It should be noted that, if x(t) represents the potential change in the electrocardiogram, then it represents the speed of change of the electrocardiogram signal, or the degree of inclination of the signal at that moment, using the first-order derivative formula. The larger the slope, the faster the signal changes, and the smaller the slope, the slower the change. Through the first-order derivative, it is possible to identify areas where the signal changes more dramatically, such as the steep rise or fall of the waveform. Next, the second-order derivative of the ECG signal is calculated and expressed using the formula, which represents the change in the rate of change of the ECG signal, also called acceleration; the second-order derivative reflects the degree of change in the rate of change of the signal; if the first-order derivative represents the speed of change of the ECG signal, then the second-order derivative represents the change in the speed of change, that is, the acceleration of the ECG signal, which helps to reveal the inflection point of the heart rate mutation characteristic point of the ECG signal, especially in the transition area of ​​the waveform; When the acceleration of the ECG signal is large, the rate of change of the ECG signal changes greatly, usually at the inflection point of the waveform; for example, in the ECG, the start and end positions of the QRS complex are often where the acceleration changes greatly; When the acceleration of the ECG signal is zero or close to zero, the rate of change of the ECG signal tends to be stable, usually the flat top or flat bottom part of the waveform; S23: Preset an acceleration change threshold value e, and determine whether the acceleration of the change rate is greater than the acceleration change threshold value 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; It should be noted that when the acceleration of the rate of change (i.e., the rate of change of the slope) is greater than the set threshold, it usually indicates that the signal has changed rapidly or mutated. For example, at the boundary 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 rate of change. At the R wave position, the depolarization of the ventricle is very rapid, resulting in a large change in the potential, so the acceleration of the rate of change is large. When the slope change rate of the waveform is less than the set threshold, this usually indicates that the waveform changes at a relatively slow rate, 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 beginning and end stages of the P wave are represented by the P wave representing atrial depolarization, and its potential changes relatively slowly, so the slope change rate is relatively small; the P wave usually does not have obvious mutations; the transition section of the T wave is represented by the T wave representing ventricular repolarization, and although the T wave has certain changes, its change rate is slower than the rapid changes of the QRS complex; therefore, the slope change rate at the beginning and end of the T wave is usually small; the transition zone of the waveform is represented by the period between the QRS complex and the T wave, where the waveform change rate may gradually slow down, and the slope change rate is lower than the threshold, which usually indicates that the waveform tends to be stable, indicating the recovery or calm stage of the electrocardiogram); S24: traversing the waveform of the electrocardiogram signal to find a local maximum value point and a local minimum value point in the heart rate mutation feature point 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 the 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 eliminated; The local maximum point is used to represent the peak value of the R wave and the T wave; The local minimum points are used to represent the trough values ​​of the Q and S waves; It should be noted that identifying local maximum and minimum values ​​is the key feature point to determine the waveform, especially for R wave, T wave, Q wave, S wave; by traversing the ECG signal, find the point of local maximum value, usually at the peak of R wave and T wave; R wave is the strongest peak, and T wave peak is usually lower; find the local minimum point in the ECG signal, Q wave and S wave are the first negative peak and second negative peak in the ECG respectively; Q wave is usually the first negative wave to appear, and S wave usually appears after R wave (i.e. Figure 3 As shown in the figure, the normal duration of the QRS complex is 0.06 to 0.10 seconds, which is 1.5 to 2.5 small grids, and will never exceed 3 small grids); The amplitude difference between local extreme points (such as the amplitude difference between R wave and S wave) can be calculated through the local maximum point and the local minimum point. This difference helps to distinguish between normal and abnormal (i.e. atypical waveform) waveform patterns. When detecting extreme difference points, some pseudo extreme points may be encountered. These pseudo points may be noise or other atypical waveforms. These pseudo extreme points should be removed. S25: 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.

