CN116849669A

CN116849669A - Electrocardiosignal signal processing system and defibrillator

Info

Publication number: CN116849669A
Application number: CN202310975608.5A
Authority: CN
Inventors: 巩欣洲
Original assignee: Xi'an Ruixin Kangda Medical Technology Co ltd
Current assignee: Xi'an Ruixin Kangda Medical Technology Co ltd
Priority date: 2023-08-03
Filing date: 2023-08-03
Publication date: 2023-10-10

Abstract

The application belongs to the technical field of medical appliances, and particularly relates to an electrocardiosignal processing system and a defibrillator. The central electric signal processing system comprises: an electrocardiosignal front-end processing circuit and a singlechip which can execute an electrocardiosignal analysis and identification algorithm. The electrocardiosignal analysis and identification algorithm firstly judges whether non-electrocardiosignals such as interference signals exist in the electrocardiosignal signals to be processed; if the electrocardiosignals do not exist, filtering the electrocardiosignals to be processed to obtain a first electrocardiosignal; shaping the first electrocardiosignal to obtain a second electrocardiosignal; and obtaining a QRS wave identification result in the second electrocardiosignal based on the current dynamic threshold and the current average value threshold. Based on the electrocardiosignal processing system, the QRS wave in the electrocardiosignal can be accurately identified, and then the identification accuracy of the non-defibrillation heart rhythm of the AED can be greatly improved, so that the safety and reliability of the AED equipment can be improved.

Description

Electrocardiosignal signal processing system and defibrillator

Technical Field

The application relates to the technical field of medical equipment, in particular to an electrocardiosignal signal processing system and a defibrillator.

Background

AEDs (automatic external defibrillators, automated External Defibrillator) are capable of stimulating the heart to contract and expand simultaneously with a certain amount of current to restore normal beating rhythms to patients suffering from ventricular fibrillation and ventricular tachycardia.

Among them, the electrocardiosignal processing part inside the AED is an important functional component. It mainly plays a role in heart rhythm recognition. AED regulations require that the heart rate without defibrillation, i.e., the non-defibrillation heart rate, be identified with an accuracy of no less than 99%, room flutter identification accuracy of no less than 95%, and ventricular tachycardia identification accuracy of no less than 75%.

An important feature of non-defibrillation heart rate is that the signal has a QRS wave; however, since the QRS waveform changes in various non-defibrillation abnormal cardiac rhythm electrocardiosignals are very complex, it is not a simple matter to accurately identify the non-defibrillation cardiac rhythm.

Disclosure of Invention

In order to solve the problem of accurate identification caused by larger waveform variation of the non-defibrillation arrhythmia in the related technology, the singlechip in the central electric signal processing system can run a novel rapid real-time electrocardiosignal analysis and identification algorithm, and the identification rate is more than 99.8% through clinical and database tests. The accuracy of the non-defibrillation heart rhythm identification is improved, so that the safety and reliability of the AED can be improved; the method has the characteristics of high speed and small real-time operation, so that the defibrillation equipment such as an AED and the like using the method greatly reduces the calculation capacity and the resource occupation requirement, can greatly reduce the realized circuit and the power consumption, and is convenient for realizing the miniaturized portable electrocardiosignal of the AED.

The first aspect of the present application provides an electrocardiograph signal processing system, comprising:

the system comprises an electrocardiosignal acquisition circuit and a singlechip which is connected with the electrocardiosignal acquisition circuit and can execute an electrocardiosignal analysis and identification algorithm;

the electrocardiosignal acquisition circuit is used for acquiring an electrocardiosignal to be processed and transmitting the electrocardiosignal to the singlechip;

the electrocardiosignal analysis and identification algorithm in the singlechip is used for: receiving the electrocardiosignals to be processed, and judging whether interference signals exist in the electrocardiosignals to be processed; if the interference signal does not exist, filtering the electrocardiosignal to be processed to obtain a first electrocardiosignal;

shaping the first electrocardiosignal to obtain a second electrocardiosignal;

based on the current dynamic threshold value and the current average value threshold value, a QRS wave identification result in the second electrocardiosignal is obtained; the current dynamic threshold is obtained based on an R wave tip peak value and a historical dynamic threshold of the second electrocardiosignal, and the current average threshold is obtained based on a signal average value of the second electrocardiosignal.

