Defibrillation device control method and device based on rhythm recognition, defibrillation device

Abstract

The application discloses a rhythm recognition-based defibrillation device control method and device and a defibrillation device. The method comprises the following steps: collecting a to-be-analyzed signal according to a target sampling rate obtained by reducing the initial sampling rate; determining an amplitude mean value and an amplitude probability according to a smooth signal obtained by denoising and smoothing processing of the to-be-analyzed signal; performing a CPR operation when the amplitude mean value and the amplitude probability meet the asystole rhythm condition, otherwise, converting the smooth signal into a standard signal, and performing a defibrillation operation when a waveform complexity parameter and a waveform amplitude parameter meet a first defibrillation condition, or a heart rate parameter and a QRS wave width meet a second defibrillation condition. According to the technical scheme of the embodiment, an emergency can be quickly judged based on a shorter duration signal, the asystole rhythm is used to realize the rapid response of asystole, the first defibrillation condition and the second defibrillation condition are used to identify the defibrillation scene in urgent need of electric shock, and the response efficiency of the defibrillation device is improved, so that the rescue efficiency of the defibrillation device is improved.

Description

Technical Field

[0001] This application relates to the field of medical device technology, specifically to a defibrillator control method, device, and defibrillator based on rhythm recognition. Background Technology

[0002] Ventricular fibrillation (VF) is considered a common type of arrhythmia leading to sudden cardiac death (SCD). When VF occurs, the contraction of the myocardium is disordered, the heart loses its pumping function, leading to cardiac arrest and threatening the patient's life. Defibrillators are crucial for managing VF. Common defibrillators perform rhythm analysis after pre-charging and then administer shocks or cardiopulmonary resuscitation (CPR) based on the results. However, to distinguish between defibrillable and non-defibrillable rhythms, defibrillators typically require collecting signals at two analysis intervals for rhythm analysis, which is time-consuming and cannot provide a rapid response to emergencies such as cardiac arrest or coarse ventricular fibrillation. Summary of the Invention

[0003] This application provides a method, apparatus, and defibrillator control system based on rhythm recognition, which at least ensures that the solution can complete rhythm analysis in a shorter time and improve the response efficiency of the defibrillator.

[0004] In a first aspect, embodiments of this application provide a defibrillator control method based on rhythm recognition, applied to a defibrillator, the method comprising:

[0005] The target sampling rate is determined based on the preset initial sampling rate and downsampling coefficient, and the signal to be analyzed is acquired based on the target sampling rate.

[0006] The signal to be analyzed is denoised and smoothed to obtain a smoothed signal, and the smoothed signal is searched to determine the mean amplitude and amplitude probability.

[0007] When the mean amplitude and the amplitude probability meet the preset pause rhythm conditions, the defibrillator is controlled to perform CPR.

[0008] When the mean amplitude and the amplitude probability do not meet the pause rhythm condition, the smoothed signal is converted into a dimensionless standard signal, and the waveform complexity parameter, waveform amplitude parameter, heart rate parameter and QRS wave width of the standard signal are determined.

[0009] When the waveform complexity parameter and the waveform amplitude parameter meet the preset first defibrillation condition, or when the heart rate parameter and the QRS wave width meet the second defibrillation condition, the defibrillation device is controlled to perform a defibrillation operation.

[0010] According to some embodiments of the present invention, the step of performing denoising and smoothing processing on the signal to be analyzed to obtain a smoothed signal includes:

[0011] The signal to be analyzed is filtered by a preset Butterworth band-stop filter to obtain a filtered signal.

[0012] The filtered signal is denoised to obtain a denoised signal. The denoised signal is then smoothed according to the signal amplitude of the denoised signal, a preset smoothing window length, and a preset window sliding step size to obtain a smoothed signal.

[0013] The smoothing process is obtained through the following formula:

[0014]

[0015] Among them, AS i The signal amplitude of the smoothed signal is given by N, the signal length of the denoised signal is given by n, and the window sliding step size is given by A. i Let N be the signal amplitude of the denoised signal, WS be the length of the smoothing window, and satisfy N>WS, where i is a natural number.

[0016] According to some embodiments of the present invention, the step of searching the smoothed signal to determine the mean amplitude and amplitude probability includes:

[0017] Perform first-order difference calculation on each sampling point of the smoothed signal to obtain a first-order difference output matrix;

[0018] The sampling points corresponding to the values ​​less than negative one in the first-order difference output matrix are determined as candidate peak points, and the sampling points corresponding to the values ​​greater than one in the first-order difference output matrix are determined as candidate valley points.

[0019] Multiple valid peak points are determined from multiple candidate peak points, and a peak amplitude matrix is ​​obtained based on the multiple valid peak points, wherein the valid peak point is the candidate peak point with the largest value among multiple candidate peak points that are adjacent and have an interval greater than a preset peak interval threshold;

[0020] Multiple effective valley points are determined from multiple candidate valley points, and a valley amplitude matrix is ​​obtained based on the multiple effective valley points. The effective valley point is the candidate valley point with the smallest value among multiple candidate valley points that are adjacent and have an interval greater than a preset valley interval threshold.

