A method, system, terminal and storage medium for cardiopulmonary resuscitation assisted monitoring and control.

Abstract

This invention discloses a method, system, terminal, and storage medium for cardiopulmonary resuscitation (CPR) assisted monitoring and control. The method includes: simultaneously acquiring the target user's electrocardiogram (ECG) signal and mechanical compression signal; suppressing interference in the ECG signal based on the mechanical compression signal to obtain a processed ECG signal; generating an ECG rhythm interpretation result based on the processed ECG signal; calculating the signal-to-noise ratio (SNR) based on the processed ECG signal; determining the reliability level of the ECG rhythm interpretation result based on the SNR; generating an ECG rhythm analysis result based on the ECG rhythm interpretation result and the reliability level; acquiring the target user's ventilation parameters; generating a comprehensive CPR quality index based on the ECG rhythm analysis result, ventilation parameters, and compression parameters; and generating adjustment instructions for the CPR assisted monitoring and control device based on the comprehensive CPR quality index. This invention eliminates the need for compression pauses, maintaining continuous chest compressions and avoiding the problem of decreased coronary perfusion pressure due to interruption.

Description

Technical Field

[0001] This invention relates to the field of cardiopulmonary resuscitation (CPR) monitoring and control technology, and in particular to a CPR auxiliary monitoring and control method, system, terminal and storage medium. Background Technology

[0002] During cardiopulmonary resuscitation (CPR), real-time analysis of the patient's electrocardiogram (ECG) signals and accurate interpretation of the ECG rhythm are crucial for determining whether to administer critical interventions such as defibrillation. However, the mechanical movements generated by continuous chest compressions, especially when using mechanical compression devices for rhythmic compressions, can superimpose significant compression artifacts on the ECG signal through the electrode-skin interface and tissue conduction, resulting in severe distortion of the ECG waveform and greatly increasing the difficulty of interpreting the ECG rhythm.

[0003] To suppress such compression interference, existing techniques often involve acquiring electrocardiogram (ECG) signals and performing rhythm analysis during the brief intervals between compression interruptions. However, this method of analyzing ECGs during interrupted compressions sacrifices the continuity of chest compressions and can easily lead to a decrease in coronary perfusion pressure.

[0004] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a cardiopulmonary resuscitation auxiliary monitoring and control method, system, terminal and storage medium to address the above-mentioned defects of the prior art. The aim is to solve the problem that the existing interrupted chest compression analysis method cannot maintain the continuity of chest compressions and is prone to causing a drop in coronary perfusion pressure.

[0006] The technical solution adopted by this invention to solve the problem is as follows: In a first aspect, embodiments of the present invention provide a method for cardiopulmonary resuscitation assisted monitoring and control, the method comprising: Simultaneously acquire the target user's electrocardiogram (ECG) signal and mechanical compression signal, and suppress interference in the ECG signal based on the mechanical compression signal to obtain a processed ECG signal; An electrocardiogram (ECG) rhythm interpretation result is generated based on the processed ECG signal; the signal-to-noise ratio (SNR) is calculated based on the processed ECG signal, and the reliability level of the ECG rhythm interpretation result is determined based on the SNR; an ECG rhythm analysis result is generated based on the ECG rhythm interpretation result and the reliability level. The ventilation parameters of the target user are obtained, and a comprehensive cardiopulmonary resuscitation quality index is generated based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals. Adjustment instructions for the cardiopulmonary resuscitation auxiliary monitoring and control device are generated based on the comprehensive cardiopulmonary resuscitation quality index.

[0007] In one embodiment, the step of dynamically adaptively suppressing interference in the electrocardiogram (ECG) signal based on the mechanical compression signal and generating a processed ECG signal in real time includes: The mechanical pressing signal is subjected to pressing cycle detection and cycle normalization processing, and the most recent normalized cycle waveforms are selected to generate the time domain interference template of the current pressing cycle. The time-domain interference template or the time-delayed aligned mechanical pressing signal is used as a reference signal; The electrocardiogram (ECG) signal is filtered based on the reference signal using an adaptive filter to obtain the processed ECG signal.

[0008] In one embodiment, the step of calculating the signal-to-noise ratio based on the processed electrocardiogram signal and determining the reliability level of the electrocardiogram rhythm interpretation result based on the signal-to-noise ratio includes: The processed electrocardiogram signal is subjected to first-order difference and peak detection to obtain the R wave position; The root mean square value of the signal within the window corresponding to the R-wave position is calculated as the signal energy. The root mean square value of the signal within the TP segment window between two adjacent R waves is calculated as the noise energy. The signal-to-noise ratio (SNR) is calculated based on the signal energy and the noise energy, and the confidence level is determined based on the SNR and several preset SNR threshold ranges.