[0020] It should be noted that in the ECG signal processing, the rate of change of the waveform refers to the speed of change of the potential of the ECG signal (i.e., the slope of the ECG signal); the QRS complex is the part of the ECG with the fastest rate of change, so we can effectively distinguish the QRS complex (i.e., the components of the QRS complex are divided into Q wave: the first wave in the QRS complex that deflects downward; R wave: the wave in the QRS complex that deflects upward; S wave: the wave that deflects downward after the R wave; which constitutes the QRS complex, and the QRS complex is the part with the fastest rate of change in the ECG, and can also reflect the normal or abnormal state of the ECG signal to the greatest extent) from other relatively gentle waveforms (such as P wave and T wave). Selecting a suitable rate of change threshold (i.e., threshold position t) helps to distinguish the starting and ending points of the QRS complex by setting a threshold of the rate of change; The starting point of the QRS complex can be determined by judging whether the rate of change of the electrocardiogram signal exceeds the preset threshold position t for the first time. The starting point of the QRS complex usually occurs where the signal potential rises sharply, that is, when the slope exceeds the threshold for the first time. This position is usually the starting position of the R wave of the QRS complex, marking the beginning of ventricular depolarization; If the ECG signal does not exceed the preset rate of change threshold again, then we can regard the position where the threshold was exceeded for the last time as the end point of the QRS complex, which usually appears after the S wave, marking the end of the ventricular depolarization process; S26: Obtain the starting point time and the ending point time of the QRS complex through the starting point and the ending point of the waveform, and obtain the duration of the QRS complex through calculation through the starting point time and the ending point time; It should be noted that by determining the starting and ending points 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 heart rhythm problems); abnormal duration of the QRS complex may indicate certain problems in the cardiac conduction system, such as ventricular hypertrophy, conduction block, etc. S27: marking boundary points of the internal waveform segments through the starting point and the ending point of the QRS complex; Extract the waveform segments of all R waves of the electrocardiogram signal (i.e., all R waves of the QRS complex in the electrocardiogram signal) through the boundary points, calculate the time difference between every two adjacent R waves in the electrocardiogram signal, and 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); Calculate 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. The calculation formula is: ; In the formula, is the mean of the RR interval difference series; It is expressed as RR interval difference; N is the number of data points (i.e., data points are actual values ​​extracted from the ECG signal, and each RR interval value (i.e., the time difference between two adjacent R waves) is a data point; if an ECG signal is monitored, a series of RR intervals will be obtained after R wave detection, and these RR intervals are data points; when calculating the RR interval difference, 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); It should be noted that by determining the starting and ending points of the QRS complex, the boundary points of each waveform segment can be accurately marked (such as the starting and ending points of the R wave in each segment of the QRS complex). These boundary points are the basis of electrocardiogram analysis and diagnosis, helping doctors determine the health status of the heart; The R wave is usually the strongest wave in the electrocardiogram, representing the depolarization of the ventricle and a sign of detecting heartbeats. Therefore, taking the R wave of the electrocardiogram signal as an example, early warning identification of the heart is performed. The RR interval refers to the time difference between two adjacent R waves. The time interval between each two adjacent R waves (i.e., the value of each RR interval) is extracted from the electrocardiogram signal to obtain the RR interval sequence, which provides data on the heartbeat cycle and is the basis for studying heart rhythm and heart rate fluctuations. RR(i) is used to represent the time difference between the i-th two adjacent R waves in the RR interval sequence, and RR(i+1) is used to represent the time difference between two adjacent R waves with adjacent RR(i) time differences (i.e., RR(i) and RR(i+1) are the time differences between two adjacent RR intervals). The formula Indicates the calculation of the RR interval difference (that is, the RR interval difference is The difference reflects the fluctuation of