In some embodiments, the electrocardiograph signal analysis algorithm is further configured to: when a second electrocardiosignal is obtained, the current heart rate is calculated based on the second electrocardiosignal.

In some embodiments, the electrocardiosignal analysis algorithm is further configured to trigger a timer to count if the interference signal is present, so as to perform the step of receiving the electrocardiosignal to be processed after counting is finished.

In some embodiments, the electrocardiograph signal analysis algorithm is further configured to:

the obtaining the QRS wave recognition result in the second electrocardiograph signal based on the current dynamic threshold and the current average threshold includes:

identifying the second electrocardiosignal based on the current dynamic threshold value to obtain a first QRS identification result;

identifying the second electrocardiosignal based on the current mean threshold value to obtain a second QRS identification result;

and if the first R wave crest time point in the first QRS recognition result is matched with the second R wave crest time point in the second QRS recognition result, determining a QRS complex at least based on the first QRS recognition result.

if the first R peak time point in the first QRS recognition result is not matched with the second R peak time point in the second QRS recognition result, determining a correct result in the first QRS recognition result and the second QRS recognition result based on the historical recognition result, and determining a QRS complex based on the correct result.

In some embodiments, the calculating of the current dynamic threshold in the electrocardiosignal analysis algorithm includes:

judging whether target data in the second electrocardiosignal is in a sample hold period or an exponential decay period;

if the target data is in the sample hold period, updating the maximum value of the current dynamic threshold value based on the signal value of the target data;

and if the target data is in the exponential decay period, calculating a current dynamic threshold based on a decay period calculation formula.

In some embodiments, the decay period calculation formula specifically includes:

V _Ceof (i)＝V _RMax (0)*exp(-B _adj *i*e _Coed /T _lhr )；

wherein the V is _Ceof For the current dynamic threshold calculated in real time, i is a count value, V is _RMax (0) The maximum R wave value reserved in the previous sample hold period of the current exponential decay period; the exp is an exponential function, the B _adj As an empirical value, the T _lhr Is the lowest heart rate cycle time.

In some embodiments, the filtering operation of the electrocardiosignal to be processed in the electrocardiosignal analysis algorithm is low-pass filtering and then differential filtering.

In some embodiments, the shaping the first electrocardiograph signal in the electrocardiograph signal analysis algorithm is a nonlinear amplification operation after low-pass filtering the first electrocardiograph signal.

A second aspect of the present application provides a defibrillator comprising: a housing, an electrode circuit, and an electrocardiosignal processing system as described above connected with the electrode circuit.

The technical scheme provided by the application can comprise the following beneficial effects: the non-defibrillation heart rhythm can be accurately identified based on the QRS wave, and the identification accuracy of the non-defibrillation heart rhythm is improved, so that the safety and reliability of the AED can be improved; the method has the characteristics of high speed and small real-time operation, so that the defibrillation equipment such as an AED and the like using the method greatly reduces the calculation capacity and the resource occupation requirement, can greatly reduce the realized circuit and the power consumption, and is convenient for realizing the miniaturization portability of the AED.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.

Fig. 1 is a schematic structural diagram of an electrocardiograph signal processing system according to an embodiment of the present application.

Fig. 2 is a schematic flow chart of a single chip microcomputer for obtaining a QRS wave recognition result according to an embodiment of the present application.

Fig. 3 is a schematic diagram of a filtering effect in a practical scenario according to an embodiment of the present application.

Fig. 4 is a schematic diagram of another filtering effect in a practical scenario shown in an embodiment of the present application.

Fig. 5 is a schematic diagram of still another filtering effect in a practical scenario shown in an embodiment of the present application.

Fig. 6 is a schematic diagram of a defibrillator according to an embodiment of the present application.

Detailed Description

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

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

It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the application. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

The embodiment of the application is applied to the technical field of medical equipment, in particular to an Automatic External Defibrillator (AED), wherein the heart rate needs to be accurately identified, and at least the following requirements need to be met:

accurately identifying various types of ventricular fibrillation and ventricular tachyrhythms which need defibrillation;

accurately identifying other normal and abnormal heart rhythms which do not need defibrillation;

in fully automatic external defibrillation equipment, it is necessary to identify R waves that are atrial fibrillation and the variation of ventricular rate waves;

in the early warning algorithm, there is a need to identify various types of ventricular flutter and earlier ventricular abnormal contractions that would induce ventricular fibrillation.