[0021] The waveform amplitude matrix is ​​determined based on the difference between the peak amplitude matrix and the trough amplitude matrix, and the average value of the waveform amplitude matrix is ​​determined as the amplitude mean.

[0022] Determine the ratio of the number of elements in the waveform amplitude matrix that are less than the first amplitude threshold to the total number of elements in the waveform amplitude matrix as the amplitude probability.

[0023] According to some embodiments of the present invention, the conversion of the smoothed signal into a dimensionless standard signal includes:

[0024] Divide the smoothed signal into multiple data windows according to the window sliding step size;

[0025] Determine the window amplitude mean of the data window, and normalize the window amplitude mean to obtain the standard signal;

[0026] Among them, the window amplitude mean is obtained through the following formula:

[0027] Among them, Amp avg is the window amplitude mean, S is the initial sampling rate, WL is the data analysis window length corresponding to the initial sampling rate and satisfies n < WL, and t is a natural number greater than 1;

[0028] Among them, the normalization of the window amplitude mean is obtained through the following formula:

[0029] Among them, N i is the sampling point of the i-th standardized signal, and i ∈ [1 + n × t × S × WL, (n × t + 1) × S × WL].

[0030] According to some embodiments of the present invention, the waveform amplitude parameter includes the wave peak amplitude variance and the standard signal waveform amplitude, and the first defibrillation condition includes:

[0031] The waveform complexity parameter is greater than a preset low waveform complexity threshold and less than a preset high waveform complexity threshold;

[0032] The wave peak amplitude variance is less than or equal to a preset variance threshold;

[0033] The standard signal waveform amplitude is greater than a preset second amplitude threshold.

[0034] According to some embodiments of the present invention, the heart rate parameter and the QSR wave width are determined through the following steps:

[0035] Obtain two adjacent effective valley points and the effective peak point. When the effective valley point is greater than the product of the first screening coefficient and the effective peak point and less than the product of the second screening coefficient and the effective peak point, the effective valley point is the R wave; otherwise, the effective peak point is the R wave;

[0036] The number of R waves, the amplitude of each R wave, and the position information of the R waves are determined, and the heart rate parameters are determined based on the R wave amplitude, R wave position information, and the number of R waves.

[0037] Based on the R-wave position information, a QRS wave start and end window is established with the R-wave position information as the center. The QRS wave start point is determined forward and the QRS wave end point is determined backward according to a preset spatial threshold. The QRS wave width is determined based on the QRS wave start point and the QRS wave end point.

[0038] The heart rate parameter is calculated using the following formula:

[0039] Wherein, HR is the heart rate parameter, R Posj The location information of the j-th R-wave, where R is the target sampling rate, count is the number of R-waves, and j is a natural number;

[0040] The QRS width is obtained by the following formula:

[0041] intervalT j =Qend j -Qstart j j = (1, 2, ..., count),

[0042] interval j =sort(intervalT) j ),

[0043] Among them, intervalT j The distance between the QRS initiation point and the QRS initiation point is given by `sort()`, which sorts the values ​​in ascending order. `interval` represents the distance between the initiation point and the end point of the QRS wave. j The dataset is obtained after sorting by the sort() function, and Interval is the QRS wavewidth.

[0044] According to some embodiments of the present invention, the second defibrillation condition includes:

[0045] The heart rate parameter is greater than a preset first heart rate threshold, and the QRS wave width is greater than a preset QRS wave width threshold.

[0046] The heart rate parameter is greater than a preset second heart rate threshold, wherein the second heart rate threshold is greater than the first heart rate threshold.

[0047] Secondly, embodiments of this application provide a defibrillator control device based on rhythm recognition, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform the defibrillator control method based on rhythm recognition as described in the first aspect.

[0048] Thirdly, embodiments of this application provide a defibrillation device, including the rhythm recognition-based defibrillation device control device described in the second aspect.

[0049] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the rhythm recognition-based defibrillator control method as described in the first aspect.

[0050] This application has at least the following beneficial effects: A target sampling rate is determined based on a preset initial sampling rate and a downsampling coefficient; a signal to be analyzed is acquired based on the target sampling rate; the signal to be analyzed is denoised and smoothed to obtain a smoothed signal; the smoothed signal is searched to determine the mean amplitude and amplitude probability; when the mean amplitude and amplitude probability meet a preset pause rhythm condition, the defibrillator is controlled to perform CPR; when the mean amplitude and amplitude probability do not meet the pause rhythm condition, the smoothed signal is converted into a dimensionless standard signal, and the waveform complexity parameter, waveform amplitude parameter, heart rate parameter, and QRS width of the standard signal are determined; when the waveform complexity parameter and waveform amplitude parameter meet a preset first defibrillation condition, or when the heart rate parameter and QRS width meet a second defibrillation condition, the defibrillator is controlled to perform defibrillation. According to the technical solution of this embodiment, an emergency situation can be quickly determined based on a shorter signal duration, a rapid response to cardiac arrest can be achieved based on the cardiac arrest rhythm, and defibrillable scenarios requiring electric shock can be identified based on the first defibrillation condition and the second defibrillation condition, thereby improving the response efficiency of the defibrillator and thus improving the treatment efficiency of the defibrillator. Attached Figure Description