[0009] In one implementation, the step of determining the reliability level of the ECG rhythm interpretation result based on the signal-to-noise ratio parameter includes: When the confidence level is as low as a certain confidence level, the output signal is unreliable, the compression is maintained, and auxiliary operation suggestions are generated based on historical ECG rhythm interpretation results and the current compression frequency.

[0010] In one embodiment, the step of generating a comprehensive cardiopulmonary resuscitation quality index based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals includes: The electrocardiogram rhythm analysis results, the ventilation parameters, and the chest compression parameters were quantitatively scored respectively. The comprehensive cardiopulmonary resuscitation quality index is generated based on the three types of scores.

[0011] In one embodiment, the compression parameters include one or more of the following: compression depth, compression frequency, chest recoil, and compression interruption time; the ventilation parameters include one or more of the following: tidal volume, ventilation frequency, and inspiratory time.

[0012] In one implementation, the adjustment instructions include one or more of the following: adjusting compression depth, adjusting compression rate, adjusting ventilation volume, and preparing for defibrillation.

[0013] Secondly, embodiments of the present invention also provide a cardiopulmonary resuscitation (CPR) auxiliary monitoring and control system, the system comprising: The signal processing module is used to simultaneously acquire the target user's electrocardiogram (ECG) signal and mechanical compression signal, and to suppress interference in the ECG signal based on the mechanical compression signal to obtain the processed ECG signal. The signal analysis module is used to generate an electrocardiogram (ECG) rhythm interpretation result based on the processed ECG signal; calculate the signal-to-noise ratio (SNR) based on the processed ECG signal; determine the reliability level of the ECG rhythm interpretation result based on the SNR; and generate an ECG rhythm analysis result based on the ECG rhythm interpretation result and the reliability level. The quality analysis module is used to obtain the ventilation parameters of the target user and generate a comprehensive cardiopulmonary resuscitation quality index based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals. The instruction generation module is used to generate adjustment instructions for the cardiopulmonary resuscitation auxiliary monitoring and control device based on the comprehensive cardiopulmonary resuscitation quality index.

[0014] Thirdly, embodiments of the present invention also provide a terminal, the terminal including a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing the cardiopulmonary resuscitation assisted monitoring and control method as described above; the processor is used to execute the programs.

[0015] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having stored thereon a plurality of instructions adapted to be loaded and executed by a processor to implement the steps of the cardiopulmonary resuscitation assisted monitoring and control method as described above.

[0016] The beneficial effects of this invention are as follows: By simultaneously acquiring the target user's electrocardiogram (ECG) signal and mechanical compression signal, the artifacts generated by the compression action are separated and filtered out from the aliased original ECG signal, thereby restoring a relatively clean ECG waveform while compression is performed continuously. The entire analysis process does not require pausing compressions, thus maintaining the continuity of chest compressions and avoiding the problem of decreased coronary perfusion pressure caused by interruption. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the cardiopulmonary resuscitation assisted monitoring and control method provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the cardiopulmonary resuscitation auxiliary monitoring and control system provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the terminal provided in the embodiment of the present invention. Detailed Implementation

[0021] This invention discloses a method, system, terminal, and storage medium for assisting in cardiopulmonary resuscitation (CPR) monitoring and control. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0023] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0024] To address the aforementioned deficiencies in existing technologies, this invention provides a method for cardiopulmonary resuscitation (CPR) assisted monitoring and control. The method includes: simultaneously acquiring the electrocardiogram (ECG) signal and mechanical compression signal of a target user; suppressing interference in the ECG signal based on the mechanical compression signal to obtain a processed ECG signal; generating an ECG rhythm interpretation result based on the processed ECG signal; calculating the signal-to-noise ratio (SNR) based on the processed ECG signal; determining the reliability level of the ECG rhythm interpretation result based on the SNR; generating an ECG rhythm analysis result based on the ECG rhythm interpretation result and the reliability level; acquiring the ventilation parameters of the target user; generating a comprehensive CPR quality index based on the ECG rhythm analysis result, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signal; and generating adjustment instructions for the CPR assisted monitoring and control device based on the comprehensive CPR quality index. This invention, by simultaneously acquiring the target user's ECG signal and mechanical compression signal, separates and filters out artifacts generated by the compression action from the aliased original ECG signal, thereby restoring a relatively clean ECG waveform when compression is performed continuously. The entire analysis process does not require pausing chest compressions, thus maintaining the continuity of chest compressions and avoiding the problem of decreased coronary perfusion pressure due to interruption.

[0025] like Figure 1 As shown, the method includes: Step S100: Simultaneously acquire the target user's electrocardiogram (ECG) signal and mechanical compression signal, and suppress interference in the ECG signal based on the mechanical compression signal to obtain the processed ECG signal.

