An electrocardiogram signal processing system and a defibrillator

Abstract

This application belongs to the field of medical device technology, and particularly relates to an electrocardiogram (ECG) signal processing system and a defibrillator. The ECG signal processing system includes an ECG signal front-end processing circuit and a microcontroller capable of executing an ECG signal analysis and recognition algorithm. The ECG signal analysis and recognition algorithm first determines whether there are interference signals or other non-ECG signals in the ECG signal to be processed; if not, it filters the ECG signal to obtain a first ECG signal; it then shapes the first ECG signal to obtain a second ECG signal; and based on a current dynamic threshold and a current mean threshold, it obtains the QRS wave recognition result in the second ECG signal. Based on the aforementioned ECG signal processing system, the QRS wave in the ECG signal can be accurately identified, thereby greatly improving the accuracy of non-defibrillation rhythm recognition by the AED, and thus improving the safety and reliability of the AED device.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to an electrocardiogram signal processing system and a defibrillator. Background Technology

[0002] An AED (Automated External Defibrillator) uses an electrical current to stimulate the heart to contract and relax synchronously, thus restoring a normal heart rhythm in patients with ventricular fibrillation and ventricular tachycardia.

[0003] The electrocardiogram (ECG) signal processing unit inside an AED is a crucial functional component. Its primary function is heart rhythm recognition. AED regulations require an accuracy rate of at least 99% for non-defibrillation heart rates, at least 95% for ventricular fibrillation, and at least 75% for ventricular tachycardia.

[0004] An important characteristic of non-defibrillated heart rate is that the signal has a QRS wave; however, because the QRS waveform changes are very complex in the electrocardiogram signals of various non-defibrillated arrhythmias, accurately identifying non-defibrillated heart rhythms is not a simple task. Summary of the Invention

[0005] To overcome the problem of accurate identification caused by large waveform variations in non-defibrillation arrhythmias in related technologies, the microcontroller in the ECG signal processing system of this application can run a novel, fast, real-time ECG signal analysis and identification algorithm, which has achieved a recognition rate of over 99.8% in clinical and database tests. The improved accuracy of non-defibrillation rhythm identification enhances the safety and reliability of AEDs. The fast, real-time operation with low computational load significantly reduces the computational power and resource requirements of defibrillation devices such as AEDs using this method, allowing for a substantial reduction in circuit size and power consumption, facilitating the miniaturization and portability of AEDs.

[0006] The first aspect of this application provides an electrocardiogram (ECG) signal processing system, comprising:

[0007] An electrocardiogram (ECG) signal acquisition circuit, and a microcontroller connected to the ECG signal acquisition circuit capable of executing an ECG signal analysis and recognition algorithm;

[0008] The electrocardiogram (ECG) signal acquisition circuit is used to acquire the ECG signal to be processed and transmit it to the microcontroller.

[0009] The electrocardiogram (ECG) signal analysis and recognition algorithm in the microcontroller is used to: receive the ECG signal to be processed and determine whether there is an interference signal in the ECG signal to be processed; if there is no interference signal, then perform a filtering operation on the ECG signal to be processed to obtain a first ECG signal.

[0010] The first electrocardiogram (ECG) signal is shaped to obtain the second ECG signal;

[0011] Based on the current dynamic threshold and the current mean threshold, the QRS wave identification result in the second electrocardiogram signal is obtained; the current dynamic threshold is obtained based on the R wave peak value of the second electrocardiogram signal and the historical dynamic threshold, and the current mean threshold is obtained based on the signal mean of the second electrocardiogram signal.

[0012] In some embodiments, the electrocardiogram (ECG) signal analysis algorithm is further used to: calculate the current heart rate based on the second ECG signal after obtaining the second ECG signal.

[0013] In some embodiments, the ECG signal analysis algorithm is further configured to trigger a timer if the interference signal is present, so that when the timer expires, the step of receiving the ECG signal to be processed is performed.

