Monitoring system and monitoring method based on electrocardiograph

Abstract

The invention discloses a monitoring system and a monitoring method based on an electrocardiograph, which belong to the technical field of electrocardiographs, and are characterized in that interference levels are dynamically divided by extracting a common-mode offset change rate and a phase drift value between leads and carrying out normalized weighted calculation on an error severity index ESEI; under the moderate interference level, the digital filtering intensity is further adaptively adjusted based on the ESEI change trend, accurate evaluation and response regulation and control of the signal quality problem are achieved, and therefore the stability, the accuracy and the intelligent level of an electrocardiogram monitoring system in a complex application scene are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrocardiographs, and particularly to a monitoring system and a monitoring method based on an electrocardiograph. Background Art

[0002] An electrocardiograph is a medical device used to record and analyze the electrical activity of the heart. It captures the weak electrical signals generated by the heart during each beat by placing electrodes on the patient's body surface and converts these signals into an electrocardiogram (ECG or EKG). Medical staff can judge whether the heart rhythm is normal, whether there are arrhythmias, myocardial ischemia or other heart diseases by observing the waveform changes of the electrocardiogram. This device is widely used in clinical diagnosis, first aid, physical examination and long-term heart monitoring.

[0003] The prior art has the following deficiencies: In the prior art, when an electrocardiograph uses a differential amplifier to suppress common-mode noise (such as power supply interference), if the electrode impedances are seriously unbalanced, it may cause the common-mode signal to be converted into a differential-mode signal and be misamplified, resulting in serious interference. Especially during intraoperative monitoring or in a high-motion state, it is easy to cause misdiagnosis, false alarms or system failures. Summary of the Invention

[0004] The purpose of the present invention is to provide a monitoring system and a monitoring method based on an electrocardiograph to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A monitoring method based on an electrocardiograph, including: Obtaining the electrocardiogram signal of the user through a plurality of electrodes and inputting the electrocardiogram signal into a differential amplifier for preliminary amplification; Performing analog filtering and analog-to-digital conversion on the amplified electrocardiogram signal to generate digital electrocardiogram data; Extracting the common-mode offset change rate and the inter-lead phase drift value from the digital electrocardiogram data; Normalizing the common-mode offset change rate and the inter-lead phase drift value, and calculating the error severity index ESEI according to a preset weight; Dividing the interference degree caused by electrode impedance imbalance into multiple levels according to the value of the ESEI, and the levels include a slight interference level, a moderate interference level and a severe interference level; Dynamically adjusting the processing strategy of the monitoring system based on the moderate interference level, and the strategy includes adaptively adjusting the digital filtering intensity of the electrocardiogram signal according to the change of the adjusted ESEI.

[0006] Preferably, the obtaining the electrocardiogram signal of the user through a plurality of electrodes includes: Arranging a plurality of patch electrodes on the user's body surface; Using a standard 12-lead system or a subset thereof, attach electrodes to the designated anatomical positions on the limbs and chest. Form a set of leads with two electrodes to collect the bioelectric potential difference formed by the cardiac electrical activity. The signals collected by the electrodes are transmitted through wires to the input end of a differential amplifier, and two signal sources are input to each lead channel.

[0007] Preferably, the analog filtering and analog-to-digital conversion of the amplified electrocardiogram signals include: Perform high-pass filtering, low-pass filtering, and notch filtering on the amplified analog electrocardiogram signals in sequence. The high-pass filter is used to remove baseline drift and low-frequency noise, and its cut-off frequency is set between 0.05 Hz and 0.5 Hz. The low-pass filter is used to suppress high-frequency myoelectric and radio frequency interference, and its cut-off frequency is set between 100 Hz and 150 Hz. The notch filter is used to suppress power supply power frequency interference, and the center frequency is 50 Hz or 60 Hz. The filtered analog signal is digitized through an analog-to-digital converter, with a sampling rate of 250 Hz to 1000 Hz and a resolution of 12 bits or 16 bits.

[0008] Preferably, extracting the common-mode offset change rate and the inter-lead phase drift value from the digital electrocardiogram data specifically includes: The method for obtaining the common-mode offset change rate is: select two symmetric lead signals, including the original digital data of lead I and lead II, and denote them respectively as ; calculate the instantaneous common-mode signal , and the expression is: ; define a time window T, which contains N sampling points; calculate the common-mode offset change rate CMODR within the window: ; in the formula, is the instantaneous common-mode signal at the starting time point of the time window, is the instantaneous common-mode signal at the ending time point of the time window.