In addition, most cardiac rhythms which do not need defibrillation contain R waves or QRS waves formed by normal ventricular contractions, so that in general, the most basic cardiac rhythm recognition algorithm only accurately recognizes that the R waves exist in electrocardiosignals, namely, the cardiac rhythms do not need defibrillation at the moment. The real-time accurate identification of QRS waves is therefore a very important link for the electrocardiographic identification algorithm of the AED.

For algorithms in AED devices, one international hard indicator of non-defibrillation heart rhythm is an identification rate of no less than 99%. Accurate identification of the QRS wave is therefore of great importance to AED devices.

In the related art, the QRS complex may be identified by methods such as an R-wave peak detection method, an R-wave track length method, and the like. However, the inventor finds that the normal electrocardiosignals are regular, the variation of the signal amplitude and the characteristic values is small, and the consistency of the time and the amplitude of each cardiac cycle is good. And thus is easily identifiable. However, there are various variations in the PQRST wave of the abnormal electrocardiographic signal, and there is a possibility that the time and amplitude characteristic values are different from the normal values in each period of the electrocardiographic signal, and the differences may also vary with the variations of the cardiac physiology; even more irregular is that the interference of power frequency noise interference, myoelectricity interference, limb movement interference, heart rate variation and characteristic value variation caused by body position change, electrocardio baseline drift interference caused by polarization voltage mutation, respiratory interference and the like can be doped in the actual electrocardiosignal acquisition, and the duration time and the caused amplitude change of the interference are unknown without any rule.

Based on this, in the embodiment of the application, an electrocardiosignal processing system and a defibrillator are provided, which can identify and process interference and noise signals, enable QRS identification not to be affected by the interference signals, and can quickly restore the normal detection function of data processing, so that the identification of each correct QRS wave is not lost, the identification result about the QRS wave can be accurately and quickly output, and the identification accuracy is more than 99.9%.

Embodiments of the present application are described in detail below.

Referring to fig. 1 and 2, fig. 1 is a schematic structural diagram of an electrocardiograph signal processing system according to an embodiment of the present application, and fig. 2 is a schematic flow chart of a single chip microcomputer according to an embodiment of the present application for obtaining a QRS wave recognition result.

An electrocardiograph signal processing system 10 according to an embodiment of the present application includes:

an electrocardiosignal acquisition circuit 1 and a singlechip 2 connected with the electrocardiosignal acquisition circuit; the singlechip 2 can store and execute electrocardiosignal analysis and recognition algorithm.

the single chip microcomputer 2 central electric signal analysis and identification algorithm can comprise the following steps of single chip microcomputer:

s100, receiving the electrocardiosignals to be processed, and judging whether interference signals exist in the electrocardiosignals to be processed;

s200, if the interference signal does not exist, filtering the electrocardiosignal to be processed to obtain a first electrocardiosignal;

s300, shaping the first electrocardiosignal to obtain a second electrocardiosignal;

s400, based on the current dynamic threshold value and the current average value threshold value, a QRS wave identification result in the second electrocardiosignal is obtained; the current dynamic threshold is obtained based on an R wave tip peak value and a historical dynamic threshold of the second electrocardiosignal, and the current average threshold is obtained based on a signal average value of the second electrocardiosignal.

In the embodiment of the application, an electrocardiosignal acquisition circuit is electrically connected with a singlechip. The singlechip can be an MCU with low cost, weak operation capability and low power consumption. The electrocardiosignal acquisition circuit can be connected with an external circuit, can receive the electrocardiosignals to be processed sent by the external circuit, for example, is connected with other data sensing devices, and can only receive the electrocardiosignals to be processed. In practice, for example, it may be connected to the electrode acquisition related circuitry in the AED. The single chip microcomputer 2 may be connected to other circuits, such as an execution circuit, etc., and may have a connection relationship with the single chip microcomputer 2 with other circuits that need to process the QRS wave recognition result obtained by the method, which is not described herein.