[0014] In some embodiments, the electrocardiogram signal analysis algorithm is further used for:

[0015] The process of obtaining the QRS wave identification result in the second electrocardiogram signal based on the current dynamic threshold and the current mean threshold includes:

[0016] Based on the current dynamic threshold, the second electrocardiogram signal is identified to obtain the first QRS identification result;

[0017] The second electrocardiogram signal is identified based on the current mean threshold, and the second QRS identification result is obtained.

[0018] If the first R-wave peak time point in the first QRS identification result matches the second R-wave peak time point in the second QRS identification result, then the QRS group is determined at least based on the first QRS identification result.

[0019] In some embodiments, the electrocardiogram signal analysis algorithm is further used for:

[0020] If the first R-wave peak time point in the first QRS identification result does not match the second R-wave peak time point in the second QRS identification result, then the correct result in the first QRS identification result and the second QRS identification result is determined based on the historical identification results, and the QRS group is determined based on the correct result.

[0021] In some embodiments, the calculation process of the current dynamic threshold in the electrocardiogram signal analysis algorithm includes:

[0022] Determine whether the target data in the second electrocardiogram signal is in the sampling hold period or the exponential decay period;

[0023] If the target data is within the sampling and holding period, the maximum value of the current dynamic threshold is updated based on the signal value of the target data;

[0024] If the target data is in the exponential decay period, the current dynamic threshold is calculated based on the decay period calculation formula.

[0025] In some embodiments, the formula for calculating the decay period specifically includes:

[0026] V Ceof (i)=V RMax (0)*exp(-B adj *i*e Coed / T lhr );

[0027] Wherein, the V Ceof The current dynamic threshold is calculated in real time, where i is the count value and V is the value of the current value. RMax (0) represents the maximum R-wave value retained in the previous sampling and holding period of the current exponential decay period; exp is an exponential function, and B... adj The T value is an empirical value. lhr This is the lowest heart rate cycle time.

[0028] In some embodiments, the filtering operation on the ECG signal to be processed in the ECG signal analysis algorithm is low-pass filtering followed by differential filtering.

[0029] In some embodiments, the shaping of the first electrocardiogram signal in the electrocardiogram signal analysis algorithm involves performing a low-pass filter on the first electrocardiogram signal followed by a nonlinear amplification operation.

[0030] A second aspect of this application provides a defibrillator, comprising: a housing, electrode circuitry, and an electrocardiogram signal processing system as described above connected to the electrode circuitry.

[0031] The technical solution provided in this application can include the following beneficial effects: it can accurately identify non-defibrillation rhythms based on QRS waves, and the improved accuracy of non-defibrillation rhythm identification can improve the safety and reliability of AEDs; the characteristics of fast real-time computation and small amount of computation make the defibrillation devices such as AEDs using this method greatly reduce the computing power and resource requirements, which can greatly reduce the size of the implemented circuit and reduce power consumption, making it easier to achieve the miniaturization and portability of AEDs.

[0032] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0033] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0034] Figure 1 This is a schematic diagram of the structure of an electrocardiogram signal processing system according to an embodiment of this application.

[0035] Figure 2 This is a schematic diagram illustrating the process by which a microcontroller obtains the QRS wave recognition result in an embodiment of this application.

[0036] Figure 3 This is a schematic diagram illustrating a filtering effect in a real-world scenario, as shown in an embodiment of this application.

[0037] Figure 4 This is a schematic diagram illustrating another filtering effect in a real-world scenario as shown in the embodiments of this application.

[0038] Figure 5 This is a schematic diagram illustrating another filtering effect in a real-world scenario as shown in the embodiments of this application.

[0039] Figure 6 This is a schematic diagram of the defibrillator structure shown in the embodiments of this application. Detailed Implementation

[0040] Preferred embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0041] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0042] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0043] This application applies to the field of medical device technology, particularly to automated external defibrillators (AEDs). These devices need to accurately identify heart rate and must at least meet the following requirements:

[0044] Accurately identify various types of ventricular fibrillation and ventricular tachycardia that require defibrillation;

[0045] Accurately identify other normal and abnormal heart rhythms that do not require defibrillation;

[0046] In fully automated external defibrillators, it is necessary to identify atrial fibrillation and the abnormal R wave of ventricular tachycardia.