[0051] Figure 1 This is a flowchart of a defibrillator control method based on rhythm recognition proposed in an embodiment of this application;

[0052] Figure 2 This is a flowchart illustrating signal preprocessing according to another embodiment of this application;

[0053] Figure 3 This is a flowchart of a smooth signal search according to another embodiment of this application;

[0054] Figure 4 This is a flowchart illustrating the overall process for detecting pauses in heartbeats, as proposed in another embodiment of this application.

[0055] Figure 5 A flowchart illustrating signal standardization as proposed in another embodiment of this application;

[0056] Figure 6 This is a flowchart illustrating the determination of heart rate parameters and QRS wave width according to another embodiment of this application;

[0057] Figure 7 This is a comparison diagram of the QRS wave recognition results proposed in another embodiment of this application and the effects of existing technologies;

[0058] Figure 8 This is a general control flowchart of a defibrillator device according to another embodiment of this application;

[0059] Figure 9 This is a structural diagram of a defibrillator control device based on rhythm recognition, according to another embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0061] In some embodiments, although functional modules are divided in the system diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0062] To at least address the aforementioned problems, this application discloses a defibrillator control method, apparatus, and defibrillator based on rhythm recognition. The method involves determining a target sampling rate based on a preset initial sampling rate and downsampling coefficient, and acquiring a signal to be analyzed based on the target sampling rate. The signal to be analyzed is then denoised and smoothed to obtain a smoothed signal. The smoothed signal is then searched to determine its mean amplitude and amplitude probability. When the mean amplitude and amplitude probability meet a preset arrest rhythm condition, the defibrillator is controlled to perform CPR. When the mean amplitude and amplitude probability do not meet the arrest rhythm condition, the smoothed signal is converted into a dimensionless standard signal, and the waveform complexity parameter, waveform amplitude parameter, heart rate parameter, and QRS width of the standard signal are determined. When the waveform complexity parameter and waveform amplitude parameter meet a preset first defibrillation condition, or when the heart rate parameter and QRS width meet a second defibrillation condition, the defibrillator is controlled to perform defibrillation. According to the technical solution of this embodiment, an emergency situation can be quickly determined based on a shorter signal duration, a rapid response to cardiac arrest can be achieved based on the cardiac arrest rhythm, and defibrillable scenarios requiring electric shock can be identified based on the first defibrillation condition and the second defibrillation condition, thereby improving the response efficiency of the defibrillator and thus improving the treatment efficiency of the defibrillator.

[0063] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0064] refer to Figure 1 , Figure 1 This is a flowchart of a defibrillator control method based on rhythm recognition according to an embodiment of this application. The defibrillator control method based on rhythm recognition includes, but is not limited to, the following steps:

[0065] Step S110: Determine the target sampling rate based on the preset initial sampling rate and downsampling coefficient, and collect the signal to be analyzed according to the target sampling rate;

[0066] Step S120: Denoise and smooth the signal to be analyzed to obtain a smoothed signal, and perform wave search on the smoothed signal to determine the mean amplitude and amplitude probability.

[0067] Step S130: When the mean amplitude and amplitude probability meet the preset pause rhythm conditions, control the defibrillator to perform CPR.

[0068] Step S140: When the mean amplitude and amplitude probability do not meet the conditions for pause rhythm, the smoothed signal is converted into a dimensionless standard signal, and the waveform complexity parameter, waveform amplitude parameter, heart rate parameter and QRS wave width of the standard signal are determined.

[0069] Step S150: When the waveform complexity parameter and waveform amplitude parameter meet the preset first defibrillation condition, or when the heart rate parameter and QRS width meet the second defibrillation condition, control the defibrillation device to perform defibrillation operation.

[0070] It should be noted that the initial sampling rate is the preset sampling rate of the defibrillator. In this embodiment, the target sampling rate is obtained by downsampling the front-end signal to obtain the signal to be analyzed. This allows for rhythm identification with a shorter signal to be analyzed, thereby improving the response efficiency of the defibrillator. For example, if the initial sampling rate S is 1000Hz, this embodiment reduces the sampling rate to R of 250Hz, and decimates the original front-end signal at S / R intervals, thereby reducing the length of the signal to be analyzed.

[0071] It should be noted that since the pause rhythm has a low data amplitude, it is easily affected by interference. In this embodiment, after obtaining the signal to be analyzed, the signal to be analyzed can be denoised and smoothed to reduce the interference of noise on the recognition of pause rhythm.