[0009] Preferably, the method for obtaining the inter-lead phase drift value is: set the two selected electrocardiogram leads as , which respectively represent the electrocardiogram signals of the two leads on the same time axis. Select a time window from the continuous electrocardiogram data, which contains M sampling points; perform cross-correlation processing on the two lead signals within the time window to obtain their correlation function at different time delays, and the expression is: ; where τ is the time lag variable, and find the lag amount that maximizes the cross-correlation function, and the expression is: ; Convert it into time units to obtain the inter-lead phase drift value ILPD, and convert the number of sampling points into actual time units: ; where Δt is the time interval corresponding to each sampling point.

[0010] Preferably, normalize the common-mode offset change rate and the inter-lead phase drift value, and calculate the error severity index ESEI according to a preset weight, specifically including: Normalize the common-mode offset change rate and the inter-lead phase drift value so that they are both between [0, 1], and calculate the error severity index ESEI according to the normalized common-mode offset change rate and inter-lead phase drift value.

[0011] Preferably, divide the interference degree caused by electrode impedance imbalance into multiple levels according to the value of the ESEI. The levels include a slight interference level, a moderate interference level, and a severe interference level, specifically including: Compare the obtained error severity index ESEI with gradient standard thresholds. The gradient standard thresholds include a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold. Compare the error severity index ESEI with the first standard threshold and the second standard threshold respectively; If the error severity index ESEI is greater than the second standard threshold, it is determined to be in the severe interference level, an alarm prompt is immediately issued, the electrode state is rechecked, or a lead switching and enhanced filtering strategy is enabled; If the error severity index ESEI is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it is determined to be in the moderate interference level, and a warning prompt, enhanced digital filtering, or increased sampling redundancy is performed; If the error severity index ESEI is less than the first standard threshold, it is determined to be in the slight interference level, and the normal analysis process is continued.

[0012] Preferably, the adaptive adjustment of the filtering intensity according to the change of the ESEI includes: When an increasing trend of the ESEI is detected, enhance the filtering intensity; When a decreasing trend of the ESEI is detected, reduce the filtering intensity; When the change amplitude is less than the set micro-variation threshold ϵ, keep the current filtering parameters unchanged.

[0013] Preferably, the adaptive filtering method is based on variable-scale empirical mode decomposition, including: Decompose the electrocardiogram signal into multiple IMF modes; Identify the high-frequency noise IMF and filter it out; Dynamically adjust the spline interpolation point density K and the envelope extreme window length W.

[0014] The present invention proposes a monitoring system based on an electrocardiograph, which includes a signal acquisition module, a front-end signal processing module, a feature extraction module, an interference evaluation and fusion analysis module, an interference level judgment module, and an adaptive signal optimization module; Signal acquisition module: Obtain the electrocardiogram signal of the user through multiple electrodes, and input the electrocardiogram signal into a differential amplifier for preliminary amplification; Front-end signal processing module: Perform analog filtering and analog-to-digital conversion on the amplified electrocardiogram signal to generate digital electrocardiogram data; Feature extraction module: Extract the common-mode offset change rate and the inter-lead phase drift value from the digital electrocardiogram data; Interference evaluation and fusion analysis module: Normalize the common-mode offset change rate and the inter-lead phase drift value, and calculate the error severity index ESEI according to the preset weight; Interference level judgment module: Divide the interference degree caused by electrode impedance imbalance into multiple levels according to the value of the ESEI, and the levels include a slight interference level, a moderate interference level, and a severe interference level; Adaptive signal optimization module: Dynamically adjust the processing strategy of the monitoring system based on the moderate interference level, and the strategy includes adaptively adjusting the digital filtering intensity of the electrocardiogram signal according to the change of the ESEI.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are as follows: 1. By introducing two characteristic parameters, namely the common-mode offset change rate (CMODR) and the inter-lead phase drift value (ILPD), the present invention effectively identifies the interference of the electrocardiogram signal caused by problems such as electrode impedance imbalance and channel asynchronization, and calculates the error severity index (ESEI) through normalization and weighted fusion, realizing the refined quantification and classification of the interference degree. Compared with the existing technologies that rely on single noise characteristics or threshold judgment methods, this solution more comprehensively reflects the signal quality state, significantly improving the system's perception ability and reaction accuracy to signal abnormalities under abnormal working conditions (such as intraoperative interference, mobile monitoring).