[0047] The early warning algorithm needs to identify various types of ventricular flutter that can induce ventricular tachycardia and ventricular fibrillation, as well as earlier abnormal ventricular contractions.

[0048] Furthermore, since most heart rhythms that do not require defibrillation contain R waves or QRS waves formed by normal ventricular contractions, the most basic heart rhythm recognition algorithm, in general, only needs to accurately identify the presence of R waves in the electrocardiogram signal to mean that the heart rhythm does not require defibrillation. Therefore, real-time and accurate identification of QRS waves is a crucial aspect of the ECG recognition algorithm for AEDs.

[0049] For AED devices, an internationally recognized hard standard for non-defibrillation rhythm recognition is a recognition rate of no less than 99%. Therefore, accurate QRS wave recognition is of great importance for AED devices.

[0050] In related technologies, methods such as R-wave peak detection and R-wave trajectory length methods can be used to identify QRS complexes. However, the applicant has found that normal electrocardiogram (ECG) signals are very regular, with small variations in signal amplitude and various characteristic values, and good consistency in time and amplitude within each cardiac cycle. Therefore, they are easy to identify. However, the PQRST wave in abnormal ECG signals exhibits various variations. In each cycle of the ECG signal, the time and amplitude characteristic values ​​may differ from normal values, and these differences may also change with cardiac physiology. Even more irregularly, during actual ECG signal acquisition, interference from power line noise, electromyography, limb movement, heart rate and characteristic value variations caused by changes in body position, ECG baseline drift caused by polarization voltage mutations, respiratory interference, etc., is introduced. The duration and amplitude changes caused by these interferences are unknown and have no regularity.

[0051] Based on this, this application provides an electrocardiogram signal processing system and a defibrillator that can identify and process interference and noise signals, and make QRS identification unaffected by interference signals. It can also quickly restore the normal detection function of data processing, so as not to miss the identification of every correct QRS wave. It can accurately and quickly output the identification results of QRS waves with an identification accuracy rate of more than 99.9%.

[0052] The embodiments of this application will be described in detail below.

[0053] See Figure 1 , Figure 2 , Figure 1 This is a schematic diagram of the structure of an electrocardiogram signal processing system according to an embodiment of this application. Figure 2 This is a schematic diagram illustrating the process by which a microcontroller obtains the QRS wave recognition result in an embodiment of this application.

[0054] An embodiment of this application illustrates an electrocardiogram (ECG) signal processing system 10, comprising:

[0055] The system includes an electrocardiogram (ECG) signal acquisition circuit 1 and a microcontroller 2 connected to the ECG signal acquisition circuit; the microcontroller 2 can store and execute ECG signal analysis and recognition algorithms.

[0056] The electrocardiogram (ECG) signal acquisition circuit is used to acquire the ECG signal to be processed and transmit it to the microcontroller.

[0057] The microcontroller 2's central electrical signal analysis and recognition algorithm may include the following steps: Microcontroller:

[0058] S100: Receive the ECG signal to be processed and determine whether there is an interference signal in the ECG signal to be processed;

[0059] S200. If the interference signal does not exist, the ECG signal to be processed is filtered to obtain the first ECG signal.

[0060] S300: Shape the first ECG signal to obtain the second ECG signal;

[0061] S400. Based on the current dynamic threshold and the current mean threshold, the QRS wave identification result in the second electrocardiogram signal is obtained; the current dynamic threshold is obtained based on the R wave peak value of the second electrocardiogram signal and the historical dynamic threshold, and the current mean threshold is obtained based on the signal mean of the second electrocardiogram signal.

[0062] In this embodiment, the ECG signal acquisition circuit is electrically connected to a microcontroller. The microcontroller can be a low-cost, low-power MCU with limited computing power. The ECG signal acquisition circuit can be connected to external circuits to receive the ECG signal to be processed, such as other data sensors, as long as it can receive the ECG signal. In practical use, it can be connected to the electrode acquisition circuit in an AED. The microcontroller 2 can be connected to other circuits, such as execution circuits. It can also be connected to other circuits that require processing to obtain the QRS wave recognition result; these connections will not be elaborated further.