the heartbeat cycle; when the heart rate is stable, the RR interval difference is small; when the heart rate changes greatly, the RR interval difference is large (which may indicate a symptom of arrhythmia); Calculation of the standard deviation of the RR interval difference (SDRR) is a statistical measure of the RR interval difference, indicating the degree of fluctuation of the RR interval difference. It can identify abnormal heartbeats, arrhythmias and other problems, and reflect heart health; S28: calculating the average value of n data points before and after each RR interval in the RR interval sequence; The rate of change (i.e., slope) of the RR interval series was calculated by the mean value and the standard deviation of the difference; The periodic gradual change characteristics of the RR interval sequence are obtained through the change rate of the RR interval sequence; According to the periodic gradual change characteristics, the waveform trend inflection points of the RR interval sequence due to the change rate are recorded; It should be noted that the n-point moving average is a method of smoothing signals. Through the n-point moving average processing, the interference caused by instantaneous fluctuations or noise in the electrocardiogram can be effectively removed, making the trend of heart rate changes on a long time scale more obvious; it can help identify changes in heart rate over a long period of time, such as a slowing of the heart rate during rest or an acceleration of the heart rate during exercise; The periodic gradual change features of the RR interval sequence are further extracted; for example, a constant negative slope indicates that the heartbeat is gradually slowing down, while a constant positive slope indicates that the heartbeat is gradually accelerating; the periodic gradual change features reveal the gradual change pattern of the heart rate over time; under certain physiological or psychological states, the heartbeat may gradually accelerate or slow down; for example, the heartbeat gradually accelerates during exercise and gradually slows down during rest; by extracting these gradual change features, different physiological or emotional states (such as exercise, stress, relaxation, etc.) can be identified; The slope calculated by the mean value and the standard deviation of the difference provides the rate of change of the heart rate (i.e. the rate of change) for each time point; the change of the heart rate is usually not sudden, but occurs gradually; by calculating the slope at each moment, the acceleration or deceleration of the heart rate can be quantified; for example, during exercise, the heart rate gradually accelerates and the slope value will gradually increase; during rest, the heart rate gradually slows down and the slope value gradually decreases; such quantitative analysis helps to understand the physiological process more accurately; The periodic gradual change feature refers to the pattern of gradual changes in heart rhythm or heart rate over time, which is usually manifested as a gradual increase or decrease in the heartbeat interval (i.e., RR interval), a gradual slowing or speeding up of the heart rate, and a gradual accumulation of changes in the QRS complex. This gradual change may be caused by gradual changes in the heart's physiological state, automatic regulation, or external stimulation, such as acceleration during exercise, deceleration during rest, etc. The heart rate gradually changes from a normal frequency to a slower or faster rhythm. This change may occur over a long period of time, reflecting changes in the heart during the physiological adaptation process. By calculating the change in slope based on the continuous change trend of the periodic gradual change characteristics, the turning point of 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) can be identified; the acceleration and deceleration of heart rate are usually not constant, but there are certain turning points; these turning points can be accurately captured through the change in slope, which is very important for monitoring changes in individual physiological status; S29: the RR interval sequence selects an abnormal cycle sequence according to the cycle gradual change feature, marks the abnormal fluctuation of the abnormal cycle sequence by the waveform trend inflection point, and obtains the heart rate mutation feature; identifies the heart rate mutation feature and the cycle gradual change feature and outputs the heart evaluation result; It should be noted that the characteristic of sudden change in heart rate refers to an obvious and rapid sudden change in heart rate in a short period of time, which is usually manifested as a sudden increase or decrease in heart rate, and the change is large; this sudden change may be caused by heart disease, sinus node abnormality, conduction system abnormality or certain external stimuli; the process includes sudden acceleration