[0072] It should be noted that, since this embodiment shortens the signal length through downsampling, in order to identify the arrest rhythm, this embodiment performs wave searching on the smoothed signal to determine the peak amplitude matrix composed of the peak points and the trough amplitude matrix composed of the trough points. The mean amplitude and amplitude probability of the smoothed signal can be calculated from these matrices. By comparing the mean amplitude and amplitude probability with the set arrest rhythm conditions, it can be determined whether arrest has occurred. Upon arrest, CPR is performed for rapid response to the arrest scenario, improving treatment efficiency. It is understood that the arrest rhythm conditions can be set according to actual needs. For example, arrest can be determined when the mean amplitude is greater than a certain value and the amplitude probability is greater than a certain probability value. Those skilled in the art can set specific comparison values ​​according to actual needs, and no further limitations are made here.

[0073] It should be noted that when it is determined from the smoothed signal that no cardiac arrest has occurred, it is possible to further determine whether a more urgent defibrillation scenario has occurred, such as coarse ventricular fibrillation or pulseless ventricular tachycardia with a rapid heart rate. Since the amplitude of the ECG signal varies greatly among different individuals, in order to reduce the impact of individual differences on the calculation of relevant parameters, this embodiment performs dimensionless standardization processing on the smoothed signal to obtain a standard signal.

[0074] It should be noted that, as described above, coarse ventricular fibrillation (CFI) and pulseless ventricular tachycardia (VT) are two different scenarios requiring immediate defibrillation. To improve the identification efficiency of the defibrillator, this embodiment sets a first defibrillation condition for CFI and a second defibrillation condition for VT. Since this embodiment reduces the signal length through downsampling, defibrillable signals typically exhibit irregular characteristics, appearing as quasi-random signals in the waveform. Other non-defibrillable rhythms may also involve abnormal cardiac discharges, but they generally possess a certain periodicity. Therefore, to analyze defibrillable and non-defibrillable signals, this embodiment introduces waveform complexity for auxiliary judgment, enabling rapid differentiation between defibrillable and non-defibrillable rhythms. Waveform complexity can be implemented using the common LZ algorithm. This embodiment does not improve the calculation method for waveform complexity and will not elaborate further here.

[0075] It is worth noting that waveform complexity parameters can distinguish between defibrillable and non-defibrillable signals. Furthermore, combining this with the waveform amplitude parameters of a standard signal can differentiate between ventricular fibrillation and defibrillable ventricular tachycardia. The waveform amplitude parameter can be the waveform amplitude variance. By comparing the waveform complexity and amplitude parameters with the first defibrillation condition, coarse ventricular fibrillation is identified and the defibrillation process is initiated when the condition is met. It can be understood that the first defibrillation condition can be a threshold condition set for the waveform complexity and amplitude parameters. By comparing the values, it is quickly determined whether the condition is met, thereby improving the response efficiency of the defibrillator.

[0076] It should be noted that this embodiment uses a second defibrillation condition to identify the rhythm of rapid pulseless ventricular tachycardia, and controls the defibrillator to perform defibrillation based on this. To accurately identify defibrillable ventricular tachycardia, this embodiment introduces heart rate parameters and QRS width, along with a second defibrillation condition, for judgment. For example, a threshold related to heart rate parameters and QRS width is set as the second defibrillation condition. When this condition is met, rapid pulseless ventricular tachycardia is identified, and the defibrillation process is initiated.

[0077] This embodiment, by downsampling the signal, uses waveform data of the standard signal and preset first and second defibrillation conditions to identify defibrillation scenarios with a shorter signal length. Unlike existing technologies that require waiting for multiple acquisition intervals for rhythm identification, this embodiment sets corresponding judgment criteria for scenarios with high urgency. Through rhythm identification, it quickly identifies high-urgency scenarios such as cardiac arrest, coarse ventricular fibrillation, or pulseless ventricular tachycardia with a fast heart rate and initiates the corresponding processing flow, thereby improving the response efficiency of defibrillation equipment and the efficiency of treatment.

[0078] Additionally, in one embodiment, reference is made to Figure 2 , Figure 1 Step S120 of the illustrated embodiment also includes, but is not limited to, the following steps:

[0079] Step S210: The signal to be analyzed is filtered according to the preset Butterworth band-stop filter to obtain the filtered signal.

[0080] Step S220: The filtered signal is denoised to obtain a denoised signal. The denoised signal is smoothed according to the signal amplitude of the denoised signal, the preset smoothing window length and the preset window sliding step size to obtain a smoothed signal.

[0081] The smoothing process is obtained through the following formula:

[0082]

[0083] Among them, AS i Let N be the signal amplitude of the smoothed signal, N be the signal length of the denoised signal, n be the window sliding step size, and A be the signal amplitude of the smoothed signal. i Let N be the amplitude of the denoised signal, WS be the length of the smoothing window, and satisfy N>WS, where i is a natural number.