[0063] It is understandable that the ECG signal to be processed can be obtained in real time through the electrode acquisition circuitry in the AED. The ECG signal to be processed can include data related to a heartbeat, which can be set according to actual needs. The heart rate range is 30 to 240 beats per minute, which is a maximum of one heartbeat every 2 seconds and a minimum of 0.25 seconds, or 4 heartbeats per second.

[0064] In this embodiment of the application, the electrocardiogram signal to be processed may contain various interference signals, which can be divided into two categories: catastrophic and filterable.

[0065] Based on this, the single-chip microcomputer ECG signal analysis and identification algorithm described in this application embodiment can also be used to: if the interference signal exists, trigger a timer to start timing, so that when the timing ends, the step of receiving the ECG signal to be processed is executed.

[0066] When there are catastrophic interference signals in the ECG signal to be processed, the data processing method is not suitable for operation. Forcing the operation may lead to incorrect conclusions and could result in fatal consequences.

[0067] Among them, catastrophic interference signals are not normal, naturally occurring physiological signals. They can generally be defined and identified by the range of signal amplitude and duration. However, when these catastrophic interferences are present, the data processing method can be reset to a stopped analysis state.

[0068] It is understandable that patient limb movement signals, interference during electrode application, and sudden level changes caused by other people touching the patient can all cause catastrophic interference to the ECG signal. During this period, the ECG signal is submerged in interference that cannot be filtered out, so the analysis must be stopped.

[0069] Possible methods include: calculating the signal peak-to-peak value, baseline fluctuation value, and signal slope. If the value exceeds the normal range, a 2-second retriggered software timer is triggered. Once the timer is set, the subsequent data processing method will be reset and the analysis will stop; it will not be executed again until the timer is reset after a set time.

[0070] Another type of filterable interference signal is simply unwanted and useless signals mixed in with the ECG signal to be processed. These signals will not cause the data processing method to crash, but they will affect the accuracy of analysis, such as QRS complexes. Therefore, in this embodiment, a non-ECG signal detection method is provided, so that the data processing method only analyzes valid data, such as human ECG signals.

[0071] Based on this, the microcontroller includes a first low-pass filter module and a differential filter module; therefore, the filtering operation on the ECG signal to be processed is low-pass filtering followed by differential filtering.

[0072] The low-pass filter module can configure a low-pass filter with a cutoff frequency of 35Hz to filter out high-frequency noise in the QRS complex, improving the time stability of subsequent QRS group identification. It can also achieve signal amplitude normalization. The low-pass filter can be implemented in software.

[0073] The differential filtering module can use a second-order differential filter with a time interval of 0.04s (the interval of the R wave) to filter out baseline drift interference and low-frequency signals such as P-waves and T-waves, as well as signals with small slope changes such as PVC, SVT, VT, and baseline drift.

[0074] To accelerate the calculation, the following second-order difference equation can be used:

[0075] Xdif(n)=Xecg(n)–2*Xecg(n-Rn)+Xecg(n-2*Rn) (Formula 1).

[0076] Where Xecg(n) is the pre-processed ECG signal; Rn = 0.04 * Fs; Fs is the sampling rate of the ECG signal; selecting a time interval of 0.04s will maximize the differential value of the R wave, while the differential values ​​of other low-frequency signals will be smaller; the lower the frequency, the smaller the output after differential filtering.

[0077] By first low-pass filtering and then differential filtering the ECG signal to be processed, almost only the QRS complex signal remains in the first ECG signal, while the amplitude of other characteristic waves is very small.

[0078] See Figure 3 , Figure 4 , Figure 3 This is a schematic diagram illustrating a filtering effect in a real-world scenario, as shown in an embodiment of this application. Figure 4 This is a schematic diagram illustrating another filtering effect in a real-world scenario as shown in the embodiments of this application.