of heartbeat (such as tachycardia or atrial fibrillation), sudden slowing of heartbeat (such as bradycardia or sinus arrest), sudden arrhythmia (such as premature beats, ventricular fibrillation, etc.); for example, in the electrocardiogram, a sudden increase or decrease in heart rate may be related to acute disease or abnormal heart function. The amplitude of heart rate change is large, and the time is usually short; The periodic gradual change feature usually shows gradual and continuous changes, with a small change in heart rate, which may last for a long time and is usually related to normal physiological adaptation or chronic problems; the heart rate mutation feature shows sudden and sharp changes, with a large change in heart rate, which is 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 feature and the heart rate mutation feature involve changes in heart rate, their time scales, change patterns and physiological mechanisms are different; Specifically, Figure 4 As shown, in step S29, the RR interval sequence screens the abnormal cycle sequence according to the cycle gradual change feature, marks the abnormal fluctuation of the abnormal cycle sequence by the waveform trend inflection point, and obtains the heart rate mutation feature; the heart rate mutation feature and the cycle gradual change feature are identified and the heart evaluation result is output. The specific operation steps are as follows: S291: Preset a normal cycle range u according to the cycle gradual change feature (i.e., preset the normal cycle range u according to the characteristics of the cycle gradual change feature for identifying the physiological state of an individual (i.e., the characteristics capable of identifying, for example, the acceleration of the heartbeat during exercise and the deceleration of the heartbeat during rest)), and determine whether the RR interval in the RR interval sequence is within the normal cycle range u (i.e., the RR interval is within the preset normal cycle range u, and when the RR interval is within the normal cycle range u, it indicates that the heartbeat is normal); If the RR interval is equal to the normal cycle range u, then the RR interval is judged to be normal and no abnormality occurs; If the RR interval is greater than the normal cycle range u, the RR interval is judged 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 judged to be too short and 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; It should be noted that, according to the characteristics of the physiological state of an individual identified by the periodic gradual change feature (i.e., the characteristics capable of identifying the acceleration of the heartbeat during exercise and the deceleration of the heartbeat during rest), a normal cycle range u is preset, and it is judged whether there is an abnormal heart rate range in the RR interval in the RR interval sequence through the normal cycle range u (i.e., it is explained here that the RR interval is obtained by the time difference between two adjacent R waves in the electrocardiogram signal in the above step S26, so 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); and the preset normal cycle range u is the heart rate condition of the R wave of a normal human body within a certain time, that is, the time of the RR interval, so it is possible to judge whether the heart rate of the RR interval is normal through the normal cycle range u); If the RR interval is within the normal cycle range u, it indicates an abnormal heartbeat cycle, which may be arrhythmia or other problems; if the RR interval is less than the normal cycle range u, it indicates a fast heartbeat (too short cycle); if the RR interval is greater than the normal cycle range u, it indicates a slow heartbeat (too long cycle); S292: Counting the number of abnormal cycle sequences and calculating the proportion of abnormal cycle sequences to RR interval sequences; If the proportion of abnormal cycle sequences is greater than half of the RR interval sequences, the cycle mutation threshold o of the RR interval difference is preset; Determine whether the RR interval difference (that is, the RR interval difference here is the RR interval difference of the abnormally long cycle and the abnormally short cycle, the difference of the abnormal cycle in the abnormal cycle sequence, and the difference of the RR interval that does not belong to the normal heart rate) is greater than the 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; It should be noted that counting the number of RR intervals in the abnormal cycle sequence and then determining whether the number of RR intervals in the abnormal cycle sequence accounts for half or more of the total RR interval sequence can help determine the overall stability of the ECG signal; if so, it may indicate a problem with heart health; Abnormal fluctuations in the RR interval difference are one of the early signals for detecting arrhythmias.