[0016] 2. The present invention introduces the variable-scale empirical mode decomposition (VSEMD) algorithm at the moderate interference level, and dynamically adjusts the key parameters of the filter (such as the density of spline interpolation points and the envelope extreme window length) based on the change trend of the ESEI, realizing the adaptive filtering optimization of the electrocardiogram signal. This strategy not only avoids the waveform distortion caused by over-filtering, but also effectively suppresses dynamic interference while ensuring the integrity of key waveform features, significantly improving the stability, accuracy, and intelligent level of the electrocardiogram monitoring system in complex application scenarios. Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments described in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0018] Figure 1 This is the method mind map of the present invention.

[0019] Figure 2 This is the system module mind map of the present invention. Detailed implementation manners

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0021] Embodiment 1. Please refer to Figure 1 As shown, a monitoring method based on an electrocardiograph in this embodiment includes: Obtaining an electrocardiogram signal of a user through multiple electrodes and inputting the electrocardiogram signal into a differential amplifier for preliminary amplification; Performing analog filtering and analog-to-digital conversion on the amplified electrocardiogram signal to generate digital electrocardiogram data; Extracting a common-mode offset change rate and an inter-lead phase drift value from the digital electrocardiogram data; Normalizing the common-mode offset change rate and the inter-lead phase drift value, and calculating an error severity index ESEI according to a preset weight; Dividing the interference degree caused by electrode impedance imbalance into multiple levels according to the value of the ESEI. The levels include a slight interference level, a moderate interference level, and a severe interference level; Dynamically adjusting the processing strategy of the monitoring system based on the moderate interference level. The strategy includes adaptively adjusting the digital filtering intensity of the electrocardiogram signal according to the change of the adjusted ESEI.

[0022] The electrocardiogram (ECG) signals of a user are obtained through multiple electrodes, specifically including: arranging multiple electrodes on the user's body surface, where the electrodes are disposable patch electrodes or reusable electrodes made of Ag / AgCl; adopting the standard 12-lead system or its subset (such as 3-lead, 5-lead), and the electrodes are attached to the corresponding positions on the limbs (RA, LA, RL, LL) and chest (V1–V6); each group of leads consists of two electrodes for differential input to collect the bioelectric potential difference formed by the cardiac electrical activity.

[0023] Each electrode is connected to the front-end signal acquisition module through a cable; to improve the signal quality, the skin surface needs to be cleaned before attaching the electrode to reduce the skin-electrode contact impedance; the electrode impedance is controlled within the range of conventional medical requirements (<5kΩ), and at the same time, it is ensured that the bilateral lead impedances are as matched as possible; the signals collected by the electrodes are usually 0.5mV to 4mV, and the frequency range is from 0.05Hz to 150Hz.

[0024] The differential amplifier (such as INA333 or AD620) has a high common-mode rejection ratio (CMRR), usually ≥100dB; the two input terminals of the differential amplifier are respectively connected to the two electrodes of a lead to measure the potential difference; the output signal of the amplifier is a multiple of the difference of the input signals, and the amplification factor can be set, such as 20 to 100 times; the differential amplifier amplifies the differential-mode signals of the two input electrodes and effectively suppresses the common-mode signals (such as power supply power frequency noise) that exist commonly on the two electrodes.

[0025] The differential amplifier ensures that the common-mode signals are affected equally on the paths of the two input terminals through symmetric design, so as to cancel each other out during the amplification process; common common-mode interference sources include: 50Hz / 60Hz power supply noise (such as introduced by lighting, medical equipment); electric field induction around the patient; potential fluctuations at the electrode-skin interface caused by body movement; if the electrode impedances are symmetric, the common-mode rejection effect is ideal; if the impedances are unbalanced, the differential amplifier may misamplify the common-mode noise as a differential-mode signal.