[0079] See Figure 3 and Figure 4 The C signal curve is shown in the figure. Figure 3 In a normal ECG, the P wave and T wave amplitudes decrease after differential analysis, but the amplitude attenuation of the QRS differential signal does not change significantly. Figure 4 This diagram illustrates the filtering effect of an electrocardiogram (ECG) signal for arrhythmia (VT). In the diagram, A represents the original signal, B represents the amplitude-normalized signal (i.e., the signal after low-pass filtering), C represents the QRS differential signal (i.e., the signal after differential filtering), E represents the dynamic threshold signal representing the current dynamic threshold, and F represents the mean threshold signal representing the current mean. D represents the amplified QRS feature enhancement signal. The signals in other filtering effect diagrams represent similar content. In each effect diagram, the horizontal axis represents time, and the vertical axis represents the signal amplitude.

[0080] In this embodiment of the application, the differential filter output signal contains both positive and negative signals. After obtaining the first ECG signal, the first ECG signal can be further shaped, that is, the output signal needs to be shaped.

[0081] In practical applications, shaping can be achieved by taking the absolute value and then using a low-pass filter to filter out several fluctuation values ​​on the QRS group, forming a single-peak R-wave. This low-pass filter can be implemented using various methods, such as integration, time-window averaging, and IIR low-pass filters with suitable bandwidth. If a low-pass filter with low delay is used, the impact on the time delay for R-wave identification is minimal.

[0082] Furthermore, to enhance the signal-to-noise ratio and facilitate the identification of complex signal QRS waves, embodiments of this application can also perform nonlinear amplification processing on single-peak QRS waves. For example, a signal squaring algorithm can be used to amplify differences in signal amplitude.

[0083] In this embodiment of the application, the single-chip microcomputer electrocardiogram signal analysis and recognition algorithm can also be used to: calculate the current heart rate based on the second electrocardiogram signal after obtaining the second electrocardiogram signal.

[0084] In this embodiment, the current heart rate can be calculated after each shaped QRS wave is detected, by calculating the length of the RR interval between the two waves, thus calculating the real-time Beat-Beat heart rate. The detection method can be, for example, identifying the peak point of the R wave, such as... Figure 3 The signal is analyzed to determine if the triangular waveform contains sufficiently large spikes. If such spikes are detected, a shaped QRS complex is confirmed. If a QRS complex is present in the signal, the patient is deemed not to require defibrillation; otherwise, further analysis identifies the patient as having ventricular fibrillation, ventricular tachycardia, or other rhythms, and the defibrillator's internal charging and discharging circuitry is activated to administer defibrillation.

[0085] After obtaining the second electrocardiogram signal, the core task is to identify the QRS complex within it.

[0086] In this embodiment of the application, the electrocardiogram signal analysis and recognition algorithm in the microcontroller can also be used for:

[0087] The step of identifying the QRS complex in the second electrocardiogram signal based on the current dynamic threshold and the current mean threshold includes:

[0088] Based on the current dynamic threshold, the second electrocardiogram signal is identified to obtain the first QRS identification result;

[0089] The second electrocardiogram signal is identified based on the current mean threshold, and the second QRS identification result is obtained.

[0090] If the first R-wave peak time point in the first QRS identification result matches the second R-wave peak time point in the second QRS identification result, then the QRS group is determined at least based on the first QRS identification result.

[0091] The electrocardiogram signal analysis and recognition algorithm in the microcontroller can also be used for:

[0092] If the first R-wave peak time point in the first QRS identification result does not match the second R-wave peak time point in the second QRS identification result, then the correct result in the first QRS identification result and the second QRS identification result is determined based on the historical identification results, and the QRS group is determined based on the correct result.

[0093] The microcontroller is specifically configured as follows:

[0094] The calculation process for the current dynamic threshold includes:

[0095] Determine whether the target data in the second electrocardiogram signal is in the sampling hold period or the exponential decay period;

[0096] If the target data is within the sampling and holding period, the maximum value of the current dynamic threshold is updated based on the signal value of the target data;

[0097] If the target data is in the exponential decay period, the current dynamic threshold is calculated based on the decay period calculation formula.

[0098] Wherein, the V Ceof The current dynamic threshold is calculated in real time, where i is the count value and V is the value of the current value. RMax (0) represents the maximum R-wave value retained in the previous sampling and holding period of the current exponential decay period; exp is an exponential function, and B... adj The T value is an empirical value. lhr This is the lowest heart rate cycle time.