[0026] In standard CPR procedures, chest compressions are considered crucial for sustaining life. To achieve accurate interpretation of chest compressions without interruption and accurate interpretation of heart rhythm, this embodiment requires simultaneous acquisition of the target user's electrocardiogram (ECG) signal and mechanical compression signal. Artifacts generated by the compression action are separated and filtered out from the aliased raw ECG signal, thereby restoring a relatively clean ECG waveform while compressions are performed continuously. Specifically, on one hand, the raw ECG signal is acquired through surface electrodes, which contains the true ECG waveform and compression artifacts caused by chest compressions, electrode displacement, etc. On the other hand, a mechanical compression signal is simultaneously introduced. This signal can originate from a position sensor or accelerometer built into the compression device, or from pressure and displacement sensors externally placed on the compression pad. This signal can reflect the physical motion parameters of chest compressions in real time, such as the starting point, depth, frequency, and waveform of the compressions.

[0027] After obtaining these two co-originating signals, a dynamic interference model needs to be constructed and interference suppression applied to the original ECG signal. Interference suppression is a dynamic and adaptive process. The system generates a time-domain interference template synchronized with the compression action based on the mechanical compression signal. Subsequently, an adaptive filter is used, with the compression signal as a reference input, to adaptively cancel noise in the ECG signal.

[0028] In short, this embodiment acquires ECG and compression signals in real time during continuous chest compressions, and uses the mechanical compression signal as a reference to dynamically suppress interference in the ECG signal, ultimately obtaining a relatively pure processed ECG signal that can be used for rhythm analysis and decision-making in clinical practice, thus achieving interpretation without stopping compressions.

[0029] In one implementation, the step of suppressing interference in the electrocardiogram (ECG) signal based on the mechanical compression signal to obtain a processed ECG signal includes: The mechanical pressing signal is subjected to pressing cycle detection and cycle normalization processing, and the most recent normalized cycle waveforms are selected to generate the time domain interference template of the current pressing cycle. The time-domain interference template or the time-delayed aligned mechanical pressing signal is used as a reference signal; The electrocardiogram (ECG) signal is filtered based on the reference signal using an adaptive filter to obtain the processed ECG signal.

[0030] Specifically, the mechanical compression signal changes over time, reflecting the real-time displacement curve of the compression head relative to the chest. During cardiac resuscitation (CPR), the compression rate is generally 100 to 120 times per minute, and the compression waveform is approximately a half-cycle sine wave or trapezoidal wave.

[0031] Generate a time-domain interference template by following these steps: (1) Pressing cycle detection: For mechanical pressing signal s press (t) Perform peak detection and extract the start time T of each press. i With the termination time T i+1 This determines the duration d of the i-th pressing cycle. i =T i+1 -T i .

[0032] (2) Period normalization: Since there will be slight differences in the force and speed of each press, the lengths of these single-cycle waveforms are not consistent. Therefore, period normalization is required. All identified single-cycle waveforms are resampled to a uniform and identical length L (e.g., L=200 sampling points) to obtain the normalized single-cycle waveform p. i (n). Where n = 1, 2, ..., L.

[0033] (3) Generate time-domain interference template: Select the normalized waveforms of the most recently completed M pressing cycles (e.g., M is 5), perform median filtering or averaging on them, and thus obtain the time-domain interference template T for the current stage. noise (n), i.e., the noise template function: ; The above formula can be regarded as: T noise (n) is equal to the sum of multiple normalized waveforms p i (n) Take the median. Median filtering is used instead of simple averaging because it can effectively resist occasional irregular pressing or impact artifacts, preventing these occasional anomalies from causing template distortion.

[0034] (4) Dynamically update the time-domain interference template: After each new pressing cycle is completed, the system will normalize it and include it in the calculation pool to update the time-domain interference template, thereby tracking the continuous changes in pressing depth or frequency in real time.

[0035] Next, an adaptive filter (taking the LMS algorithm as an example) is used to remove noise from the mixed ECG signal in the following steps to achieve adaptive noise cancellation based on the compression signal: (1) Determine the input signal: The system has two inputs, of which the main input is the original electrocardiogram signal x(t), which is composed of the real electrocardiogram signal s. ECG (t) (the actual ECG signal function) and the artifacts caused by compression n press The noise function (t) is a linear superposition of x(t) and s(t), i.e., x(t) = s ECG (t)+n press (t); the reference input is the generated time-domain interference template or the time-delayed aligned mechanical pressing signal s. press (t).