[0026] The system can monitor the electrode impedance of each channel in real time (for example, through an injected micro-current test circuit); if it is detected that the impedance of a certain channel exceeds the standard or is significantly unbalanced (such as the difference > 2kΩ), the user can be prompted to reattach the electrode or the lead can be automatically switched; the common-mode signal monitoring algorithm can be combined to judge whether there is a risk of "differential-mode misamplification of common-mode", and a protection or compensation mechanism can be triggered.

[0027] The amplified ECG signals are subjected to analog filtering and analog-to-digital conversion to generate digital ECG data, specifically including: The amplified ECG signals first enter the analog filter circuit to remove the low-frequency drift, high-frequency interference and power supply power frequency noise existing in the signals. The analog filter generally consists of three stages in series: a high-pass filter, a low-pass filter and a notch filter.

[0028] A high-pass filter is used to remove low-frequency components such as DC offset and baseline drift in the electrocardiogram (ECG) signal. Its cut-off frequency is generally designed between 0.05 Hz and 0.5 Hz, which can effectively filter out low-frequency interference caused by body position changes, respiratory movements, etc., while retaining low-frequency physiological information such as P waves and ST segments. The high-pass filter is usually constructed using an RC circuit or an operational amplifier. When designing, attention should be paid to avoiding signal attenuation in the diagnostically sensitive area due to an overly high cut-off frequency.

[0029] Subsequently, the signal enters a low-pass filter to suppress high-frequency signals such as electromyogram interference and radio frequency coupling. The cut-off frequency of the low-pass filter is generally set in the range of 100 Hz to 150 Hz to balance the integrity of the QRS waveform and the effective suppression of high-frequency noise. This filter usually adopts a multi-stage structure, such as a Sallen-Key or Butterworth filter, to ensure sufficient roll-off characteristics while retaining waveform details.

[0030] To further suppress power interference in the environment, the signal also needs to be processed by a notch filter. The notch filter is specifically designed to attenuate the 50 Hz or 60 Hz frequency components, depending on the national power grid standard. Common designs include active Twin-T notch filters, which can provide deep attenuation near the target frequency while minimizing the impact on other frequency components. To avoid phase distortion introduced by the notch, the bandwidth and Q value should be carefully selected.

[0031] The filtered ECG signal is then sent to the analog-to-digital converter (ADC) module. The ADC converts the analog signal into a digital signal for subsequent digital signal processing and analysis. In a typical system, the ADC has a high resolution (such as 12 bits, 16 bits or higher) to capture weak ECG signal fluctuations. The sampling rate is generally not less than 250 Hz and often reaches 500 Hz to 1000 Hz in medical-grade devices to ensure the restoration accuracy of rapidly changing signals such as QRS waves.

[0032] The analog-to-digital conversion process is controlled by a timer to ensure uniform sampling time intervals and enable multi-channel synchronous sampling to support multi-lead recording. The ADC front end is usually equipped with a clipping and protection circuit to prevent chip damage caused by overvoltage or electrostatic discharge. The converted digital data forms the raw data basis of the electrocardiogram, which can directly enter the digital filtering, feature recognition and diagnosis modules, or can be stored or transmitted to external devices or remote platforms through a communication interface.

[0033] Extract the common-mode offset change rate and the inter-lead phase drift value from the digital ECG data, specifically including: The common-mode offset change rate is used to evaluate the degree of fluctuation of the common-mode voltage within a short period of time, indirectly reflecting the dynamic change of the common-mode noise caused by electrode impedance imbalance.

[0034] The acquisition method is as follows: Select the original digital data of two symmetric leads, including Lead I and Lead II, and denote them respectively as ; Calculate the instantaneous common-mode signal , and the expression is: ; This is the average value of the two signals, which should theoretically be approximately zero, but may deviate under electrode imbalance or interference.

[0035] Define the time window T, for example, 1 second, which contains N sampling points; Calculate the common-mode offset change rate CMODR within the window: ; In the formula, is the instantaneous common-mode signal at the start time point of the time window, is the instantaneous common-mode signal at the end time point of the time window.