It will be appreciated that the electrocardiographic signals to be processed may be acquired in real time by electrode acquisition related circuitry in the AED. The electrocardiosignal to be processed can comprise related data of a heartbeat beat and can be set according to actual needs. Wherein the heart rate is in the range of 30 to 240 beats per minute, i.e. a heartbeat of at most 2 seconds and a beat of at least 0.25 seconds, i.e. a beat of 4 seconds.

In the embodiment of the application, various interference signals may exist in the electrocardiosignal to be processed, and the electrocardiosignals can be divided into two types: catastrophic, filterable.

Based on this, the single-chip microcomputer central electric signal analysis and recognition algorithm in the embodiment of the application can also be used for: if the interference signal exists, triggering a timer to count so as to execute the step of receiving the electrocardiosignal to be processed after counting is finished.

When there is a catastrophic disturbance signal in the electrocardiosignal to be processed, the data processing method is not suitable for operation, forced operation or erroneous conclusion, and can cause fatal consequences.

Wherein the catastrophic interfering signal is not a normal naturally occurring physiological signal, and can generally be defined and identified from a signal amplitude range, duration length, etc.; however, in the presence of these catastrophic disturbances, the data processing method may be reset in a stopped analysis state.

It can be understood that the patient limb movement signal and the interference in the electrode slice pasting process, the sudden level change caused by the contact of other people with the patient, etc. can generate catastrophic interference on the electrocardiosignal, and the electrocardiosignal is submerged in the interference during the period of time, and the interference cannot be filtered, so that the analysis can only be stopped.

The method which can be adopted comprises the following steps: calculating a signal peak value, a baseline fluctuation value and a signal slope, triggering a 2-second retriggerable software timer exceeding a normal variation range, resetting a subsequent data processing method once the timer is set, and stopping analysis; execution is needed until after a fixed time reset.

The other type of filterable interference signal is only unnecessary useless signal, and is mixed in the electrocardiosignal to be processed, so that the data processing method cannot be collapsed, but the analysis accuracy of the QRS complex is affected. Therefore, in the embodiment of the application, a non-electrocardiosignal detection method is provided, so that the data processing method only analyzes effective data such as electrocardiosignals of a human body.

Based on the above, the singlechip comprises a first low-pass filtering module and a differential filtering module; and filtering the electrocardiosignals to be processed into low-pass filtering and then differential filtering.

The low-pass filter module can be used for configuring a low-pass filter with a cut-off frequency of 35Hz for the signal, filtering high-frequency noise in the QRS, and improving the time stability in the subsequent QRS complex identification. Amplitude normalization of the signal can be achieved. The low-pass filter may be implemented in software.

The differential filtering module may use a second order differential filter with a time interval of 0.04s (R wave interval), and may filter out baseline wander interference, low frequency signals such as P wave T wave, and the like, and PVC, SVT, VT, and signal sums with smaller slope changes, such as baseline wander.

In order to accelerate the operation speed, the following 2-order difference equation may be adopted:

xdif (n) =xecg (n) -2 x Xecg (n-Rn) +xecg (n-2 x Rn) (formula 1).

Wherein Xecg (n) is a pre-processed electrocardiographic signal; rn=0.04 xfs; fs is the sampling rate of the ECG signal; selecting a time interval of 0.04s, so that the difference value of R waves is maximum, and the difference values of other low-frequency signals are smaller; the lower the frequency, the smaller the number of differential filtered products.

By low-pass filtering and differential filtering of the electrocardiosignals to be processed, only QRS complex signals almost remain in the first electrocardiosignals, and the amplitudes of other characteristic waves are small.

Referring to fig. 3 and fig. 4, fig. 3 is a schematic diagram of a filtering effect in an actual scene shown in an embodiment of the present application, and fig. 4 is a schematic diagram of another filtering effect in an actual scene shown in an embodiment of the present application.

See the C signal curves in fig. 3 and 4. The P-wave and T-wave of the normal electrocardiograph in fig. 3 are reduced in amplitude after difference, but the QRS differential signal amplitude attenuation is not changed much. Fig. 4 is a schematic diagram of the filtering effect of an electrocardiographic signal of an abnormal heart rhythm (VT). In the figure, A is an original signal, B is an amplitude normalization signal, namely a low-pass filtered signal, C is a QRS differential signal, namely a differential filtered signal. E is a dynamic threshold signal representing the current dynamic threshold, and F is an average threshold signal representing the current average. And D is the QRS characteristic strengthening signal after the amplification treatment. The other filtering effect diagrams are similar in what each signal characterizes. In each effect graph, the horizontal axis represents time, and the vertical axis represents signal amplitude values.