[0021] Arrhythmias such as atrial fibrillation and premature ventricular beats often manifest as abnormal changes in the RR interval, which can be identified by calculating the RR interval difference. Therefore, by presetting the cycle mutation threshold o of the RR interval difference and judging whether the RR interval difference is greater than the cycle mutation threshold o, potential heart rhythm problems can be determined. S293: Mark the inflection point of the waveform trend of the RR interval with cycle mutation as the 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; It should be noted that the cycle 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 mutation points reflect the rapid changes in heart rate, such as the occurrence of arrhythmia; by identifying these inflection points, potential abnormal fluctuations can be warned; Between each cycle mutation point, the mutation amplitude of two adjacent RR intervals is calculated; the mutation amplitude reflects the drastic changes in the heartbeat cycle, usually corresponding to abnormal fluctuations in the electrocardiogram; this helps to detect and quantify drastic fluctuations in heart rate, which is especially important in determining whether there is arrhythmia; The QRS complex represents the process of cardiac contraction and is the most important clue to abnormal cardiac activity, while the R wave is the most representative of the changes in heart rate caused by the QRS complex. Therefore, all mutation amplitudes constitute the heart rate mutation characteristics of the QRS complex. Heart rate mutation characteristics are used as a basis for reflecting rapid changes in heart rate, and are mainly used for predicting abnormal fluctuations in electrocardiogram signals; S294: extracting mutation parameters and gradual change parameters from the heart rate mutation characteristics and the period gradual change characteristics respectively; using the mutation parameters and gradual change parameters to comprehensively evaluate the heart warning; the mutation parameters include: mutation occurrence frequency; the gradual change parameters include: gradual change frequency; It should be noted that the periodic gradual change feature usually manifests as a gradual and continuous change, with a small change in heart rate, which may last for a long time and is usually related to normal physiological adaptation or chronic problems; the heart rate sudden change feature manifests as a sudden and sharp change, with a large change in heart rate, which is usually caused by pathological reasons (such as arrhythmia, acute cardiac events) and usually occurs in a short period of time; Therefore, by extracting the gradual change parameters and the sudden change parameters through two different features, the periodic gradual change feature may be a precursor to the sudden change of heart rate. Especially in chronic heart problems, the gradual change state of the heart may eventually lead to the occurrence of sudden change of heart rate. Therefore, by monitoring the changes of the periodic gradual change feature, the potential risk of sudden change of heart rate can be identified in advance. Sudden change parameters are mainly used to detect acute heart problems and sudden risks, such as arrhythmias and acute cardiac events; gradual change parameters help reveal chronic heart disease or potential physiological adaptation problems, such as heart failure and gradual decline in heart function. Specifically, Figure 5 As shown, in step S294, 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, and the specific operation steps are as follows: S2941: analyzing the period mutation point of the heart rate mutation characteristic through the duration of the QRS complex, and taking the duration of the QRS complex as the mutation duration of the heart rate mutation characteristic; The above-mentioned cycle mutation point is marked as the turning point of the RR interval waveform trend; the RR interval is the time interval between two heartbeats, and the cycle mutation point reflects 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 a sudden change has occurred in the cardiac conduction, which may cause a mutation in the RR interval, and the duration of the mutation is the duration of the change in the duration of the QRS complex; if the duration of the QRS complex increases or decreases, and continues to change for a certain period of time, this period of change can be used as the mutation duration (TD) of the heart rate mutation feature; S2942: Obtain the frequency of occurrence of mutations based on the frequency of occurrence of excessively long periods and excessively short periods of the abnormal period sequence in the duration of the mutation; As explained in step S291, the abnormal cycle sequence is an abnormal mutation that occurs in the RR interval sequence. The frequency of the cycle mutation points is recorded by calculating the time intervals of the RR intervals of the excessively long and short cycles in the duration of the QRS complex (i.e., the duration of the mutation) (i.e., the RR interval is the time interval between two heartbeats), thereby obtaining the mutation occurrence frequency. S2943: extracting gradient parameters from the periodic gradient feature to obtain a gradient rate; In step S28, the periodic gradual change feature is obtained by calculating the slope of the mean value of the RR interval of the RR interval sequence and the standard deviation of the difference to provide the rate information of the heart rate change (i.e., the change rate) at each time point, so the change rate of the RR interval can be directly used as the gradual change rate of the periodic gradual change feature; S2944: Determine whether the gradual change rate of the periodic gradual change 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 gradual change rate and change trend can reveal potential chronic heart diseases (such as heart failure, myocardial ischemia, etc.)); If not, the cardiac monitoring data collected from the target patient is determined to be normal; If so, it is further determined whether a sudden change occurs by monitoring the frequency of sudden changes when the gradual change rate is too long (i.e., against the background of a gradually changing heart rate, if a sudden change occurs suddenly, it may mean the occurrence of a transition from a chronic problem to an acute problem; for example, when the gradual change of the heart rate becomes faster and faster and the amplitude increases, a sudden rapid fluctuation (sudden change) in the heart rate may indicate the occurrence of a serious arrhythmia); 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 (i.e., first, the target patient is checked through the periodic gradual change characteristics to see whether there is a chronic heart disease with a stable acceleration of the gradual change rate (i.e., the rate of change). If a chronic heart disease occurs, it is necessary to check whether the gradual change of the heart rate may suddenly become faster and faster (i.e., the acceleration of the rate of change is continuously increasing), so it is necessary to monitor the fixed mutation frequency that has been obtained to see whether a mutation occurs at a certain frequency, thereby converting the chronic heart disease into an acute heart disease, so as to remind the subsequent medical staff who have issued an early warning to prevent the target patient's condition from worsening and to provide timely treatment); If so, it is determined that the cardiac monitoring data collected by the target patient is abnormal and the target patient has an acute heart disease; It should be noted that by judging whether the gradient rate of the periodic gradient feature is too long, abnormal heart rate change rate can be identified. 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 gradient rate, especially changes in heart rate or RR interval; if the acceleration of this change (i.e., the change in the gradient rate) increases or accelerates, it means that the condition may be worsening; by monitoring these changes, potential acute events can be discovered early to prevent patients from entering an acute attack state without timely treatment; By monitoring the frequency of sudden changes in heart rate, drastic fluctuations in heart rhythm can be detected in time; sudden fluctuations often indicate the occurrence of serious arrhythmias. If they can be discovered and treated in time, the risk of acute heart disease can be greatly reduced, and rapid intervention can be used to avoid worsening of the patient's condition. When the gradient rate is abnormal, by monitoring the frequency of sudden changes in heart rate, the transition from chronic heart problems to acute problems can be detected in time. For example, if the heart rate suddenly fluctuates drastically when the gradient rate is accelerated or the acceleration is increased, this may be a precursor to arrhythmias or other acute conditions.