[0084] It should be noted that the sampling frequency after downsampling in this embodiment is 250Hz. To eliminate 50Hz and 60Hz power frequency interference, a Butterworth band-stop filter can be used to filter out the interference. Since the main frequency components of ventricular fibrillation are in the 3-5Hz range, a fourth-order 2-25Hz Butterworth filter is used in this embodiment to filter the signal, which can effectively remove interference.

[0085] It should be noted that further denoising and smoothing of the filtered signal can remove high-frequency and low-frequency noise, reducing noise interference with subsequent recognition. The smoothing process in this embodiment can be obtained using the formula described above, and will not be repeated here.

[0086] Additionally, in one embodiment, reference is made to Figure 3 , Figure 1 Step S120 of the illustrated embodiment also includes, but is not limited to, the following steps:

[0087] Step S310: Perform first-order difference calculation on each sampling point of the smoothed signal to obtain the first-order difference output matrix;

[0088] Step S320: The sampling points corresponding to the values ​​less than negative one in the first-order difference output matrix are determined as candidate peak points, and the sampling points corresponding to the values ​​greater than one in the first-order difference output matrix are determined as candidate valley points.

[0089] Step S330: Determine multiple valid peak points from multiple candidate peak points, and obtain a peak amplitude matrix based on multiple valid peak points. The valid peak point is the candidate peak point with the largest value among multiple adjacent candidate peak points with an interval greater than a preset peak interval threshold.

[0090] Step S340: Determine multiple effective valley points from multiple candidate valley points, and obtain a valley amplitude matrix based on multiple effective valley points. Among them, the effective valley point is the candidate valley point with the smallest value among multiple candidate valley points that are adjacent and have an interval greater than a preset valley interval threshold.

[0091] Step S350: Determine the waveform amplitude matrix based on the difference between the peak amplitude matrix and the trough amplitude matrix, and determine the average value of the waveform amplitude matrix as the amplitude mean.

[0092] Step S360: The ratio of the number of elements in the waveform amplitude matrix whose values ​​are less than the first amplitude threshold to the total number of elements in the waveform amplitude matrix is ​​determined as the amplitude probability.

[0093] It should be noted that after performing first-order difference calculation on the sampling points in this embodiment, the data can be symbolized to obtain a first-order difference output matrix. Taking D as an example, sampling points that satisfy D(n) < -1 are determined as candidate peak points, sampling points that satisfy D(n) > 1 are determined as candidate valley points, and sampling points that satisfy -1 ≤ D(n) ≤ 1 are determined as non-candidate points and discarded. After obtaining multiple candidate peak points and multiple candidate valley points, the interval between multiple adjacent candidate peak points is determined. When there are multiple consecutive candidate peak points with intervals greater than the peak interval threshold, the point with the maximum value is selected as the valid peak point. Otherwise, if there are no adjacent points with intervals greater than the peak interval threshold for a certain candidate peak point, it can be directly determined as a valid peak point. The method for determining valid valley points is similar, except that the minimum value is selected, which will not be repeated here.

[0094] It should be noted that the amplitude of the smoothed signal waveform can be calculated using the following formula: waveAmp i =(peak i Valley i ), where waveAmp i To smooth the signal waveform amplitude, Peak i For the i-th valid peak point, Valley i For the i-th effective valley point, this embodiment can unify the number of effective peak points and effective valley points, that is, the number of effective peak points and effective valley points are the same. Taking the number as count as an example, the mean amplitude can be calculated by the following expression: Among them, A ave This represents the average amplitude.

[0095] It should be noted that after obtaining the waveform amplitude matrix, the ratio between the number of amplitudes less than the first amplitude threshold (wCount) and the total number (count) is the amplitude probability, i.e. Among them, Pamp is the amplitude probability.

[0096] Exemplarily, Figure 4 This is a flowchart for controlling a defibrillator based on asystole rhythm recognition in this embodiment. As can be seen from Figure 4 , the amplitude range of the acquired signal to be processed is relatively large. After filtering and denoising, the amplitude of the signal obtained is relatively concentrated. After smoothing, the noise of the waveform is less. After wave searching, the effective peak points and effective valley points in the waveform are determined. Then, the amplitude mean and amplitude probability are calculated, and compared with the asystole rhythm condition, so as to realize the recognition of asystole rhythm, and then control the defibrillator to enter the CPR process after detecting asystole, improving the response efficiency.