[0036] Specifically, regarding time delay alignment: because the compression sensor is attached to the chest wall while the ECG electrodes are attached to the body surface, their different physical locations and conductive media cause a lag between the appearance of pressure artifacts in the ECG signal and the time the sensor records the compression action. The system needs to align the mechanical compression signal s... press (t) Delay compensation is performed. Specifically, the original electrocardiogram signal x(t) and the mechanical compression signal s are calculated. press The cross-correlation function of (t) is used to determine the optimal time delay τ by finding the time delay corresponding to the maximum value of the cross-correlation function. This yields the time-aligned reference signal r(t), denoted as r(t=s). press (t-τ).

[0037] (2) LMS adaptive filtering: The system sets up a transverse filter with an order of K (usually 8 to 16 sampling points), and its dynamically adjustable weight vector is denoted as W(t) (i.e. filter weight vector).

[0038] The filter output is an estimate of the pressing noise at the current moment: ; Among them, r(t)=[r(t),r(t-1),...,r(t-K+1)] T That is, the output of the filter is determined based on the transpose of the weight vector and the reference input (including the reference input at the current time and the past K-1 time steps).

[0039] The system then subtracts the filter output from the main input x(t). Thus, the error signal e(t) is calculated, i.e. .

[0040] If the noise estimation is accurate, the error signal should approximate a clean ECG signal. The role of the LMS algorithm is to continuously and automatically adjust the weight vector, causing it to evolve in the direction that minimizes the energy of the error signal.

[0041] The LMS weight update formula is: ; In the formula, μ is the step size factor (between 0.01 and 0.1, automatically adjusted according to the pressing frequency). The above formula can be viewed as: the weight vector at the next moment is equal to the current weight vector plus a correction term, which is determined by the step size factor, the current error, and the current reference input. Through multiple learning and adjustments, the filter can automatically establish a dynamic mapping relationship between the reference input and the actual mixed pressing artifacts in the main input.

[0042] (3) Output signal: error signal e(t), i.e., the ECG signal after suppressing compression artifacts. When the pressing action is regular, the filter can quickly converge to a stable state, making the QRS complex in the output signal clear and distinct, and the P wave and T wave also identifiable. Even if the pressing is interrupted for some reason or the pressing frequency changes suddenly, the filter weights are able to reconverge within 2 to 3 pressing cycles, quickly adapt to the new interference mode, and achieve continuous pressing interpretation.

[0043] Step S200: Generate an electrocardiogram (ECG) rhythm interpretation result based on the processed ECG signal; calculate the signal-to-noise ratio (SNR) based on the processed ECG signal; determine the reliability level of the ECG rhythm interpretation result based on the SNR; and generate an ECG rhythm analysis result based on the ECG rhythm interpretation result and the reliability level.

[0044] Specifically, a preliminary ECG rhythm interpretation result is generated based on the processed ECG signal, such as determining it to be sinus rhythm, ventricular tachycardia, or ventricular fibrillation. Simultaneously, cardiac characteristics are extracted from the processed ECG signal, and the signal-to-noise ratio (SNR) and / or the reliability level of the ECG rhythm interpretation result are calculated in real time. The specific value of the reliability level reflects the clarity of the waveform and the reliability of the interpretation.

[0045] In one implementation, the step of calculating the signal-to-noise ratio based on the processed electrocardiogram signal and determining the reliability level of the electrocardiogram rhythm interpretation result based on the signal-to-noise ratio includes: The processed electrocardiogram signal is subjected to first-order difference and peak detection to obtain the R wave position; The root mean square value of the signal within the window corresponding to the R-wave position is calculated as the signal energy. The root mean square value of the signal within the TP segment window between two adjacent R waves is calculated as the noise energy. The signal-to-noise ratio (SNR) is calculated based on the signal energy and the noise energy, and the confidence level is determined based on the SNR and several preset SNR threshold ranges.

[0046] Furthermore, the step of determining the reliability level of the ECG rhythm interpretation result based on the signal-to-noise ratio parameter includes: When the confidence level is as low as a certain confidence level, the output signal is unreliable, the compression is maintained, and auxiliary operation suggestions are generated based on historical ECG rhythm interpretation results and the current compression frequency.

[0047] Specifically, since a clean noise reference cannot be obtained under continuous pressing conditions, a signal-to-noise ratio (SNR) estimation method based on QRS group amplitude is used for real-time calculation.

[0048] The first step is to detect potential QRS complexes: This involves analyzing the processed ECG signal. First-order difference and peak detection are performed to mark the candidate R-wave locations. k .

[0049] The second step is to calculate the signal energy: using each identified R-wave position as a reference, a window is opened around the R-wave (e.g., from 50ms before the R-wave to 150ms after the R-wave), and the root mean square value of the signal within this window is calculated to obtain the current signal energy. .