In the embodiment of the application, the differential filtering output signal has positive and negative signals, and after the first electrocardiosignal is obtained, the first electrocardiosignal can be further subjected to signal shaping, namely the output signal is required to be shaped.

In practical use, the shaping can be to take absolute value and then filter out several fluctuation values on the QRS wave group by a low-pass filter to form a single-peak R wave. The low-pass filter can be realized by various methods, and the purposes of integral operation, time window accumulated average, IIR low-pass filter with proper bandwidth and the like can be achieved; if a low-pass filter with small delay is used, the effect on the time delay of identifying the R-wave is small.

Furthermore, in order to enhance the signal-to-noise ratio and easily identify the QRS wave of the complex signal, the embodiment of the application can also perform nonlinear amplification processing on the QRS wave with a single wave peak. The difference in signal amplitude is amplified, for example using a signal squaring algorithm.

In the embodiment of the application, the single chip microcomputer center electric signal analysis and identification algorithm can also be used for: when a second electrocardiosignal is obtained, the current heart rate is calculated based on the second electrocardiosignal.

In the embodiment of the application, the current heart rate can be performed after each shaped QRS wave is detected, and the time length of the front and rear R-R intervals is calculated, so that the real-time Beat-Beat heart rate can be calculated. The detection mode may, for example, identify a peak point of the R wave, for example, whether the triangular wave pattern in fig. 3 has a spike peak with a sufficient amplitude, and if the peak point is detected, it may be determined that the shaped QRS wave is detected. Wherein the patient is identified as not requiring defibrillation if a QRS wave is present in the signal; otherwise, continuing to analyze and identify the heart rhythm such as ventricular fibrillation, and the like, and controlling a charging and discharging circuit in the defibrillator to perform defibrillation treatment on the patient.

After the second electrocardiosignal is obtained, the core is to identify the QRS complex in the second electrocardiosignal.

In the embodiment of the application, the electrocardiosignal analysis and identification algorithm in the singlechip can also be used for:

the identifying QRS complexes in the second cardiac signal based on the current dynamic threshold and the current mean threshold includes:

The electrocardiosignal analysis and identification algorithm in the singlechip can also be used for:

The singlechip is specifically configured as follows:

the calculation process of the current dynamic threshold value comprises the following steps:

Wherein the V is _Ceof For the current dynamic threshold calculated in real time, i is a count value, V is _RMax (0) As the wayThe maximum R-wave value remaining in the previous sample-and-hold period of the previous exponential decay period; the exp is an exponential function, the B _adj As an empirical value, the T _lhr Is the lowest heart rate cycle time.

In the embodiment of the application, the self-adaptive dynamic threshold calculation is set, and the detection of the existence of a QRS wave is the recognition of the weight of the QRS wave.

By obtaining the second electrocardiosignal, baseline drift is filtered, the amplitude of the low-frequency signal is compressed, and the amplitude of the QRS complex is enhanced. For regular electrocardiosignals, the presence of a QRS wave is identified with enough information; but for many specific abnormal heart rhythms, the QRS wave amplitude and interval are not uniform, as the signal morphology will change with changes in the condition and physiology of the heart. In this case, simply determining by a simple fixed signal amplitude threshold would result in missed or false detection of many QRS waves.

In view of this, in the embodiment of the present application, a sample-hold and exponential decay algorithm is used to dynamically calculate the QRS complex threshold according to the detected QRS complex amplitude and R-R interval, thereby greatly reducing the probability of missed detection.

The calculation of the current dynamic threshold will be described in detail below.

In the embodiment of the application, the QRS signal identification and threshold calculation process in the second electrocardiosignal is divided into a sample-hold period Ph of a threshold value and an exponential decay period Ps of the threshold value; recording the number Nh and the number Nh of sampling points in the two periods respectively by using two timer variables, and respectively corresponding to the time lengths of the two periods; these two periods alternate.

It will be appreciated that the original signal of fig. 3 and 4 is composed of individual actual sample points. For example, on an AED 228 electrocardiographic signals are acquired a second, i.e., 228 sampling points.