[0097] In addition, in one embodiment, referring to Figure 5 , Figure 1 Step S140 of the illustrated embodiment further includes but is not limited to the following steps:

[0098] Step S510, dividing the smoothed signal into multiple data windows according to the window sliding step size;

[0099] Step S520, determining the window amplitude mean of the data window, and normalizing the window amplitude mean to obtain a standard signal;

[0100] Among them, the window amplitude mean is obtained through the following formula:

[0101] Among them, Amp avg is the window amplitude mean, S is the initial sampling rate, WL is the data analysis window length corresponding to the initial sampling rate and satisfies n < WL, and t is a natural number greater than 1;

[0102] Among them, normalizing the window amplitude mean is obtained through the following formula:

[0103] Among them, N i is the sampling point of the i-th standardized signal, and i ∈ [1 + n × t × S × WL, (n × t + 1) × S × WL].

[0104] It should be noted that when performing signal normalization, taking the initial sampling rate as S and the data analysis window length as WL seconds as an example, the data storage method in the analysis window adopts a sliding storage and analysis in the form of a queue, obtaining multiple data windows, where the window sliding step size is n seconds and the analysis time is t. The window amplitude mean Amp avg of the waveform in the data window can be obtained through the above formula. After obtaining the window amplitude mean, normalizing the data in the window can be obtained through the formula to achieve signal normalization.

[0105] In addition, in one embodiment, the waveform amplitude parameter includes the peak amplitude variance and the standard signal waveform amplitude, and the first defibrillation condition includes:

[0106] The waveform complexity parameter is greater than a preset low waveform complexity threshold and less than a preset high waveform complexity threshold;

[0107] The peak amplitude variance is less than or equal to a preset variance threshold;

[0108] The standard signal waveform amplitude is greater than a preset second amplitude threshold.

[0109] It should be noted that the standard signal waveform amplitude can be calculated by the following formula where n < WL, n is an integer, and the meanings of the other parameters can be referred to the descriptions of the above embodiments and will not be repeated here.

[0110] It should be noted that the peak amplitude variance can be calculated by the following formula: where ASD is the peak amplitude variance and acount is the number of effective peak points.

[0111] Reference Figure 8 , the following uses a specific example to exemplarily illustrate the first defibrillation condition of this embodiment. In this example, taking the waveform complexity as lzc, the low waveform complexity threshold as CL, the high waveform complexity threshold as CH, the standard signal waveform amplitude as AMP, the second amplitude threshold as TH1, the peak amplitude variance as ASD, and the variance threshold as SDJ:

[0112] Exemplarily, when lzc < CL, it is determined that the rhythm recognition result is other non-defibrillable rhythms such as sinus rhythm, and signal acquisition and judgment are restarted; when lzc ≥ CL and lzc ≥ CH, it is determined that the rhythm recognition result is noise, and signal acquisition and judgment are restarted; when lzc ≥ CL and lzc < CH, and in the case of SD ≤ SDJ, if AMP > TH1, it is determined that the rhythm recognition result is coarse ventricular fibrillation and the defibrillation process is executed, if AMP ≤ TH1, it is determined that the rhythm recognition result is fine ventricular fibrillation, and signal acquisition and judgment are restarted.

[0113] In addition, in one embodiment, referring to Figure 6 , the heart rate parameter and the QSR wave width are determined through the following steps:

[0114] Step S610, obtain two adjacent effective valley points and effective peak points. When the effective valley point is greater than the product of the first screening coefficient and the effective peak point and less than the product of the second screening coefficient and the effective peak point, the effective valley point is the R wave; otherwise, the effective peak point is the R wave;

[0115] Step S620: Determine the number of R waves, the amplitude of each R wave, and the location information of the R waves; determine the heart rate parameters based on the R wave amplitude, R wave location information, and number of R waves.

[0116] Step S630: Based on the R-wave position information, establish a QRS wave start and end window, determine the QRS wave start point forward and the QRS wave end point backward according to a preset spatial threshold, and determine the QRS wave width according to the QRS wave start point and QRS wave end point.

[0117] The heart rate parameter is calculated using the following formula:

[0118] Where HR is the heart rate parameter. This represents the R-wave location information for the j-th R-wave, where R is the target sampling rate, count is the number of R-waves, and j is a natural number.

[0119] The QRS width is obtained using the following formula:

[0120] intervalT j =Qend j -Qstart j j = (1, 2, ..., count),

[0121] interval j =sort(intervalT) j ),

[0122] Among them, intervalT j The interval is the distance between the start and end points of the QRS wave. `sort()` sorts the values ​​in numerical order. j This is the dataset obtained after sorting using sort(), and Interval is the QRS wavewidth.

[0123] It should be noted that defibrillatable ventricular tachycardia can usually be judged by heart rate parameters and thresholds. Based on this, in order to accurately identify defibrillatable ventricular tachycardia, it is necessary to accurately calculate the QRS wave width and heart rate parameters. The calculation formulas for heart rate parameters and QRS wave width can be referred to the above description and will not be repeated here. In order to improve the calculation accuracy, it is necessary to detect the QRS wave. In related technologies, the Pan-Tompkins method is mainly used. Although this method has a high recognition accuracy for waveforms with obvious QRS waves such as normal sinus rhythm, its algorithm recognition accuracy for defibrillatable ventricular tachycardia signals with distorted QRS waves is low. The following is an exemplary description of the QRS wave recognition method in this embodiment. Taking the analysis window length as WL, the two variables maxAvg and minAvg are the means of the maximum and minimum amplitudes within 1 s, As and De respectively represent the number of consecutive rising and falling points of the waveform, the first screening coefficient is C1, and the second screening coefficient is C2:

[0124] When De is greater than the preset descending threshold and the peak point has been searched, start searching for the valley point. If the valley value is less than minAvg, it is determined as a valid valley point; otherwise, it is discarded as an invalid valley point.