[0050] The third step is to calculate the noise energy: In the electrical activity cycle of the heart, there is a segment between two adjacent R waves called the TP segment (the interval from the end of the T wave to the beginning of the next P wave on the electrocardiogram). A window is opened in the TP segment between two consecutive R waves (usually considered to have no significant cardiac electrical activity), and the root mean square value within this window is calculated as the background noise power, thus obtaining the noise energy. .

[0051] The fourth step is the calculation of the signal-to-noise ratio (SNR): The system substitutes the signal energy and noise energy obtained above into the logarithmic ratio formula, and finally outputs the SNR value in decibels. The specific formula is as follows: ; To prevent the denominator from being zero, a very small positive number is added. .

[0052] Furthermore, it should be noted that for certain arrhythmias where a clear R wave cannot be detected, such as when the ECG waveform during ventricular fibrillation presents as irregular fibrillation waves without a regular QRS complex, the above-mentioned method based on the R wave window will no longer be applicable. In this case, an alternative is to use the power spectral density ratio to estimate the signal-to-noise ratio (SNR). This involves dividing the signal into two frequency bands: one is the 5-30 Hz band, which primarily carries ECG energy, and the other is the 0.5-5 Hz band, which mainly contains compression artifacts and baseline drift energy. The ratio of the total energy within these two frequency bands is then used as a substitute estimate for the SNR.

[0053] After obtaining objective and quantitative signal-to-noise ratio values, credibility grading can be performed, and the credibility level of the ECG rhythm interpretation result of this signal segment can be determined based on multiple preset signal-to-noise ratio threshold ranges.

[0054] The output strategy for SNR range (dB) confidence level is as follows: (1) When the signal-to-noise ratio is greater than or equal to 20 dB, the credibility is marked as "high". At this time, the system determines that the signal quality is pure and can directly output specific sinus rhythm, ventricular tachycardia or ventricular fibrillation rhythm analysis results without any warnings. (2) When the signal-to-noise ratio falls between 15 and 20 dB, the reliability is marked as "medium". The system will display the prompt "Reliability is medium, it is recommended to combine other parameters for judgment" while outputting the ECG rhythm interpretation result; (3) When the signal-to-noise ratio drops to the range of 10 to 15 dB, the credibility is marked as "low". At this time, the waveform recognition has deteriorated significantly. The system will generate a "uncertain" or "suspicious" heart rhythm conclusion and give a clear text prompt: "The pressure interference is large. Do not interrupt the pressure unless necessary. You can refer to the auxiliary algorithm results."

[0055] (4) When the signal-to-noise ratio is below 10 dB, the reliability is marked as "extremely low". At this time, no specific heart rhythm classification is output, only "interference" or "signal is unreliable, continue pressing" is output. At the same time, auxiliary suggestions are given based on historical data (the last 3 reliable readings before the interference) and the current pressing frequency (e.g. "still suggests a high probability of ventricular fibrillation, it is recommended to prepare for defibrillation").

[0056] It should be noted that the confidence threshold used in this embodiment is configurable and can be adjusted by clinicians through the monitoring interface.

[0057] Step S300: Obtain the ventilation parameters of the target user, and generate a comprehensive cardiopulmonary resuscitation quality index based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals.

[0058] Specifically, this embodiment treats the electrocardiogram rhythm analysis results, compression parameters, and ventilation volume as three interrelated data points, collectively constructing an intuitive and quantifiable cardiopulmonary resuscitation quality index (CPR-QI), with a range of 0 to 100 points. Compression parameters include one or more of the following: compression depth, compression rate, chest recoil, and compression interruption time. Ventilation parameters include one or more of the following: tidal volume, ventilation rate, and inspiratory time.

[0059] In one implementation, the step of generating a comprehensive cardiopulmonary resuscitation quality index based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals includes: The electrocardiogram rhythm analysis results, the ventilation parameters, and the chest compression parameters were quantitatively scored respectively. The comprehensive cardiopulmonary resuscitation quality index is generated based on the three types of scores.

[0060] Specifically, the system will score the parameters of three different types independently.

[0061] The first category is the quantitative scoring of the results of electrocardiogram rhythm analysis. The scoring of this type of parameter does not use a bias deduction system, but directly assigns different base scores based on the response characteristics of the rhythm type to defibrillation.

[0062] For example, regarding the results of electrocardiogram rhythm analysis: Shockable rhythms: ventricular fibrillation (VF) and pulseless ventricular tachycardia (VT) are assigned a score of +30 (baseline score); Unshockable rhythms: cardiac arrest (Asystole) and pulseless electrical activity (PEA) are assigned a value of +10. Resumption of Automated Cycling (ROSC) signs: The presence of regular QRS clusters with a frequency >60 bpm triggers the "Terminate CPR" recommendation and excludes it from quality scoring.