[0099] In this embodiment, an adaptive dynamic threshold calculation is set up, and detecting the existence of a QRS wave is of paramount importance for identifying the QRS wave.

[0100] By obtaining the second ECG signal, baseline drift has been filtered out, the amplitude of low-frequency signals has been compressed, and the amplitude of the QRS complex has been enhanced. For regular ECG signals, there is sufficient information to identify the presence of QRS waves; however, for many special arrhythmias, the amplitude and interval of the QRS waves are inconsistent because the signal morphology changes with the patient's condition and cardiac physiology. In such cases, simply relying on a fixed signal amplitude threshold will lead to many missed or false detections of QRS waves.

[0101] In view of this, in the embodiments of this application, a sample hold and exponential decay algorithm is used to dynamically calculate the QRS group threshold based on the detected QRS group amplitude and RR interval, thereby greatly reducing the possibility of missed detection.

[0102] The calculation process of the current dynamic threshold is explained in detail below.

[0103] In this embodiment of the application, the process of QRS signal identification and threshold calculation in the second electrocardiogram signal is divided into a threshold sampling and holding period Ph and a threshold exponential decay period Ps; and two timer variables are used to record the number of sampling points Nh and Nh during these two periods, which correspond to the time length of the two periods respectively; these two periods alternate.

[0104] Understandable Figure 3 and Figure 4 The raw signal on an AED is composed of individual actual sampling points. For example, an AED collects 228 ECG signals per second, which is 228 sampling points.

[0105] In this embodiment of the application, the purpose of the sampling and holding period is to determine the maximum value of the signal, which is the threshold. As long as the value of the newly sampled signal is greater than the current maximum value, the maximum value is updated, and the processing method is always in the sampling and holding period.

[0106] In this embodiment of the application, when the signal begins to fall below the current maximum value, the sampling and holding period ends, and the heart rate can be calculated once based on the count value of the sampling and holding period and the count value of the sampling point of the previous exponential decay period; at the same time, the threshold decay period begins to count.

[0107] The formula for calculating the current dynamic threshold during the threshold decay period is as follows:

[0108] V Ceof (i)=V RMax (0)*exp(-B adj *i*e Coed / T lhr )(Formula 2).

[0109] Wherein, the V Ceof The current dynamic threshold is calculated in real time, where i is the count value and V is the value of the current value. RMax (0) is the maximum R-wave value retained in the previous sampling hold period of the current exponential decay period.

[0110] The exp is an exponential function, and its input is (-B) adj *i*e Coed / T lhr ) is a negative number, indicating that the output decreases as time i increases.

[0111] The B adj These are empirical values; you can use data between 1 and 5.

[0112] The T lhr The minimum heart rate cycle time can be (T+0.2), where 0.2 is the redundancy time. For example, if the minimum heart rate is 30 BPM, then the cardiac cycle T at the minimum heart rate is 2 seconds. lhr =2.2 seconds, eCoed is a value related to VRMax(0) and signal-to-noise ratio, and is calculated using the following formula:

[0113] e Coed =log(V RMax (0) / Cn) (Formula 3).

[0114] Where Cn is the maximum digitized noise amplitude set by the system; if we want to suppress QRS noise in the same frequency band with a maximum amplitude of 0.1mV, and the digitized value of 0.1mV is Mn, then Cn is the square of Mn.

[0115] For example Figure 5 , Figure 5 This is a schematic diagram illustrating another filtering effect in a real-world scenario as shown in the embodiments of this application. Figure 5 This is a schematic diagram illustrating the filtering effect on a type of electrocardiogram signal with drastic QRS variation.

[0116] In this embodiment of the application, the advantages of using the dynamic threshold method to identify QRS groups are:

[0117] (1) Accurate identification of R-wave peak points.

[0118] (2) It can identify electrocardiogram signals with large QRS amplitude variations.

[0119] (3) The recognition threshold of the QRS complex changes rapidly and automatically following the preceding ECG cycle.

[0120] (4) The larger the R-wave amplitude, the faster the threshold decays, thus avoiding the subsequent small-amplitude QRS signal being missed.