In the embodiment of the application, the purpose of the sample-and-hold period is to determine the maximum value of the signal, namely the threshold value; 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 sample hold period.

In the embodiment of the application, when the signal starts to be smaller than the current maximum value and the sampling hold period ends, the heart rate can be calculated once according to the count value of the sampling hold period and the count value of the sampling point of the last exponential decay period; while the threshold decay period begins counting.

Wherein, the calculation formula of the current dynamic threshold value of the threshold value decay period is as follows:

V _Ceof (i)＝V _RMax (0)*exp(-B _adj *i*e _Coed /T _lhr ) (equation 2).

Wherein the V is _Ceof For the current dynamic threshold calculated in real time, i is a count value, V is _RMax (0) The maximum R-wave value that remains in the sample-and-hold period preceding the current exponential decay period.

The exp is an exponential function, its input (-B) _adj *i*e _Coed /T _lhr ) Is a negative number, indicating that the output decreases with increasing time i.

The B is _adj For empirical values, data between 1 and 5 may be taken.

The T is _lhr For the lowest heart rate cycle time, the value may be (t+0.2), where 0.2 is the redundant time; if the lowest heart rate is, for example, 30BPM, the cardiac cycle T of the lowest heart rate is 2 seconds, T _lhr =2.2 seconds, eCoed is a value related to VRMax (0) and signal-to-noise ratio, and the calculation formula is:

e _Coed ＝log(V _RMax (0) /Cn) (equation 3).

Where Cn is the set maximum digitized noise amplitude of the system; if the maximum amplitude of the QRS same-band noise is to be suppressed at 0.1mV, and the value after 0.1mV is digitized at Mn, cn is the square of Mn.

For example, fig. 5 is a schematic diagram of still another filtering effect in a practical scenario shown in an embodiment of the present application. Fig. 5 is a schematic diagram of the filtering effect of an electrocardiosignal with severe QRS variation.

In the embodiment of the application, the method for identifying the QRS complex by adopting the dynamic threshold method has the advantages that:

(1) And the R wave crest value point identification is accurate.

(2) An electrocardiosignal whose QRS complex amplitude varies greatly can be identified.

(3) The recognition threshold of the QRS complex rapidly and automatically changes following the preceding electrocardiographic cycle.

(4) The larger the R-wave amplitude, the faster the threshold decay, avoiding the subsequent omission of small amplitude QRS signals.

(5) High noise immunity.

Wherein the attenuation can be carried out without using a negative exponential function, and a linear attenuation function with a variable slope and a small calculation amount is also available; the slope is also correlated with the newly detected R-wave value.

Because each characteristic waveform of different heart rhythm signals is changed, no algorithm can perfectly solve all problems; the embodiment of the application also carries out auxiliary identification by using the current average value threshold value of the QRS waveform signal.

The calculation formula of the mean threshold value Vmean (n) is as follows:

wherein N is _TL Is T in formula 2 _lhr Corresponding sampling points, e.g. T _lhr =2.2 seconds, sample rate is 250, then N _TL ＝550。V _qrs Is the amplitude of the shaped and non-linearly amplified (e.g., squared) second cardiac signal.

In practical application, the average value calculation can be completed by only one subtraction and one addition and finally one integral division. Specifically, the QRS data of the time window to be overflowed is subtracted from the accumulation sum calculated last time, and the newly calculated QRS data is added, so that the accumulation of signals is completed.

Wherein the QRS data of the overflow time window is expiration data. An operation of dividing the time window length required for averaging is not necessary.

The mean curve of the QRS enhanced signal is shown as the F curve in fig. 3, 4, 5.

When the enhanced QRS wave is larger than the QRS mean value, starting counting, and when the enhanced QRS wave is smaller than the mean value threshold value, stopping counting; the peak value of the waveform larger than the mean value is an R wave crest value point, the next peak value of the enhanced QRS larger than the mean value is the R wave time of the next heart beat rhythm, the two R-R intervals are cardiac cycles, and the heart rate of the second method can be calculated.

It can be appreciated that the advantage of obtaining the second QRS recognition result is a low omission rate, but a higher false detection rate.