[0063] The second category is the quantitative scoring of the pressure parameters. The scoring of these parameters follows the principle of starting with a full score and deducting points for deviations. The deduction is calculated based on the deviation from the preset standard value.

[0064] For example, regarding the pressure parameter (assessed every 10 seconds), the scoring strategy for parameter compliance is as follows: For every 0.5cm deviation from the standard in pressing depth of 5 to 6cm, deduct 2 points; base score is 10 points.

[0065] The pressing frequency is 100 to 120 times per minute. For every 5 times the frequency deviates from the target, 1 point will be deducted; the base score is 10 points.

[0066] If the residual pressure is >3cmH2O after each chest recoil, 1 point will be deducted each time, up to a maximum deduction of 5 points. Any interruption lasting more than 2 seconds will result in a deduction of 2 points for each second of interruption, up to a maximum deduction of 15 points. The pressing section is worth 30 points, S press Score = 30 - Total deductions (minimum 0 points).

[0067] The third category is the quantitative scoring of ventilation parameters, which also uses a deviation deduction system. The ventilation parameter scores are calculated based on their deviation from preset standard values.

[0068] For example, the scoring strategy for ventilation parameters (assessed every 30 seconds) is as follows: Tidal volume (adult) is 400 to 600 ml. For every 50 ml deviation, deduct 1 point. Base score is 10 points. For each ventilation rate exceeding or falling below 8 to 10 breaths per minute, 2 points will be deducted, with a base score of 10 points. For each 0.2-second deviation from the 1-1.5 second inhalation time, deduct 1 point; base score is 5 points. Airway peak pressure <40cmH2O exceeding the limit will result in a deduction of 2 points; The ventilation section is worth 25 points. vent Score = 25 - Total deductions.

[0069] After independently quantifying and scoring the three types of parameters, a comprehensive score is calculated. The formula for calculating the Comprehensive Cardiopulmonary Resuscitation Quality Index (CPR-QI) is to directly sum the scores of the three types, with an optional bonus score added. That is, CPR-QI equals the compression parameter score plus the ventilation parameter score plus the electrocardiogram (ECG) rhythm score. The ECG rhythm score has the highest weight, followed by the compression parameter score, and then the ventilation parameter score. If the compression-to-ventilation ratio is performed exactly according to the recommended 30:2 ratio without interruption, the system adds an extra 5 points as a bonus score to encourage high-quality coordinated CPR.

[0070] For example, the comprehensive scoring model is established as follows: CPR-QI=S press Score +S vent Score +S rhythm Score; Among them, S rhythm Scoring: If the rhythm is shockable, the Srhythm score is 40; if the rhythm is not shockable, the Srhythm score is 20 (Note: Maximum 100 points, of which 40 are weighted by rhythm, 30 by compression, 25 by ventilation, and the remaining 5 points are for coordination bonus points); Coordination bonus points (5 points): When compressions and ventilations are performed in a 30:2 ratio without interruption, 5 points are added to encourage high coordination.

[0071] Step S400: Generate adjustment instructions for the cardiopulmonary resuscitation auxiliary monitoring and control device based on the comprehensive cardiopulmonary resuscitation quality index.

[0072] Furthermore, the adjustment instructions include one or more of the following: adjusting compression depth, adjusting compression frequency, adjusting ventilation volume, and preparing for defibrillation.

[0073] Specifically, the system ultimately needs to provide corresponding adjustment instructions based on the comprehensive cardiopulmonary resuscitation quality index (CPR quality index) when compressions and ventilations are out of sync. In practical applications, the system acquires ventilation signals (such as from flow rate sensors or changes in chest impedance), combines them with compression signals and electrocardiogram (ECG) signals to determine whether the current compression-to-ventilation ratio matches the preset physiological parameter targets, and then calculates the comprehensive CPR quality index. When the comprehensive CPR quality index score is low or a certain indicator deviates, the system automatically triggers feedback and generates adjustment instructions.

[0074] For example, the logic for generating adjustment instructions is as follows: If the pressing depth is insufficient, the message "Please increase the pressing depth to at least 5 centimeters" will be displayed. If the pressing frequency is too fast, the message "Slow down the pressing speed and control it to about 110 times / minute" will be displayed. If the chest cavity does not fully recoil, output "Let the chest cavity fully recoil after each compression"; If the interruption time is too long, output "Minimize press interruptions and maintain continuity"; When tidal volume is too high, the instruction is "reduce ventilation to avoid bloating"; If the tidal volume is too low, output "increase the tidal volume to about 500 ml"; If the ventilation rate is too fast, output "Slow down the ventilation rate, ventilate once every 6 seconds".