[0036] When the common-mode offset change rate (CMODR) is larger, it indicates that the common-mode voltage has obvious fluctuations within the selected time window, reflecting that the system is strongly affected by common-mode interference, which is usually related to electrode impedance imbalance, poor electrode contact, or changes in external electromagnetic interference. Such changes will cause the differential amplifier to misidentify the common-mode signal that should have been suppressed as a differential-mode signal, thus directly affecting the waveform quality of the electrocardiogram, increasing the severity of errors, and even causing the risk of misdiagnosis.

[0037] On the contrary, if the common-mode offset change rate is extremely small or close to zero, it usually indicates that the common-mode signal remains stable in time, the electrode state is good, and the common-mode rejection ability of the system works normally, with a low error risk. However, if CMODR remains zero or nearly constant, it is also necessary to consider whether there is a "dead zone" or sampling anomaly in the system. Therefore, other parameters (such as the inter-lead phase drift ILPD) should be combined for cross-verification to comprehensively evaluate the severity of errors.

[0038] The inter-lead phase drift value is used to detect the waveform time alignment between different leads, reflecting the signal timing misalignment caused by interference or hardware inconsistency.

[0039] Set the two selected electrocardiogram leads as , which respectively represent the electrocardiogram signals of the two leads on the same time axis, with the unit of millivolt (mV).

[0040] Select a time window from the continuous electrocardiogram data, for example, 2 seconds, which contains M sampling points (such as 1000 points) for local phase drift calculation.

[0041] Perform cross-correlation processing on the two lead signals within this time window to obtain the correlation function at different time delays , and the expression is: ; where τ is the time lag variable, with the unit of sampling points. Find the lag amount that maximizes the cross-correlation function , and the expression is: ; Converted to time unit, the inter-lead phase drift value ILPD is obtained. Convert the sampling points to the actual time unit (such as milliseconds): ; where Δt is the time interval corresponding to each sampling point (for example, at a sampling rate of 500 Hz, Δt = 2 ms).

[0042] When the inter-lead phase drift value (ILPD) is larger, it indicates that there is an obvious waveform misalignment in time between two electrocardiogram leads, which usually reflects problems such as signal synchronization abnormality, uneven interference influence, or inconsistent hardware channel delay. In this case, the system is prone to errors when extracting the timing relationship between multiple leads (such as electrocardiogram axis direction, P-QRS-T wave matching). Seriously, it may lead to rhythm recognition distortion or misjudgment of pathological waveforms, thus significantly increasing the severity of diagnostic errors.

[0043] On the contrary, if the inter-lead phase drift value is small or close to zero, it usually indicates that the signals of the two leads maintain good synchronization on the time axis, and the signal acquisition and channel processing processes are stable and consistent, with low system error. However, if the ILPD is continuously and abnormally zero, it may also mean that the signal is covered by homologous interference or there is a short circuit in the channel resulting in pseudo-synchronization phenomenon in a specific environment. Therefore, it is necessary to make a comprehensive judgment by combining other signal quality parameters.

[0044] Normalize the common-mode offset change rate and the inter-lead phase drift value, and calculate the error severity index ESEI according to the preset weights, specifically including:[[]] Normalize the common-mode offset change rate and the inter-lead phase drift value so that they are both within [0,1], and calculate the error severity index ESEI according to the normalized common-mode offset change rate and the inter-lead phase drift value.

[0045] For example, the present invention can use the following formula to calculate the error severity index ESEI, and the calculation expression is: ; In the formula, is the error severity index, is the inter-lead phase drift value, is the common-mode offset change rate, is the weight coefficient of the common-mode offset change rate and the inter-lead phase drift value (which can be optimized according to experimental experience or machine learning), and are both greater than 0.

[0046] Divide the interference degree caused by electrode impedance imbalance into multiple levels according to the value of the ESEI. The levels include mild interference level, moderate interference level, and severe interference level, specifically including: Compare the obtained error severity index ESEI with the gradient standard thresholds. The gradient standard thresholds include a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold. Compare the error severity index ESEI with the first standard threshold and the second standard threshold respectively; If the error severity index ESEI is greater than the second standard threshold, it is determined to be in the severe interference level. The system should immediately issue an alarm prompt and recommend that the user re-check the electrode status or enable the lead switching and enhanced filtering strategy; If the error severity index ESEI is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it is determined to be in the moderate interference level. The system can perform a warning prompt, enhanced digital filtering, or increase sampling redundancy; If the error severity index ESEI is less than the first standard threshold, it is determined to be in the slight interference level. It can be considered that the signal quality is within an acceptable range, and the normal analysis process continues.