[0022] Specifically, Figure 6 As shown, in step S294, the sudden change parameter and the gradual change parameter are used to comprehensively evaluate the cardiac warning, and the QT interval is also used to identify the cardiac warning of QT prolongation. The specific operation steps are as follows: S2941': Obtain the RR interval time series of the electrocardiogram signal by calculating the RR intervals between all R waves in the QRS complex; It should be noted that the RR interval is the time interval between two adjacent R waves. The R wave is the most significant part of the QRS complex and represents the main part of ventricular depolarization. The intervals between multiple R waves are calculated to form a time series, called the RR interval time series; this can be regarded as a periodic time series, describing the fluctuation of heart rate (or heartbeat cycle) within a certain period of time; the RR interval time series can not only reflect the normal heartbeat rhythm, but also capture abnormal conditions, especially in the diagnosis of arrhythmia, QT interval prolongation and other diseases, it has a good recognition effect; S2942': The starting point of the QRS complex is taken as the starting point of the Q wave; The end point of the QRS complex is taken as the starting point of the T wave; It should be noted that the starting point and the ending point of the QRS complex have been obtained in step S26, and the QRS complex includes: Q wave, R wave, S wave, and the Q wave is the first wave segment in the QRS complex, so the starting point of the QRS complex is also the starting point of the Q wave; The T wave represents the process of ventricular repolarization. It is a waveform immediately following the QRS complex in the electrocardiogram signal, usually a positive wave. The T wave appears immediately after the QRS complex, so the end point of the QRS complex can be used as the starting point of the T wave. S2943': collecting a waveform segment after the QRS complex in the electrocardiogram signal as a preliminary T wave; The static state of the baseline of the waveform segment of the T wave initially obtained after identifying the QRS complex in the electrocardiogram signal; the baseline stability threshold p of the electrocardiogram signal is preset; Determine whether the baseline of the waveform segment of the T wave is less than the baseline stable threshold value p; If not, it is determined that the baseline of the waveform segment of the T wave is unstable, and the waveform segment of the T wave cannot determine the end point of the T wave; If so, the baseline of the waveform segment of the T wave is determined to be stable, and the waveform segment of the T wave is preliminarily obtained; The T wave waveform segment obtained initially is divided into several small segments [t1, t2], [t2, t3], ... [ti, ti+1], and the difference of each small segment is calculated to obtain the difference value of each small segment. The calculation formula is: ; In the formula, It is represented as a waveform segment of the T wave initially obtained after the QRS complex of the electrocardiogram signal; Determine whether the difference value of each segment is consistent; If not, it is determined that the differential value of each small segment is inconsistent, and the baseline fluctuation of the waveform segment of the T wave is initially obtained, and the waveform segment after the QRS complex in the electrocardiogram signal is re-collected as the T wave (that is, when the waveform segment is re-collected as the T wave, the distance of the waveform segment is increased in the waveform segment of the originally collected T wave to obtain a new waveform segment of the collected T wave); If yes, the difference values ​​of each small segment are determined to be consistent, and the baseline of the waveform segment of the T wave is determined to be stable, and the final confirmed T wave is obtained; It should be noted that a waveform segment after the QRS complex in the electrocardiogram signal is collected as the preliminary T wave, and whether the baseline of the preliminary T wave waveform segment is a straight line is identified; first, by presetting the baseline stability threshold p of the electrocardiogram signal, a preliminary judgment is made as to whether the baseline of the T wave waveform segment is stable; if the T wave baseline is a straight line, then the signal change in this area should be stable, so the baseline stability threshold p of the electrocardiogram signal is preliminarily judged as to whether the baseline of the T wave waveform segment is stable; if the baseline of the T wave waveform segment is in a stable state, then further verification is performed as to whether the baseline of the T wave waveform segment is a straight line, and the signal difference 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 waveform segment (i.e., the amount of change 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, so the T wave is obtained; S2944': determining the end point of the T wave through the waveform segment of the T wave; The QT interval is calculated based on the starting point of the Q wave and the ending point of the T wave. According to the QT interval, a QT interval time series of an electrocardiogram signal is obtained; It should be noted that this time series reflects the temporal changes of ventricular depolarization and repolarization in each cardiac cycle; by analyzing the QT interval time series, information about heart rate changes, QT interval changes, and possible cardiac abnormalities (such as QT interval prolongation or shortening) can be obtained; Figure 7 As shown, the position of the QT interval in the electrocardiogram signal, and the relationship between the QT interval and the starting point of the QRS complex and the end point of the T wave.