[0075] For shockable rhythms: When the rhythm is shockable and the overall cardiopulmonary resuscitation (CPR) quality index is high (e.g., greater than 70), it indicates that the CPR operation is in place, and the system indicates "good CPR quality, prepare for defibrillation"; conversely, when the rhythm is shockable but the CPR quality index is low (e.g., less than 50), it indicates that there are serious problems with compressions or ventilation. In this case, the success rate of blind defibrillation is extremely low, and the system will prevent or delay the action and prompt "improve the quality of compressions and ventilation before defibrillation".

[0076] It should be noted that the feedback adjustment mechanism in this embodiment also has dynamic target setting capability, that is, the system will set a passing grade for the comprehensive cardiopulmonary resuscitation quality index. If the current comprehensive cardiopulmonary resuscitation quality index is lower than the preset threshold (e.g., 60 points), the system will automatically identify the item with the lowest score and the most deductions every assessment cycle (e.g., 30 seconds) and prioritize outputting improvement instructions for that item.

[0077] Based on the above embodiments, the present invention also provides a cardiopulmonary resuscitation (CPR) assisted monitoring and control system, such as... Figure 2 As shown, the system includes: The signal processing module is used to simultaneously acquire the target user's electrocardiogram (ECG) signal and mechanical compression signal, and to suppress interference in the ECG signal based on the mechanical compression signal to obtain the processed ECG signal. The signal analysis module is used to generate an electrocardiogram (ECG) rhythm interpretation result based on the processed ECG signal; calculate the signal-to-noise ratio (SNR) based on the processed ECG signal; determine the reliability level of the ECG rhythm interpretation result based on the SNR; and generate an ECG rhythm analysis result based on the ECG rhythm interpretation result and the reliability level. The quality analysis module is used to obtain the ventilation parameters of the target user and generate a comprehensive cardiopulmonary resuscitation quality index based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals. The instruction generation module is used to generate adjustment instructions for the cardiopulmonary resuscitation auxiliary monitoring and control device based on the comprehensive cardiopulmonary resuscitation quality index.

[0078] The signal processing module includes: an electrocardiogram acquisition module for acquiring surface electrocardiogram signals; and a compression sensing module for real-time detection of the mechanical motion parameters of chest compressions, which include at least compression displacement, velocity, or acceleration.

[0079] The system also includes a ventilation detection module for acquiring ventilation or moisture data.

[0080] The system functions include: receiving the electrocardiogram signal and mechanical motion parameters, and calculating the compression-ventilation coordination index (i.e., the cardiopulmonary resuscitation quality index (CPR-QI)) based on the data from the ventilation detection module.

[0081] The system also includes an output module for displaying processed electrocardiogram waveforms, rhythm analysis results, and compression-ventilation coordination status during continuous compression.

[0082] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 3As shown, the terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a cardiopulmonary resuscitation (CPR) assisted monitoring and control method. The display screen can be an LCD screen or an e-ink screen.

[0083] Those skilled in the art will understand that Figure 3 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0084] In one implementation, the terminal's memory stores one or more programs, and these programs are configured to be executed by one or more processors, and the programs contain instructions for performing cardiopulmonary resuscitation assisted monitoring and control methods.

[0085] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0086] In summary, this invention discloses a method, system, terminal, and storage medium for cardiopulmonary resuscitation (CPR) assisted monitoring and control. The method includes: simultaneously acquiring the target user's electrocardiogram (ECG) signal and mechanical compression signal; suppressing interference in the ECG signal based on the mechanical compression signal to obtain a processed ECG signal; generating an ECG rhythm interpretation result based on the processed ECG signal; calculating the signal-to-noise ratio (SNR) based on the processed ECG signal; determining the reliability level of the ECG rhythm interpretation result based on the SNR; generating an ECG rhythm analysis result based on the ECG rhythm interpretation result and the reliability level; acquiring the target user's ventilation parameters; generating a comprehensive CPR quality index based on the ECG rhythm analysis result, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signal; and generating adjustment instructions for the CPR assisted monitoring and control device based on the comprehensive CPR quality index. This invention, by simultaneously acquiring the target user's ECG signal and mechanical compression signal, separates and filters out artifacts generated by the compression action from the aliased original ECG signal, thereby restoring a relatively clean ECG waveform when compression is performed continuously. The entire analysis process does not require pausing chest compressions, thus maintaining the continuity of chest compressions and avoiding the problem of decreased coronary perfusion pressure due to interruption.

[0087] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for assisting in cardiopulmonary resuscitation monitoring and control, characterized in that, The method includes: Simultaneously acquire the target user's electrocardiogram (ECG) signal and mechanical compression signal, and suppress interference in the ECG signal based on the mechanical compression signal to obtain a processed ECG signal; An electrocardiogram (ECG) rhythm interpretation result is generated based on the processed ECG signal; the signal-to-noise ratio (SNR) is calculated based on the processed ECG signal, and the reliability level of the ECG rhythm interpretation result is determined based on the SNR; an ECG rhythm analysis result is generated based on the ECG rhythm interpretation result and the reliability level. The ventilation parameters of the target user are obtained, and a comprehensive cardiopulmonary resuscitation quality index is generated based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals. Adjustment instructions for the cardiopulmonary resuscitation auxiliary monitoring and control device are generated based on the comprehensive cardiopulmonary resuscitation quality index.