[0047] Dynamically adjust the processing strategy of the monitoring system based on the moderate interference level. The strategy includes adaptively adjusting the digital filtering intensity of the electrocardiogram signal according to the change of the adjusted ESEI, specifically including: When the error severity index (ESEI) is in the moderate interference level, that is, between the first standard threshold and the second standard threshold, it indicates that the electrocardiogram signal is affected by interference that can be recognized but not severely distorted. To improve the signal quality and ensure that key waveform features (such as QRS waves and T waves) are not masked, the present invention adopts a variable-scale empirical mode decomposition (VSEMD) algorithm to dynamically adjust the digital filtering intensity and achieve adaptive suppression of interference signals.

[0048] When the error severity index ESEI is greater than or equal to the first standard threshold and less than or equal to the second standard threshold and the change rate of ESEI exceeds the set threshold, trigger the dynamic filtering enhancement mode; Apply VSEMD to the electrocardiogram signal V(t) and decompose it into multiple intrinsic mode functions (IMFs). Each IMF represents an oscillatory component at a different frequency scale; According to the signal noise estimation model (such as judging based on higher-order statistics or SNR), identify the high-frequency IMF components representing noise interference and selectively filter them out; Superimpose the remaining effective IMFs to reconstruct the enhanced electrocardiogram signal Vclean(t), retain the key waveform features, and remove the moderate interference; According to the change trend of ESEI, dynamically adjust the scale control parameters of VSEMD (such as the density of spline interpolation points, the length of the envelope extreme value window, etc.) to achieve adaptive enhancement or weakening of the filtering intensity, specifically including: Within each electrocardiogram processing cycle (such as 1 second or one cardiac cycle), calculate the latest error severity index ESEI(t) in real time; Record the change in ESEI, ΔESEI, between the current cycle and the previous cycle: ΔESEI = ESEI(t) − ESEI(t−Δt); where ESEI(t) represents the error severity index at the current time point t, and Δt represents the time interval of the processing cycle, such as 1 second or one complete cardiac cycle; Judge the current interference trend based on the sign and absolute value of ΔESEI: If ΔESEI > 0: The interference intensifies; If ΔESEI < 0: The interference weakens; If : It is regarded as a stable state, where ϵ is a set micro-variation threshold.

[0049] Define an adjustment coefficient λ to control the change amplitude of the scale parameter, which can be set as: ; where is the theoretical maximum value of ESEI (such as 1), is the maximum allowable adjustment rate (such as 0.3); if the interference intensifies, increase the filtering intensity (reduce the scale); if the interference weakens, decrease the filtering intensity (enlarge the scale).

[0050] Dynamically adjust the VSEMD scale control parameter: The density of spline interpolation points (K) represents the number of extreme point interpolations used to construct the signal envelope curve; adjustment method: If the interference intensifies: ; If the interference weakens: ; Kprev is the density of spline interpolation points in the previous cycle, used for envelope construction; Knew is the updated density of interpolation points at present; Kbase is the basic adjustment unit of the density of interpolation points, indicating how many interpolation points should be increased or decreased each time, which can be set according to experience (such as 5 points / cycle).

[0051] The denser the interpolation points, the finer the envelope curve, and the higher the resolution of the decomposed IMF.

[0052] The length of the envelope extreme window (W) represents the width of the sliding window used to extract extreme values when constructing IMF components; adjustment method: If the interference intensifies: ; If the interference weakens: ; Wprev is the length of the envelope extreme window in the previous cycle, which affects the sensitivity of extreme point extraction; Wnew is the adjusted window length, which determines the response ability to local oscillations; The smaller the window, the more sensitive the response to high-frequency noise and the stronger the filtering.

[0053] Wbase is the adjustment unit of the envelope extreme value window length, indicating the number of basic pixels or sampling points changed each time, usually between 5 and 20 points.

[0054] Execute VSEMD using the updated Knew and Wnew; decompose the signal into several IMFs; after judging and filtering out the high-frequency interference IMFs, reconstruct the filtered electrocardiogram signal; output the new waveform and enter the next processing cycle.