[0023] S2945': The QT interval time series is gradually identified by the periodic gradual change feature, and the change rate of the QT interval is calculated. The calculation formula is: ; In the formula, is the sum of weighted coefficients (i.e., the sum of coefficients usually used when calculating the weighted average; this part is used to weight the change (for example, using exponential decay or other weighting functions) so that data closer to the current moment has a greater impact on the rate of change); wi is the weighting factor (i.e., this is the weighting coefficient. For different time points i, the weighting coefficient wi will be set according to the selected weighting function (for example, exponential decay); generally speaking, the most recent time point i=0 will have the largest weight, and the weight will gradually decrease over time; this weight is usually: , where α is the decay factor, which determines the decay speed of the weight; in order to effectively capture the periodicity of the QT interval (i.e., when the heart rate increases, the QT interval will shorten; and when the heart rate slows down, the QT interval will lengthen, forming a periodic fluctuation) and periodic gradual change characteristics, 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, so that the closer time points (usually more relevant to the current moment) have a greater weight in the weighted calculation, while the farther time points (with less influence) gradually contribute less to the calculation results; 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 be temporarily shortened, and this change has the greatest impact on the current heart state; therefore, in periodic changes, the most recent few data points will have the strongest impact on the current trend); represents the value of the QT interval at time t−i; 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, and is usually used to measure the change in the QT interval between consecutive moments); Indicates the difference between the current QT interval and the previous QT interval (i.e., the change in the QT interval); It should be noted that QT prolongation is usually not a sudden event, but a gradual process. The QT interval may show periodic changes, so the periodicity of the periodic gradual change feature is used to gradually identify the QT interval time series (that is, gradual change identification is represented by an identification of the gradual change process of the QT interval, and a periodic change of the change rate of the QT interval is calculated); S2846': setting the time joint window by the change rate of the RR interval and the change rate of the QT interval; Simultaneously detect the heart rate mutation of QT interval time series and RR interval time series in the time joint window; If the heart rate increases suddenly within the time window, the QT interval is judged to be prolonged, and a warning signal of QT prolongation is issued; If the heart rate did not change within the time union window, the QT interval was judged to be normal; It should be noted that a sudden change in heart rate may affect the stability of the QT interval, leading to a prolongation or irregular fluctuation of the QT interval; therefore, sudden changes in heart rate may be closely related to the occurrence of QT prolongation; Use Joint Window Analysis to synchronously analyze the time series of QT interval and RR interval; by setting a time joint window, monitor the heart rate changes of QT interval time series and RR interval time series at the same time (i.e. monitor whether there is a sudden change in heart rate). If the heart rate suddenly increases in a certain time joint window, the QT interval shows a trend of periodic extension, which may be a warning sign of QT interval prolongation. Embodiment 2 like Figure 8 As shown, the present invention also provides a patient health early warning processing system for multiple heart monitoring data, including: a collection module 10; an analysis module 20; an early warning module 30; The acquisition module 10 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 20 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 30 is used to issue an early warning based on the evaluation results of the heart health risk symptoms.