[0121] (5) High noise resistance.

[0122] Among them, the attenuation does not have to use a negative exponential function; a linear attenuation function with a variable slope that does not require much calculation is also available. The slope is also equivalent to the newly detected R wave.

[0123] Since the waveforms of different heart rhythm signals vary greatly, no single algorithm can perfectly solve all problems; this application embodiment also uses the current mean threshold of the QRS waveform signal for auxiliary identification.

[0124] The formula for calculating the mean threshold Vmean(n) is as follows:

[0125]

[0126] Where, N TL T in Formula 2 lhr The corresponding number of sampling points, such as T lhr = 2.2 seconds, sampling rate 250, then N TL =550. V qrs The amplitude of the second electrocardiogram signal is after shaping and nonlinear amplification (e.g., squaring).

[0127] In practical applications, only one subtraction and one addition are needed, followed by a final integer division, to complete the mean calculation. Specifically, the QRS data that will overflow the time window can be subtracted from the previously calculated cumulative sum, and then the newly calculated QRS data can be added to complete the signal accumulation.

[0128] The QRS data within the overflow time window is expired data. The averaging operation, which requires dividing by the time window length, is unnecessary.

[0129] The mean curve of the QRS enhancement signal is as follows: Figure 3 , 4 The F curve in Figure 5 is shown.

[0130] When the enhanced QRS wave is greater than the QRS mean, counting begins; when it is less than the mean threshold, counting stops. The peak value of the waveform greater than the mean is the R wave peak point. The peak value of the next enhanced QRS wave greater than the mean is the R wave duration of the next heartbeat rhythm. The two RR intervals constitute the cardiac cycle, and the heart rate can be calculated using the second method.

[0131] Understandably, the advantage of obtaining the second QRS identification result is a low false negative rate, but a high false positive rate.

[0132] When the first and second QRS identification results show similar R-wave peak times and heart rate values, it confirms accurate detection of the QRS complex. If the R-wave occurrence times and calculated heart rates detected by the two methods differ significantly, the correct output can be determined based on the previous identification results.

[0133] In practical applications, for ECG R-wave amplitudes and heartbeat intervals that are not fixed, the mean threshold method may detect more R-waves, leading to inaccurate RR interval calculations and consequently, inaccurate heart rate calculations. However, the attenuation detection method used to obtain the first QRS recognition result has a high accuracy rate for RR intervals and is effective for arrhythmias and abnormal rhythms with large amplitude variations. However, it may miss subsequent heartbeats in efferent rhythms and single-conduction or PVC rhythms. Therefore, if an R-wave is detected after a timeout during the calculation of the first QRS recognition result, it may indicate an efferent or PVC rhythm. If the mean threshold method detects an R-wave, it indicates a missed detection. In this case, the R-peak value can be found within the R-wave detection time period during the calculation of the second QRS recognition result to resolve the missed detection issue.

[0134] Through the above embodiments, the embodiments of this application can achieve the following technical effects:

[0135] (1) A real-time QRS identification algorithm that can run on low-cost, low-power MCUs with low computing power.

[0136] (2) It can accurately and quickly output QRS recognition results with a recognition accuracy of over 99.9%.

[0137] (3) It has strong applicability and can be used for various arrhythmias, noise, and interference signals.

[0138] A second aspect of this application also provides a defibrillator 60, such as an AED, including the aforementioned system. It includes a housing 61, an electrode circuit 62, and an electrocardiogram (ECG) signal processing system 63 connected to the electrode circuit, as described above. The electrode circuit is electrically connected to the ECG signal processing system, and the ECG processing system is electrically connected to an execution circuit 64, which performs the defibrillation function of the defibrillator. Each circuit is powered by a power supply (not shown in the figure). The defibrillator mainly comprises several parts: ECG acquisition, identification of defibrillation and non-defibrillation rhythms based on single ECG signal analysis and processing, and charging and discharging defibrillation control of the high-voltage defibrillation capacitor. If a QRS complex is present in the signal, the patient is identified as not requiring defibrillation; otherwise, further analysis identifies ventricular fibrillation, ventricular tachycardia, or other rhythms, and the internal charging and discharging circuit of the defibrillator is controlled to perform defibrillation treatment on the patient.