[0055] If within several consecutive cycles (such as 3 to 5) , it can enter the stable mode and maintain the current parameters; if a mutation or jump in ESEI is detected, quickly reset the parameters to the default safety value to avoid over-processing of the signal or waveform distortion.

[0056] Example 2, please refer to Figure 2 As shown, a monitoring system based on an electrocardiograph in this embodiment includes a signal acquisition module, a front-end signal processing module, a feature extraction module, an interference evaluation and fusion analysis module, an interference level judgment module, and an adaptive signal optimization module; Signal acquisition module: Obtain the electrocardiogram signal of the user through multiple electrodes and input the electrocardiogram signal into a differential amplifier for preliminary amplification; Front-end signal processing module: Perform analog filtering and analog-to-digital conversion on the amplified electrocardiogram signal to generate digital electrocardiogram data; Feature extraction module: Extract the common-mode offset change rate and the inter-lead phase drift value from the digital electrocardiogram data; Interference evaluation and fusion analysis module: Normalize the common-mode offset change rate and the inter-lead phase drift value, and calculate the error severity index ESEI according to the preset weight; Interference level judgment module: Divide the interference degree caused by electrode impedance imbalance into multiple levels according to the value of ESEI, and the levels include a slight interference level, a moderate interference level, and a severe interference level; Adaptive signal optimization module: Dynamically adjust the processing strategy of the monitoring system based on the moderate interference level, and the strategy includes adaptively adjusting the digital filtering intensity of the electrocardiogram signal according to the change of the adjusted ESEI.

[0057] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0058] As described above, it is only the specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A monitoring method based on an electrocardiograph, characterized in that: Including: Obtaining the electrocardiogram (ECG) signal of the user through multiple electrodes, and inputting the ECG signal into a differential amplifier for preliminary amplification; Performing analog filtering and analog-to-digital conversion on the amplified ECG signal to generate digital ECG data; Extracting the common-mode offset change rate and the inter-lead phase drift value from the digital ECG data; Normalizing the common-mode offset change rate and the inter-lead phase drift value, and calculating the error severity index ESEI according to a preset weight; Dividing the interference degree caused by electrode impedance imbalance into multiple levels according to the value of the ESEI, and the levels include a slight interference level, a moderate interference level, and a severe interference level; Dynamically adjusting the processing strategy of the monitoring system based on the moderate interference level, and the strategy includes adaptively adjusting the digital filtering intensity of the ECG signal according to the change of the adjusted ESEI.

2. The monitoring method based on an electrocardiograph according to claim 1, wherein: The obtaining the electrocardiogram (ECG) signal of the user through multiple electrodes includes: Arranging multiple patch electrodes on the body surface of the user; Adopting a standard 12-lead system or a subset thereof, and attaching the electrodes to the specified anatomical positions on the limbs and chest; Forming a group of leads with two electrodes to collect the bioelectric potential difference formed by the cardiac electrical activity; The signals collected by the electrodes are transmitted to the input end of the differential amplifier through wires, and two signal sources are input into each lead channel.

3. The monitoring method based on an electrocardiograph according to claim 1, characterized in that: The performing analog filtering and analog-to-digital conversion on the amplified ECG signal includes: Successively performing high-pass filtering, low-pass filtering, and notch filtering on the amplified analog ECG signal; The high-pass filter is used to remove baseline drift and low-frequency noise, and its cut-off frequency is set between 0.05 Hz and 0.5 Hz; The low-pass filter is used to suppress high-frequency myoelectric and radio frequency interference, and its cut-off frequency is set between 100 Hz and 150 Hz; The notch filter is used to suppress the power supply power frequency interference, and the center frequency is 50 Hz or 60 Hz; The filtered analog signal is digitized through an analog-to-digital converter, and the sampling rate is between 250 Hz and 1000 Hz, and the resolution is 12 bits or 16 bits.

4. A monitoring method based on an electrocardiograph according to claim 1, characterized in that: Extracting the common-mode offset change rate and the inter-lead phase drift value from the digital ECG data specifically includes: The method for obtaining the common-mode offset change rate is as follows: Select two symmetric lead signals, including the original digital data of Lead I and Lead II, and denote them respectively as ; Calculate the instantaneous common-mode signal , and the expression is: ; Define a time window T, which contains N sampling points; Calculate the common-mode offset change rate CMODR within the window: ; In the formula, is the instantaneous common-mode signal at the starting time point of the time window, is the instantaneous common-mode signal at the ending time point of the time window.