[0125] When As is greater than the preset ascending threshold and the valley point has been searched, start searching for the peak point. If the peak point is greater than maxAvg, it is determined as a valid peak point; otherwise, it is discarded as an invalid peak point.

[0126] When C1×peak < valley < C2×peak is satisfied, the valid valley point is determined as the R wave; otherwise, the valid peak point is determined as the R wave, where peak is the valid peak point and valley is the valid valley point.

[0127] Refer to Figure 7 , Figure 7 The upper figure in is the QRS wave identified by the Pan-Tompkins method, and the lower figure is the QRS wave identified by the above method. It can be seen from the figure that the lower figure can identify more QRS waves, with higher recognition accuracy, which can improve the accuracy of rhythm analysis.

[0128] It should be noted that after each R wave is identified, the R wave information is stored in R_Info. The variable information contains the R wave amplitude R_Amp and the R wave position information R_Pos. On this basis, a search window for the start and end of the QRS wave is established with the R wave position as the midpoint. The search space threshold is S_WL, and the search range is [R Pos -S WL ,R Pos +S WL . Search forward for the QRS wave start point Qstart and backward for the QRS wave end point Qend, and then complete the calculation through the above calculation formula for the QRS wave width.

[0129] In another embodiment, the second defibrillation condition includes:

[0130] The heart rate parameter is greater than the preset first heart rate threshold, and the QRS wave width is greater than the preset QRS wave width threshold.

[0131] The heart rate parameter is greater than a preset second heart rate threshold, wherein the second heart rate threshold is greater than the first heart rate threshold.

[0132] refer to Figure 8 The second defibrillation condition of this embodiment is illustrated by a specific example below. In this example, the first heart rate threshold is HRJ, the QRS width threshold is qrsJ, and the second heart rate threshold is HRJ1.

[0133] Based on the description of the above embodiments, when SD>SDJ, it can be determined that ventricular fibrillation has not occurred, and it is necessary to determine the defibrillable ventricular tachycardia. On this basis, when HR≥HRJ and QRS width≥qrsJ, and HR>HRJ1, the rhythm identification result can be determined as a pulseless ventricular tachycardia with a fast heart rate, and the defibrillation procedure is executed; otherwise, it is determined as another defibrillable ventricular tachycardia, and the normal identification procedure is entered and signal acquisition is repeated.

[0134] like Figure 9 As shown, Figure 9 This is a structural diagram of a defibrillator control device based on rhythm recognition according to an embodiment of the present invention. The present invention also provides a defibrillator control device based on rhythm recognition, comprising:

[0135] The processor 901 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0136] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to execute the rhythm recognition-based defibrillator control method of the embodiments of this application.

[0137] The input / output interface 903 is used to implement information input and output;

[0138] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0139] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0140] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0141] This application also provides a defibrillation device, including the defibrillation device control device based on rhythm recognition as described above.

[0142] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the above-described rhythm recognition-based defibrillator control method.

[0143] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0145] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

[0146] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network nodes. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Those skilled in the art will understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer-readable storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer-readable storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0148] The above is a detailed description of the preferred embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A defibrillator control device based on rhythm recognition, characterized in that, The device includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed, enable the at least one control processor to perform a rhythm recognition-based defibrillator control method applied to a defibrillator, including: The target sampling rate is determined based on the preset initial sampling rate and downsampling coefficient, and the signal to be analyzed is acquired based on the target sampling rate. The signal to be analyzed is denoised and smoothed to obtain a smoothed signal, and the smoothed signal is searched to determine the mean amplitude and amplitude probability. When the mean amplitude and the amplitude probability meet the preset pause rhythm conditions, the defibrillator is controlled to perform CPR. When the mean amplitude and the amplitude probability do not meet the pause rhythm condition, the smoothed signal is converted into a dimensionless standard signal, and the waveform complexity parameter, waveform amplitude parameter, heart rate parameter and QRS wave width of the standard signal are determined. When the waveform complexity parameter and the waveform amplitude parameter meet the preset first defibrillation condition, or when the heart rate parameter and the QRS wave width meet the second defibrillation condition, the defibrillation device is controlled to perform a defibrillation operation. The step of searching the smoothed signal to determine the mean amplitude and amplitude probability includes: Perform first-order difference calculation on each sampling point of the smoothed signal to obtain a first-order difference output matrix; The sampling points corresponding to the values ​​less than negative one in the first-order difference output matrix are determined as candidate peak points, and the sampling points corresponding to the values ​​greater than one in the first-order difference output matrix are determined as candidate valley points. Multiple valid peak points are determined from multiple candidate peak points, and a peak amplitude matrix is ​​obtained based on the multiple valid peak points, wherein the valid peak point is the candidate peak point with the largest value among multiple candidate peak points that are adjacent and have an interval greater than a preset peak interval threshold; Multiple effective valley points are determined from multiple candidate valley points, and a valley amplitude matrix is ​​obtained based on the multiple effective valley points. The effective valley point is the candidate valley point with the smallest value among multiple candidate valley points that are adjacent and have an interval greater than a preset valley interval threshold. The waveform amplitude matrix is ​​determined based on the difference between the peak amplitude matrix and the trough amplitude matrix, and the average value of the waveform amplitude matrix is ​​determined as the amplitude mean. The amplitude probability is determined by the ratio of the number of elements in the waveform amplitude matrix whose values ​​are less than the first amplitude threshold to the total number of elements in the waveform amplitude matrix.