When the R wave crest value time point and the heart rate value with little difference are detected in the obtained first QRS recognition result and the second QRS recognition result, the accurate detection of the QRS complex is confirmed; if the R wave detected by the two methods appears at the time point and the calculated heart rate difference is large, the correct output can be determined by the identification results of the previous times.

In actual use, for the unstable electrocardio R wave amplitude and the unstable heart beat interval, the average value threshold method can detect more R waves, and the R-R interval calculated by the method is inaccurate, so the calculated heart rate is inaccurate, but the accuracy of the R-R interval in the attenuation detection method used for obtaining the first QRS recognition result is very high, and the effect on arrhythmia and abnormal heart rate with large amplitude variation is good. However, for escape beat and single conduction or PVC heart rate, the subsequent heart beat may be missed, and thus, if the occurrence of R wave is found to be overtime during the calculation of the first QRS recognition result, it may be escape beat or PVC; however, if the average value threshold method detects an R wave, the detection omission is indicated, and at this time, an R peak value can be found in an R wave period detected in the process of calculating the second QRS recognition result, so that the detection omission problem can be solved.

Through the above embodiment, the embodiment of the application can have the following technical effects:

(1) The real-time QRS recognition algorithm can be operated on the MCU with low cost, low operation capability and low power consumption.

(2) The QRS recognition result can be accurately and rapidly output, and the recognition accuracy is more than 99.9%.

(3) The applicability is strong, and various abnormal heart rhythms, noise and interference signals can be aimed.

The second aspect of the application also provides a defibrillator 60, such as an AED, including the system described above. Comprising the following steps: a housing 61, an electrode circuit 62, and an electrocardiosignal processing system 63 as described above connected to the electrode circuit. The electrode circuit is electrically connected to an electrocardio signal processing system, which is electrically connected to an execution circuit 64 for executing a defibrillation function of the defibrillator. The individual circuits are powered by a power supply (not shown). The defibrillator mainly comprises an electrocardiograph acquisition part, a charging and discharging defibrillation control part, a discharging and defibrillation control part and a discharging and defibrillation control part, wherein the electrocardiograph acquisition part is used for carrying out defibrillation and non-defibrillation cardiac rhythm identification based on Shan Yixin electrical signal analysis and processing; identifying that the patient does not need defibrillation if a QRS wave is present in the signal; otherwise, continuing to analyze and identify the heart rhythm such as ventricular fibrillation, and the like, and controlling a charging and discharging circuit in the defibrillator to perform defibrillation treatment on the patient.

Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the application herein may be implemented as electronic hardware, computer software, or combinations of both.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing description of embodiments of the application has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An electrocardiographic signal processing system, comprising:

shaping the first electrocardiosignal to obtain a second electrocardiosignal;

2. The system of claim 1, wherein the single-chip microcomputer electrocardiosignal analysis algorithm is further configured to: when a second electrocardiosignal is obtained, the current heart rate is calculated based on the second electrocardiosignal.

3. The system of claim 1, wherein the electrocardiosignal analysis algorithm in the single-chip microcomputer is further configured to trigger a timer to count if the interference signal exists, so as to perform the step of receiving the electrocardiosignal to be processed after the counting is finished.

4. The system of claim 1, wherein the electrocardiosignal analysis algorithm in the singlechip is further used for the singlechip:

5. The system of claim 4, wherein the electrocardio signal analysis algorithm in the single-chip microcomputer is further configured to:

6. The system of claim 1, wherein the calculating the current dynamic threshold in the electrocardio signal analysis algorithm in the single-chip microcomputer comprises:

7. The system of claim 6, wherein the decay period calculation formula specifically comprises:

V _Ceof (i)＝V _RMax (0)*exp(-B _adj *i*e _Coed /T _lhr )；

8. The system of claim 1, wherein the filtering operation of the electrocardiosignal to be processed in the electrocardiosignal analysis algorithm in the singlechip is low-pass filtering and then differential filtering.

9. The system of claim 8, wherein the shaping the first cardiac signal in the cardiac signal analysis algorithm in the single chip microcomputer is a nonlinear amplification operation performed after the low-pass filtering of the first cardiac signal.

10. A defibrillator, comprising: a housing, an electrode circuit, and an electrocardiosignal signal processing system as claimed in any one of claims 1 to 9 connected to the electrode circuit.