5. The monitoring method based on an electrocardiograph according to claim 4, wherein: The method for obtaining the inter-lead phase drift value is as follows: Set two selected electrocardiogram leads as , which respectively represent the electrocardiogram signals of two leads on the same time axis. Select a time window from the continuous electrocardiogram data, which contains M sampling points; perform cross-correlation processing on the two lead signals within the time window to obtain their correlation function , and the expression is: ; where τ is the time lag variable, and find the lag amount that maximizes the cross-correlation function, and the expression is: ; convert it to time unit to obtain the inter-lead phase drift value ILPD, and convert the number of sampling points to the actual time unit: ; where Δt is the time interval corresponding to each sampling point.

6. The monitoring method based on an electrocardiograph according to claim 5, characterized in that: Normalizing the common-mode offset change rate and the inter-lead phase drift value, and calculating the error severity index ESEI according to a preset weight, specifically including: Normalizing the common-mode offset change rate and the inter-lead phase drift value so that they are both between [0, 1], and calculating the error severity index ESEI according to the normalized common-mode offset change rate and the inter-lead phase drift value.

7. The monitoring method based on an electrocardiograph according to claim 6, characterized in that: Dividing the interference degree caused by electrode impedance imbalance into multiple levels according to the value of the ESEI, and the levels include a slight interference level, a moderate interference level, and a severe interference level, specifically including: Comparing the obtained error severity index ESEI with gradient standard thresholds, the gradient standard thresholds include a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and comparing the error severity index ESEI with the first standard threshold and the second standard threshold respectively; If the error severity index ESEI is greater than the second standard threshold, it is determined to be in the severe interference level, and an alarm prompt is immediately issued to re-check the electrode status, or enable the lead switching and enhanced filtering strategy; If the error severity index ESEI is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it is determined to be in the moderate interference level, and a warning prompt, enhanced digital filtering or increased sampling redundancy is performed; If the error severity index ESEI is less than the first standard threshold, it is determined to be in the slight interference level, and the normal analysis process continues.

8. A monitoring method based on an electrocardiograph according to claim 7, characterized in that: The adaptive adjustment of the filtering strength according to the change of ESEI includes: When an increasing trend of ESEI is detected, the filtering strength is enhanced; When a decreasing trend of ESEI is detected, the filtering strength is reduced; When the change amplitude is less than the set micro-variation threshold ϵ, the current filtering parameters are maintained unchanged.

9. The monitoring method based on an electrocardiograph according to claim 8, characterized in that: The adaptive filtering method is based on variable-scale empirical mode decomposition and includes: Decompose the electrocardiogram signal into multiple IMF modes; Identify and filter out the high-frequency noise IMF; Dynamically adjust the spline interpolation point density K and the envelope extreme window length W.

10. A monitoring system based on an electrocardiograph for implementing a monitoring method based on an electrocardiograph according to any one of claims 1-9, characterized in that: It includes a signal acquisition module, a front-end signal processing module, a feature extraction module, an interference evaluation and fusion analysis module, an interference level judgment module, and an adaptive signal optimization module; Signal acquisition module: Obtain the electrocardiogram signal of the user through multiple electrodes, and input the electrocardiogram signal into a differential amplifier for preliminary amplification; Front-end signal processing module: Perform analog filtering and analog-to-digital conversion on the amplified electrocardiogram signal to generate digital electrocardiogram data; Feature extraction module: Extract the common-mode offset change rate and the inter-lead phase drift value from the digital electrocardiogram data; Interference evaluation and fusion analysis module: Normalize the common-mode offset change rate and the inter-lead phase drift value, and calculate the error severity index ESEI according to the preset weight; Interference level judgment module: Divide the interference degree caused by electrode impedance imbalance into multiple levels according to the value of ESEI, and the levels include slight interference level, moderate interference level and severe interference level; Adaptive signal optimization module: Dynamically adjust the processing strategy of the monitoring system based on the moderate interference level, and the strategy includes adaptively adjusting the digital filtering strength of the electrocardiogram signal according to the change of the adjusted ESEI.

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