[0139] Figure 6 This is a schematic diagram of the defibrillator structure shown in the embodiments of this application.

[0140] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the present application can be implemented as electronic hardware, computer software, or a combination of both.

[0141] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0142] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A cardiac signal processing system, characterized in that, include: An electrocardiogram (ECG) signal acquisition circuit, and a microcontroller connected to the ECG signal acquisition circuit capable of executing an ECG signal analysis and recognition algorithm; The electrocardiogram (ECG) signal acquisition circuit is used to acquire the ECG signal to be processed and transmit it to the microcontroller. The electrocardiogram (ECG) signal analysis and recognition algorithm in the microcontroller is used to: receive the ECG signal to be processed and determine whether there is an interference signal in the ECG signal to be processed; if there is no interference signal, perform a filtering operation on the ECG signal to be processed to obtain a first ECG signal. The first electrocardiogram (ECG) signal is shaped to obtain the second ECG signal; Based on the current dynamic threshold and the current mean threshold, the QRS wave identification result in the second electrocardiogram signal is obtained; the current dynamic threshold is obtained based on the R wave peak value of the second electrocardiogram signal and the historical dynamic threshold, and the current mean threshold is obtained based on the signal mean of the second electrocardiogram signal; The step of obtaining the QRS wave identification result in the second electrocardiogram signal based on the current dynamic threshold and the current mean threshold includes: Based on the current dynamic threshold, the second electrocardiogram signal is identified to obtain the first QRS identification result; The second electrocardiogram signal is identified based on the current mean threshold, and the second QRS identification result is obtained. If the first R-wave peak time point in the first QRS identification result matches the second R-wave peak time point in the second QRS identification result, then the QRS group is determined at least based on the first QRS identification result.

2. The system according to claim 1, characterized in that, The single-chip microcomputer electrocardiogram signal analysis algorithm is also used to: calculate the current heart rate based on the second electrocardiogram signal after obtaining the second electrocardiogram signal.

3. The system according to claim 1, characterized in that, The ECG signal analysis algorithm in the microcontroller is also used to trigger a timer if the interference signal is present, so that when the timer expires, the step of receiving the ECG signal to be processed is executed.

4. The system according to claim 1, characterized in that, The ECG signal analysis algorithm in the microcontroller is also used to: if the time point of the first R wave peak in the first QRS identification result does not match the time point of the second R wave peak in the second QRS identification result, then determine the correct result in the first QRS identification result and the second QRS identification result based on the historical identification results, and determine the QRS complex based on the correct result.

5. The system according to claim 1, characterized in that, The calculation process of the current dynamic threshold in the ECG signal analysis algorithm of the microcontroller includes: determining whether the target data in the second ECG signal is in the sampling hold period or the exponential decay period; if the target data is in the sampling hold period, then updating the maximum value of the current dynamic threshold based on the signal value of the target data; if the target data is in the exponential decay period, then calculating the current dynamic threshold based on the decay period calculation formula.

6. The system according to claim 5, wherein the formula for calculating the decay period specifically includes: V_Ceof(i) = V_RMax(0) * exp(-B_adj * i * e_Coed / T_lhr); where V_Ceof is the current dynamic threshold calculated in real time, i is the count value, V_RMax(0) is the maximum R-wave value retained in the previous sampling hold period of the current exponential decay period, exp is the exponential function, B_adj is the empirical value, and T_lhr is the minimum heart rate cycle time.

7. The system according to claim 1, characterized in that, The ECG signal analysis algorithm in the microcontroller describes a filtering operation on the ECG signal to be processed, which involves low-pass filtering followed by differential filtering.

8. The system according to claim 7, characterized in that, The ECG signal analysis algorithm in the microcontroller involves shaping the first ECG signal by performing a low-pass filter followed by nonlinear amplification.

9. A defibrillator, characterized in that, include: The housing, the electrode circuit, and the electrocardiogram signal processing system as described in any one of claims 1 to 8 connected to the electrode circuit.

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