[0024] In summary, the patient health warning processing method and system for multiple heart monitoring data proposed in the example of the present invention can remove noise and obtain a stable and clear electrocardiogram signal by preprocessing the sampling frequency and waveform of the heart monitoring data; Furthermore, a search window is set to slide and search for characteristic points of the electrocardiogram signal to obtain characteristic points of sudden changes in heart rate; the first-order derivative of the electrocardiogram signal waveform is calculated to obtain an identifiable change rate, and the acceleration is obtained by calculating the second-order derivative of the change rate, which helps to reveal the inflection point of the characteristic point of sudden changes in heart rate of the electrocardiogram signal and understand the change trend of the electrocardiogram signal; a preset acceleration change threshold e is set to determine whether the acceleration of the change rate is greater than the acceleration change threshold e, thereby determining whether there is a fluctuation point in the electrocardiogram signal, thereby extracting the stable part or transition section of the waveform in the electrocardiogram; the waveform of the electrocardiogram signal is traversed to find local maximum points and local minimum points, and the local maximum points represent the peak values ​​of the R wave and the T wave, and the local minimum points represent the trough values ​​of the Q wave and the S wave, thereby obtaining the starting point and the ending point of the waveform, which are used as the QRS complex to represent the recovery or calm stage of the electrocardiogram; Extracting the R wave of the QRS complex to calculate the RR interval provides data on the heart cycle and is the basis for studying heart rhythm and heart rate fluctuations; calculating the difference between the RR intervals can reflect problems such as abnormal heartbeats and arrhythmias; calculating the average value between the RR intervals, and calculating the rate of change of the RR interval through the average value and the difference value, thereby obtaining the periodic gradual change feature. By extracting these gradual change features, different physiological or emotional states (such as exercise, stress, relaxation, etc.) can be identified; using the periodic gradual change feature to screen abnormal periodic sequences, marking abnormal fluctuations in the abnormal periodic sequence through the inflection point of the waveform trend, obtaining the heart rate mutation feature, and determining whether the target patient has acute heart health problems; identifying the heart results of the heart rate mutation feature and the periodic gradual change feature, determining the target patient's acute and short-term heart disease, and thus issuing an early warning.

[0025] 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 it. A person skilled in the art may modify the technical solutions described in the above embodiments, or replace part or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to 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 assessment results of heart health risk symptoms.

2. According to claim 1, a patient health warning processing method for multiple heart monitoring data is characterized in that: 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: ; Where 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 the two heart rate mutation feature points; 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.

3. A patient health warning processing method for multiple heart monitoring data according to claim 2, 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.

4. The patient health warning processing method of multiple heart monitoring data according to claim 3 is 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 output as an evaluation result of the heart.

5. A patient health warning processing method for multiple heart monitoring data according to claim 4, 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.

6. A patient health warning processing method for multiple heart monitoring data according to claim 5, 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 interval 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.

7. A patient health warning processing method for multiple heart monitoring data according to claim 6, 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.

8. A patient health warning processing method for multiple heart monitoring data according to claim 7, 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.

9. A patient health warning processing system for multiple heart monitoring data, characterized in that: include: Acquisition module; Analysis module; 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; 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; The early warning module is used to issue early warnings based on the assessment results of heart health risk symptoms.

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