2. The cardiopulmonary resuscitation assisted monitoring and control method according to claim 1, characterized in that, The steps of dynamically and adaptively suppressing interference in the electrocardiogram (ECG) signal based on the mechanical compression signal to generate a processed ECG signal in real time include: The mechanical pressing signal is subjected to pressing cycle detection and cycle normalization processing, and the most recent normalized cycle waveforms are selected to generate the time domain interference template of the current pressing cycle. The time-domain interference template or the time-delayed aligned mechanical pressing signal is used as a reference signal; The electrocardiogram (ECG) signal is filtered based on the reference signal using an adaptive filter to obtain the processed ECG signal.

3. The cardiopulmonary resuscitation assisted monitoring and control method according to claim 1, characterized in that, The steps of calculating the signal-to-noise ratio based on the processed ECG signal and determining the reliability level of the ECG rhythm interpretation result based on the signal-to-noise ratio include: The processed electrocardiogram signal is subjected to first-order difference and peak detection to obtain the R wave position; The root mean square value of the signal within the window corresponding to the R-wave position is calculated as the signal energy. The root mean square value of the signal within the TP segment window between two adjacent R waves is calculated as the noise energy. The signal-to-noise ratio (SNR) is calculated based on the signal energy and the noise energy, and the confidence level is determined based on the SNR and several preset SNR threshold ranges.

4. The cardiopulmonary resuscitation assisted monitoring and control method according to claim 3, characterized in that, The steps for determining the reliability level of the ECG rhythm interpretation result based on the signal-to-noise ratio parameter include: When the confidence level is as low as a certain confidence level, the output signal is unreliable, the compression is maintained, and auxiliary operation suggestions are generated based on historical ECG rhythm interpretation results and the current compression frequency.

5. The cardiopulmonary resuscitation assisted monitoring and control method according to claim 1, characterized in that, The steps for generating a comprehensive cardiopulmonary resuscitation quality index based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals include: The electrocardiogram rhythm analysis results, the ventilation parameters, and the chest compression parameters were quantitatively scored respectively. The comprehensive cardiopulmonary resuscitation quality index is generated based on the three types of scores.

6. The cardiopulmonary resuscitation assisted monitoring and control method according to claim 5, characterized in that, The compression parameters include one or more of the following: compression depth, compression frequency, chest recoil, and compression interruption time; the ventilation parameters include one or more of the following: tidal volume, ventilation frequency, and inspiratory time.

7. The cardiopulmonary resuscitation assisted monitoring and control method according to claim 1, characterized in that, The adjustment commands include one or more of the following: adjusting compression depth, adjusting compression rate, adjusting ventilation volume, and preparing for defibrillation.

8. A cardiopulmonary resuscitation (CPR) auxiliary monitoring and control system, characterized in that, The system includes: The signal processing module is used to simultaneously acquire the target user's electrocardiogram (ECG) signal and mechanical compression signal, and to suppress interference in the ECG signal based on the mechanical compression signal to obtain a processed ECG signal. The signal analysis module is used to generate an electrocardiogram (ECG) rhythm interpretation result based on the processed ECG signal; calculate the signal-to-noise ratio (SNR) based on the processed ECG signal; determine the reliability level of the ECG rhythm interpretation result based on the SNR; and generate an ECG rhythm analysis result based on the ECG rhythm interpretation result and the reliability level. The quality analysis module is used to obtain the ventilation parameters of the target user and generate a comprehensive cardiopulmonary resuscitation quality index based on the electrocardiogram rhythm analysis results, the ventilation parameters, and the compression parameters corresponding to the mechanical compression signals. The instruction generation module is used to generate adjustment instructions for the cardiopulmonary resuscitation auxiliary monitoring and control device based on the comprehensive cardiopulmonary resuscitation quality index.

9. A terminal, characterized in that, The terminal includes a memory and one or more processors; the memory stores one or more programs; the programs contain instructions for executing the cardiopulmonary resuscitation assisted monitoring and control method as described in any one of claims 1 to 7; the processor is used to execute the programs.

10. A computer-readable storage medium storing a plurality of instructions thereon, characterized in that, The instructions are applicable to be loaded and executed by a processor to implement the steps of the cardiopulmonary resuscitation assisted monitoring and control method as described in any one of claims 1 to 7.

Images