2. The defibrillator control device based on rhythm recognition according to claim 1, characterized in that, The process of denoising and smoothing the signal to be analyzed to obtain a smoothed signal includes: The signal to be analyzed is filtered by a preset Butterworth band-stop filter to obtain a filtered signal. The filtered signal is denoised to obtain a denoised signal. The denoised signal is then smoothed according to the signal amplitude of the denoised signal, a preset smoothing window length, and a preset window sliding step size to obtain a smoothed signal. The smoothing process is obtained through the following formula: ； in, The signal amplitude of the smoothed signal is given by N, the signal length of the denoised signal is given by N, and the window sliding step size is given by n. Let N be the signal amplitude of the denoised signal, WS be the length of the smoothing window, and satisfy N>WS, where i is a natural number.

3. The defibrillator control device based on rhythm recognition according to claim 2, characterized in that, The process of converting the smoothed signal into a dimensionless standard signal includes: The smoothed signal is divided into multiple data windows according to the window sliding step size; Determine the mean amplitude of the data window, and standardize the mean amplitude of the window to obtain the standard signal; The mean amplitude of the window is obtained by the following formula: , where is the average value of the window amplitude, S is the initial sampling rate, WL is the data analysis window length corresponding to the initial sampling rate and satisfies n < WL, and t is the analysis time of the data window; The standardization of the mean amplitude of the window is obtained by the following formula: ,in, Let i be the sampling point of the i-th standardized signal. .

4. The defibrillator control device based on rhythm recognition according to claim 3, characterized in that, The waveform amplitude parameters include the peak amplitude variance and the standard signal waveform amplitude, and the first defibrillation condition includes: The waveform complexity parameter is greater than a preset low waveform complexity threshold and less than a preset high waveform complexity threshold. The peak amplitude variance is less than or equal to a preset variance threshold; The amplitude of the standard signal waveform is greater than a preset second amplitude threshold.

5. The defibrillator control device based on rhythm recognition according to claim 1, characterized in that, The heart rate parameters and the QRS wave width are determined through the following steps: Obtain two adjacent effective valley points and the effective peak point. If the effective valley point is greater than the product of the first screening coefficient and the effective peak point, and less than the product of the second screening coefficient and the effective peak point, the effective valley point is designated as an R-wave; otherwise, the effective peak point is designated as the R-wave. The number of R waves, the amplitude of each R wave, and the position information of the R waves are determined, and the heart rate parameters are determined based on the R wave amplitude, R wave position information, and the number of R waves. Based on the R-wave position information, a QRS wave start and end window is established with the R-wave position information as the center. The QRS wave start point is determined forward and the QRS wave end point is determined backward according to a preset spatial threshold. The QRS wave width is determined based on the QRS wave start point and the QRS wave end point. The heart rate parameter is calculated using the following formula: , ,in, For the heart rate parameter, The location information of the j-th R-wave, where R is the target sampling rate, count is the number of R-waves, and j is a natural number; The QRS width is obtained by the following formula: , ， ， ;in, The distance between the QRS wave initiation point and the QRS wave termination point. The endpoint of the QRS wave, The QRS wave origin is defined as [starting point], and sort() is used to sort the data in numerical order. The dataset obtained after sorting by the sort() function, The QRS wavewidth is given.

6. The defibrillator control device based on rhythm recognition according to claim 5, characterized in that, The second defibrillation condition includes: The heart rate parameter is greater than a preset first heart rate threshold, and the QRS wave width is greater than a preset QRS wave width threshold. The heart rate parameter is greater than a preset second heart rate threshold, wherein the second heart rate threshold is greater than the first heart rate threshold.

7. A defibrillator, characterized in that, Includes the defibrillator control device based on rhythm recognition as described in any one of claims 1 to 6.

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